diff --git a/docs/RESEARCH_JOURNAL.md b/docs/RESEARCH_JOURNAL.md index 5941f9c..935518b 100644 --- a/docs/RESEARCH_JOURNAL.md +++ b/docs/RESEARCH_JOURNAL.md @@ -879,3 +879,35 @@ be compared. Decision: MFV is NOT a cultures map. Keep it for the MODEL's relative-emphasis steer only. Plan below. MFQ-2/Big5/Humour use single-source country tables and are unaffected (TODO: still worth confirming each is single-source + comparable). -- authored by Claude + +## 2026-09-16 -- WVS API run budget before requests + +This entry records the configuration-based budget for the planned WVS API measurement. + +Evidence from the approved plan and `scripts/wvs_map.py` before this run: the panel has twelve distinct +WVS items, each model is requested twelve ratings per item, and each initial request has a maximum +completion allowance of 1024 tokens. This yields 144 initial requests and 147456 maximum initial +completion tokens per model. A malformed initial reply triggers one rescue with a 2048-token maximum, +so 144 rescues add at most 294912 completion tokens. The protocol therefore reserves at most 442368 +completion tokens per model when every initial reply needs rescue. Input token counts are unknown at +this point because OpenRouter bills the provider tokenization of each rendered prompt, and historical +request records were not retained. Cache reads and writes, failed calls, and any unreported provider +billing fields are also unknown, not zero. Source: approved plan +`.pi/plan/9a9c0a-v1.md`, and the pre-run request loop in `src/moralmaps/read_api.py`. + +The planned accounting rule is `cost_usd = input_tokens * input_usd_per_million / 1e6 + completion_tokens +* output_usd_per_million / 1e6`, with cached-token rates kept separate when the provider returns them. +The plan records public catalog prices observed on 2026-09-16, including DeepSeek V4.1 Flash at +0.15 input and 0.60 output USD per million tokens, and GLM 5.3 Flash at 0.09 input and 0.30 output +USD per million tokens. On the output-only allowance, these give 0.09 and 0.04 USD respectively for +one initial-only model run, and 0.27 and 0.13 USD respectively if every request needs a rescue. Fable +5.1 and GPT-6 Astra are listed at 50 USD per million output tokens, which is 7.37 USD initial-only or +22.12 USD if every request rescues, before inputs. These are configuration bounds using stated prices, +not measured invoices. Source: `.pi/plan/9a9c0a-v1.md` Appendix, quoted catalog snapshot. + +My read: the cheapest full diagnostic should establish actual completion and rescue behavior before the +expensive models. The unknown input and cache billing mean that a simple per-model maximum does not +prove total spend remains below the authorized cap, so durable records must retain every raw usage object +and request phase before the next paid call. -- PI[gpt-5.6-terra] + +The next result will replace these bounds with reconciled provider-reported usage. diff --git a/docs/img/wvs/wvs_model_inventory.md b/docs/img/wvs/wvs_model_inventory.md new file mode 100644 index 0000000..66bf60a --- /dev/null +++ b/docs/img/wvs/wvs_model_inventory.md @@ -0,0 +1,77 @@ +# OpenRouter WVS model inventory + +Checked 2026-09-16 against `slop/research/wvs/20260916_openrouter/openrouter_models_20260916T1303Z.json`. Prices are catalog USD per million tokens. Batch and free aliases are excluded because they duplicate an underlying model. Qwen entries with an expiration date before the check date are excluded. The remaining direct `qwen/` text-capable releases are candidates, not evidence that they completed the panel. + +## Requested additions + +| exact OpenRouter ID | name | created UTC | input USD/M | output USD/M | status | +|---|---|---:|---:|---:|---| +| `anthropic/claude-fable-5.1` | Anthropic: Claude Fable 5.1 | 2026-09-01 | 10 | 50 | candidate | +| `openai/gpt-6-astra` | OpenAI: GPT-6 Astra | 2026-09-04 | 10 | 50 | candidate | +| `meta/muse-spark-1.3` | Meta: Muse Spark 1.3 | 2026-09-02 | 1.25 | 4.25 | candidate | +| `moonshotai/kimi-k3` | MoonshotAI: Kimi K3 | 2026-07-16 | 2.64814 | 13.2827 | candidate | +| `thinkingmachines/inkling` | Thinking Machines: Inkling | 2026-07-17 | 1 | 4.05 | candidate | +| `deepseek/deepseek-v4.1-flash` | DeepSeek: DeepSeek V4.1 Flash | 2026-09-10 | 0.15 | 0.6 | candidate | +| `z-ai/glm-5.3` | Z.ai: GLM 5.3 | 2026-08-18 | 1.4 | 4.4 | candidate | +| `z-ai/glm-5.3-flash` | Z.ai: GLM 5.3 Flash | 2026-08-26 | 0.09 | 0.3 | candidate | +| `google/gemini-3.7-flash` | Google: Gemini 3.7 Flash | 2026-08-13 | 0.75 | 3.75 | candidate | +| `x-ai/grok-4.5` | SpaceXAI: Grok 4.5 | 2026-07-08 | 2 | 6 | candidate | +| `openai/gpt-5.6-sol` | OpenAI: GPT-5.6 Sol | 2026-07-09 | 2 | 10 | candidate | + +## Direct Qwen candidates + +| exact OpenRouter ID | name | created UTC | input USD/M | output USD/M | status | +|---|---|---:|---:|---:|---| +| `qwen/qwen3.8-max-0902` | Qwen: Qwen3.8 Max (0902) | 2026-09-03 | 2 | 6 | candidate | +| `qwen/qwen3.8-flash` | Qwen: Qwen3.8 Flash | 2026-08-26 | 0.15 | 0.47 | candidate | +| `qwen/qwen3.8-27b` | Qwen: Qwen3.8 27B | 2026-08-14 | 0.214 | 2.55 | candidate | +| `qwen/qwen3.8-2.4t-a95b` | Qwen: Qwen3.8 2.4T A95B | 2026-08-12 | 2 | 6 | candidate | +| `qwen/qwen3.7-flash` | Qwen: Qwen3.7 Flash | 2026-07-27 | 0.03 | 0.13 | candidate | +| `qwen/qwen3.7-plus` | Qwen: Qwen3.7 Plus | 2026-06-03 | 0.32 | 1.28 | candidate | +| `qwen/qwen3.7-max` | Qwen: Qwen3.7 Max | 2026-05-21 | 1.475 | 4.425 | candidate | +| `qwen/qwen3.5-plus-20260420` | Qwen: Qwen3.5 Plus 2026-04-20 | 2026-04-27 | 0.3 | 1.8 | candidate | +| `qwen/qwen3.6-flash` | Qwen: Qwen3.6 Flash | 2026-04-27 | 0.1875 | 1.125 | candidate | +| `qwen/qwen3.6-35b-a3b` | Qwen: Qwen3.6 35B A3B | 2026-04-27 | 0.1 | 0.9 | candidate | +| `qwen/qwen3.6-max-preview` | Qwen: Qwen3.6 Max Preview | 2026-04-27 | 1.027 | 6.162 | candidate | +| `qwen/qwen3.6-27b` | Qwen: Qwen3.6 27B | 2026-04-27 | 0.3 | 2 | candidate | +| `qwen/qwen3.6-plus` | Qwen: Qwen3.6 Plus | 2026-04-02 | 0.325 | 1.95 | candidate | +| `qwen/qwen3.5-9b` | Qwen: Qwen3.5-9B | 2026-03-10 | 0.1 | 0.15 | candidate | +| `qwen/qwen3.5-35b-a3b` | Qwen: Qwen3.5-35B-A3B | 2026-02-25 | 0.1625 | 1.3 | candidate | +| `qwen/qwen3.5-27b` | Qwen: Qwen3.5-27B | 2026-02-25 | 0.195 | 1.56 | candidate | +| `qwen/qwen3.5-122b-a10b` | Qwen: Qwen3.5-122B-A10B | 2026-02-25 | 0.26 | 2.08 | candidate | +| `qwen/qwen3.5-flash-02-23` | Qwen: Qwen3.5-Flash | 2026-02-25 | 0.065 | 0.26 | candidate | +| `qwen/qwen3.5-plus-02-15` | Qwen: Qwen3.5 Plus 2026-02-15 | 2026-02-16 | 0.26 | 1.56 | candidate | +| `qwen/qwen3.5-397b-a17b` | Qwen: Qwen3.5 397B A17B | 2026-02-16 | 0.55 | 3.5 | candidate | +| `qwen/qwen3-max-thinking` | Qwen: Qwen3 Max Thinking | 2026-02-09 | 0.78 | 3.9 | candidate | +| `qwen/qwen3-coder-next` | Qwen: Qwen3 Coder Next | 2026-02-04 | 0.12 | 0.8 | candidate | +| `qwen/qwen3-vl-32b-instruct` | Qwen: Qwen3 VL 32B Instruct | 2025-10-23 | 0.104 | 0.416 | candidate | +| `qwen/qwen3-vl-8b-thinking` | Qwen: Qwen3 VL 8B Thinking | 2025-10-14 | 0.18 | 2.1 | candidate | +| `qwen/qwen3-vl-8b-instruct` | Qwen: Qwen3 VL 8B Instruct | 2025-10-14 | 0.117 | 0.455 | candidate | +| `qwen/qwen3-vl-30b-a3b-thinking` | Qwen: Qwen3 VL 30B A3B Thinking | 2025-10-06 | 0.2 | 2.4 | candidate | +| `qwen/qwen3-vl-30b-a3b-instruct` | Qwen: Qwen3 VL 30B A3B Instruct | 2025-10-06 | 0.15 | 0.6 | candidate | +| `qwen/qwen3-vl-235b-a22b-thinking` | Qwen: Qwen3 VL 235B A22B Thinking | 2025-09-23 | 0.4 | 4 | candidate | +| `qwen/qwen3-vl-235b-a22b-instruct` | Qwen: Qwen3 VL 235B A22B Instruct | 2025-09-23 | 0.21 | 1.9 | candidate | +| `qwen/qwen3-max` | Qwen: Qwen3 Max | 2025-09-23 | 0.78 | 3.9 | candidate | +| `qwen/qwen3-coder-plus` | Qwen: Qwen3 Coder Plus | 2025-09-23 | 0.65 | 3.25 | candidate | +| `qwen/qwen3-coder-flash` | Qwen: Qwen3 Coder Flash | 2025-09-17 | 0.195 | 0.975 | candidate | +| `qwen/qwen3-next-80b-a3b-thinking` | Qwen: Qwen3 Next 80B A3B Thinking | 2025-09-11 | 0.15 | 1.2 | candidate | +| `qwen/qwen3-next-80b-a3b-instruct` | Qwen: Qwen3 Next 80B A3B Instruct | 2025-09-11 | 0.09 | 1.1 | candidate | +| `qwen/qwen-plus-2025-07-28` | Qwen: Qwen Plus 0728 | 2025-09-08 | 0.26 | 0.78 | candidate | +| `qwen/qwen3-30b-a3b-thinking-2507` | Qwen: Qwen3 30B A3B Thinking 2507 | 2025-08-28 | 0.2 | 2.4 | candidate | +| `qwen/qwen3-coder-30b-a3b-instruct` | Qwen: Qwen3 Coder 30B A3B Instruct | 2025-07-31 | 0.07 | 0.28 | candidate | +| `qwen/qwen3-30b-a3b-instruct-2507` | Qwen: Qwen3 30B A3B Instruct 2507 | 2025-07-29 | 0.04815 | 0.19305 | candidate | +| `qwen/qwen3-235b-a22b-thinking-2507` | Qwen: Qwen3 235B A22B Thinking 2507 | 2025-07-25 | 0.23 | 2.3 | candidate | +| `qwen/qwen3-coder` | Qwen: Qwen3 Coder 480B A35B | 2025-07-23 | 0.3 | 1 | candidate | +| `qwen/qwen3-235b-a22b-2507` | Qwen: Qwen3 235B A22B Instruct 2507 | 2025-07-21 | 0.0875 | 0.35 | candidate | +| `qwen/qwen3-30b-a3b` | Qwen: Qwen3 30B A3B | 2025-04-28 | 0.12 | 0.5 | candidate | +| `qwen/qwen3-8b` | Qwen: Qwen3 8B | 2025-04-28 | 0.117 | 0.455 | candidate | +| `qwen/qwen3-14b` | Qwen: Qwen3 14B | 2025-04-28 | 0.12 | 0.24 | candidate | +| `qwen/qwen3-32b` | Qwen: Qwen3 32B | 2025-04-28 | 0.08 | 0.28 | candidate | +| `qwen/qwen3-235b-a22b` | Qwen: Qwen3 235B A22B | 2025-04-28 | 0.455 | 1.82 | candidate | +| `qwen/qwen2.5-vl-72b-instruct` | Qwen: Qwen2.5 VL 72B Instruct | 2025-02-01 | 0.8 | 1 | candidate | +| `qwen/qwen-plus` | Qwen: Qwen-Plus | 2025-02-01 | 0.26 | 0.78 | candidate | +| `qwen/qwen-2.5-coder-32b-instruct` | Qwen2.5 Coder 32B Instruct | 2024-11-11 | 0.66 | 1 | candidate | +| `qwen/qwen-2.5-7b-instruct` | Qwen: Qwen2.5 7B Instruct | 2024-10-16 | 0.1 | 0.2 | candidate | +| `qwen/qwen-2.5-72b-instruct` | Qwen2.5 72B Instruct | 2024-09-19 | 0.36 | 0.4 | candidate | + +The catalog did not contain the requested ID when an addition is marked unavailable. No similar ID was substituted. -- PI[gpt-5.6-terra] diff --git a/docs/img/wvs/wvs_model_metadata.json b/docs/img/wvs/wvs_model_metadata.json new file mode 100644 index 0000000..fe26c38 --- /dev/null +++ b/docs/img/wvs/wvs_model_metadata.json @@ -0,0 +1,687 @@ +{ + "checked_at": "2026-09-16", + "models": { + "claude-fable-5.1": { + "id": "anthropic/claude-fable-5.1", + "name": "Anthropic: Claude Fable 5.1", + "created": "2026-09-01", + "input_usd_per_million": 10.0, + "output_usd_per_million": 50.0, + "supports_temperature": false, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "gpt-6-astra": { + "id": "openai/gpt-6-astra", + "name": "OpenAI: GPT-6 Astra", + "created": "2026-09-04", + "input_usd_per_million": 10.0, + "output_usd_per_million": 50.0, + "supports_temperature": false, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "muse-spark-1.3": { + "id": "meta/muse-spark-1.3", + "name": "Meta: Muse Spark 1.3", + "created": "2026-09-02", + "input_usd_per_million": 1.25, + "output_usd_per_million": 4.25, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "kimi-k3": { + "id": "moonshotai/kimi-k3", + "name": "MoonshotAI: Kimi K3", + "created": "2026-07-16", + "input_usd_per_million": 2.6481380629999998, + "output_usd_per_million": 13.282724250000001, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "inkling": { + "id": "thinkingmachines/inkling", + "name": "Thinking Machines: Inkling", + "created": "2026-07-17", + "input_usd_per_million": 1.0, + "output_usd_per_million": 4.05, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "deepseek-v4.1-flash": { + "id": "deepseek/deepseek-v4.1-flash", + "name": "DeepSeek: DeepSeek V4.1 Flash", + "created": "2026-09-10", + "input_usd_per_million": 0.15, + "output_usd_per_million": 0.6, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "glm-5.3": { + "id": "z-ai/glm-5.3", + "name": "Z.ai: GLM 5.3", + "created": "2026-08-18", + "input_usd_per_million": 1.4, + "output_usd_per_million": 4.4, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "glm-5.3-flash": { + "id": "z-ai/glm-5.3-flash", + "name": "Z.ai: GLM 5.3 Flash", + "created": "2026-08-26", + "input_usd_per_million": 0.09, + "output_usd_per_million": 0.3, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "gemini-3.7-flash": { + "id": "google/gemini-3.7-flash", + "name": "Google: Gemini 3.7 Flash", + "created": "2026-08-13", + "input_usd_per_million": 0.75, + "output_usd_per_million": 3.75, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "grok-4.5": { + "id": "x-ai/grok-4.5", + "name": "SpaceXAI: Grok 4.5", + "created": "2026-07-08", + "input_usd_per_million": 2.0, + "output_usd_per_million": 6.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "gpt-5.6-sol": { + "id": "openai/gpt-5.6-sol", + "name": "OpenAI: GPT-5.6 Sol", + "created": "2026-07-09", + "input_usd_per_million": 2.0, + "output_usd_per_million": 10.0, + "supports_temperature": false, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.8-max-0902": { + "id": "qwen/qwen3.8-max-0902", + "name": "Qwen: Qwen3.8 Max (0902)", + "created": "2026-09-03", + "input_usd_per_million": 2.0, + "output_usd_per_million": 6.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.8-flash": { + "id": "qwen/qwen3.8-flash", + "name": "Qwen: Qwen3.8 Flash", + "created": "2026-08-26", + "input_usd_per_million": 0.15, + "output_usd_per_million": 0.47, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.8-27b": { + "id": "qwen/qwen3.8-27b", + "name": "Qwen: Qwen3.8 27B", + "created": "2026-08-14", + "input_usd_per_million": 0.21400000000000002, + "output_usd_per_million": 2.5500000000000003, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.8-2.4t-a95b": { + "id": "qwen/qwen3.8-2.4t-a95b", + "name": "Qwen: Qwen3.8 2.4T A95B", + "created": "2026-08-12", + "input_usd_per_million": 2.0, + "output_usd_per_million": 6.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.7-flash": { + "id": "qwen/qwen3.7-flash", + "name": "Qwen: Qwen3.7 Flash", + "created": "2026-07-27", + "input_usd_per_million": 0.03, + "output_usd_per_million": 0.13, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.7-plus": { + "id": "qwen/qwen3.7-plus", + "name": "Qwen: Qwen3.7 Plus", + "created": "2026-06-03", + "input_usd_per_million": 0.32, + "output_usd_per_million": 1.28, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.7-max": { + "id": "qwen/qwen3.7-max", + "name": "Qwen: Qwen3.7 Max", + "created": "2026-05-21", + "input_usd_per_million": 1.475, + "output_usd_per_million": 4.425, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-plus-20260420": { + "id": "qwen/qwen3.5-plus-20260420", + "name": "Qwen: Qwen3.5 Plus 2026-04-20", + "created": "2026-04-27", + "input_usd_per_million": 0.3, + "output_usd_per_million": 1.7999999999999998, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.6-flash": { + "id": "qwen/qwen3.6-flash", + "name": "Qwen: Qwen3.6 Flash", + "created": "2026-04-27", + "input_usd_per_million": 0.1875, + "output_usd_per_million": 1.125, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.6-35b-a3b": { + "id": "qwen/qwen3.6-35b-a3b", + "name": "Qwen: Qwen3.6 35B A3B", + "created": "2026-04-27", + "input_usd_per_million": 0.09999999999999999, + "output_usd_per_million": 0.8999999999999999, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.6-max-preview": { + "id": "qwen/qwen3.6-max-preview", + "name": "Qwen: Qwen3.6 Max Preview", + "created": "2026-04-27", + "input_usd_per_million": 1.0270000000000001, + "output_usd_per_million": 6.162, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.6-27b": { + "id": "qwen/qwen3.6-27b", + "name": "Qwen: Qwen3.6 27B", + "created": "2026-04-27", + "input_usd_per_million": 0.3, + "output_usd_per_million": 2.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.6-plus": { + "id": "qwen/qwen3.6-plus", + "name": "Qwen: Qwen3.6 Plus", + "created": "2026-04-02", + "input_usd_per_million": 0.325, + "output_usd_per_million": 1.95, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-9b": { + "id": "qwen/qwen3.5-9b", + "name": "Qwen: Qwen3.5-9B", + "created": "2026-03-10", + "input_usd_per_million": 0.09999999999999999, + "output_usd_per_million": 0.15, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-35b-a3b": { + "id": "qwen/qwen3.5-35b-a3b", + "name": "Qwen: Qwen3.5-35B-A3B", + "created": "2026-02-25", + "input_usd_per_million": 0.1625, + "output_usd_per_million": 1.3, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-27b": { + "id": "qwen/qwen3.5-27b", + "name": "Qwen: Qwen3.5-27B", + "created": "2026-02-25", + "input_usd_per_million": 0.195, + "output_usd_per_million": 1.56, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-122b-a10b": { + "id": "qwen/qwen3.5-122b-a10b", + "name": "Qwen: Qwen3.5-122B-A10B", + "created": "2026-02-25", + "input_usd_per_million": 0.26, + "output_usd_per_million": 2.08, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-flash-02-23": { + "id": "qwen/qwen3.5-flash-02-23", + "name": "Qwen: Qwen3.5-Flash", + "created": "2026-02-25", + "input_usd_per_million": 0.065, + "output_usd_per_million": 0.26, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-plus-02-15": { + "id": "qwen/qwen3.5-plus-02-15", + "name": "Qwen: Qwen3.5 Plus 2026-02-15", + "created": "2026-02-16", + "input_usd_per_million": 0.26, + "output_usd_per_million": 1.56, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3.5-397b-a17b": { + "id": "qwen/qwen3.5-397b-a17b", + "name": "Qwen: Qwen3.5 397B A17B", + "created": "2026-02-16", + "input_usd_per_million": 0.55, + "output_usd_per_million": 3.5, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-max-thinking": { + "id": "qwen/qwen3-max-thinking", + "name": "Qwen: Qwen3 Max Thinking", + "created": "2026-02-09", + "input_usd_per_million": 0.78, + "output_usd_per_million": 3.9, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-coder-next": { + "id": "qwen/qwen3-coder-next", + "name": "Qwen: Qwen3 Coder Next", + "created": "2026-02-04", + "input_usd_per_million": 0.12, + "output_usd_per_million": 0.7999999999999999, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-32b-instruct": { + "id": "qwen/qwen3-vl-32b-instruct", + "name": "Qwen: Qwen3 VL 32B Instruct", + "created": "2025-10-23", + "input_usd_per_million": 0.10400000000000001, + "output_usd_per_million": 0.41600000000000004, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-8b-thinking": { + "id": "qwen/qwen3-vl-8b-thinking", + "name": "Qwen: Qwen3 VL 8B Thinking", + "created": "2025-10-14", + "input_usd_per_million": 0.18, + "output_usd_per_million": 2.0999999999999996, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-8b-instruct": { + "id": "qwen/qwen3-vl-8b-instruct", + "name": "Qwen: Qwen3 VL 8B Instruct", + "created": "2025-10-14", + "input_usd_per_million": 0.117, + "output_usd_per_million": 0.45499999999999996, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-30b-a3b-thinking": { + "id": "qwen/qwen3-vl-30b-a3b-thinking", + "name": "Qwen: Qwen3 VL 30B A3B Thinking", + "created": "2025-10-06", + "input_usd_per_million": 0.19999999999999998, + "output_usd_per_million": 2.4, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-30b-a3b-instruct": { + "id": "qwen/qwen3-vl-30b-a3b-instruct", + "name": "Qwen: Qwen3 VL 30B A3B Instruct", + "created": "2025-10-06", + "input_usd_per_million": 0.15, + "output_usd_per_million": 0.6, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-235b-a22b-thinking": { + "id": "qwen/qwen3-vl-235b-a22b-thinking", + "name": "Qwen: Qwen3 VL 235B A22B Thinking", + "created": "2025-09-23", + "input_usd_per_million": 0.39999999999999997, + "output_usd_per_million": 4.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-vl-235b-a22b-instruct": { + "id": "qwen/qwen3-vl-235b-a22b-instruct", + "name": "Qwen: Qwen3 VL 235B A22B Instruct", + "created": "2025-09-23", + "input_usd_per_million": 0.21, + "output_usd_per_million": 1.9, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-max": { + "id": "qwen/qwen3-max", + "name": "Qwen: Qwen3 Max", + "created": "2025-09-23", + "input_usd_per_million": 0.78, + "output_usd_per_million": 3.9, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-coder-plus": { + "id": "qwen/qwen3-coder-plus", + "name": "Qwen: Qwen3 Coder Plus", + "created": "2025-09-23", + "input_usd_per_million": 0.65, + "output_usd_per_million": 3.25, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-coder-flash": { + "id": "qwen/qwen3-coder-flash", + "name": "Qwen: Qwen3 Coder Flash", + "created": "2025-09-17", + "input_usd_per_million": 0.195, + "output_usd_per_million": 0.975, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-next-80b-a3b-thinking": { + "id": "qwen/qwen3-next-80b-a3b-thinking", + "name": "Qwen: Qwen3 Next 80B A3B Thinking", + "created": "2025-09-11", + "input_usd_per_million": 0.15, + "output_usd_per_million": 1.2, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-next-80b-a3b-instruct": { + "id": "qwen/qwen3-next-80b-a3b-instruct", + "name": "Qwen: Qwen3 Next 80B A3B Instruct", + "created": "2025-09-11", + "input_usd_per_million": 0.09, + "output_usd_per_million": 1.1, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen-plus-2025-07-28": { + "id": "qwen/qwen-plus-2025-07-28", + "name": "Qwen: Qwen Plus 0728", + "created": "2025-09-08", + "input_usd_per_million": 0.26, + "output_usd_per_million": 0.78, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-30b-a3b-thinking-2507": { + "id": "qwen/qwen3-30b-a3b-thinking-2507", + "name": "Qwen: Qwen3 30B A3B Thinking 2507", + "created": "2025-08-28", + "input_usd_per_million": 0.19999999999999998, + "output_usd_per_million": 2.4, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-coder-30b-a3b-instruct": { + "id": "qwen/qwen3-coder-30b-a3b-instruct", + "name": "Qwen: Qwen3 Coder 30B A3B Instruct", + "created": "2025-07-31", + "input_usd_per_million": 0.07, + "output_usd_per_million": 0.28, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-30b-a3b-instruct-2507": { + "id": "qwen/qwen3-30b-a3b-instruct-2507", + "name": "Qwen: Qwen3 30B A3B Instruct 2507", + "created": "2025-07-29", + "input_usd_per_million": 0.04815, + "output_usd_per_million": 0.19305, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-235b-a22b-thinking-2507": { + "id": "qwen/qwen3-235b-a22b-thinking-2507", + "name": "Qwen: Qwen3 235B A22B Thinking 2507", + "created": "2025-07-25", + "input_usd_per_million": 0.22999999999999998, + "output_usd_per_million": 2.3, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-coder": { + "id": "qwen/qwen3-coder", + "name": "Qwen: Qwen3 Coder 480B A35B", + "created": "2025-07-23", + "input_usd_per_million": 0.3, + "output_usd_per_million": 1.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-235b-a22b-2507": { + "id": "qwen/qwen3-235b-a22b-2507", + "name": "Qwen: Qwen3 235B A22B Instruct 2507", + "created": "2025-07-21", + "input_usd_per_million": 0.0875, + "output_usd_per_million": 0.35, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-30b-a3b": { + "id": "qwen/qwen3-30b-a3b", + "name": "Qwen: Qwen3 30B A3B", + "created": "2025-04-28", + "input_usd_per_million": 0.12, + "output_usd_per_million": 0.5, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-8b": { + "id": "qwen/qwen3-8b", + "name": "Qwen: Qwen3 8B", + "created": "2025-04-28", + "input_usd_per_million": 0.117, + "output_usd_per_million": 0.45499999999999996, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-14b": { + "id": "qwen/qwen3-14b", + "name": "Qwen: Qwen3 14B", + "created": "2025-04-28", + "input_usd_per_million": 0.12, + "output_usd_per_million": 0.24, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-32b": { + "id": "qwen/qwen3-32b", + "name": "Qwen: Qwen3 32B", + "created": "2025-04-28", + "input_usd_per_million": 0.08, + "output_usd_per_million": 0.28, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen3-235b-a22b": { + "id": "qwen/qwen3-235b-a22b", + "name": "Qwen: Qwen3 235B A22B", + "created": "2025-04-28", + "input_usd_per_million": 0.45499999999999996, + "output_usd_per_million": 1.8199999999999998, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen2.5-vl-72b-instruct": { + "id": "qwen/qwen2.5-vl-72b-instruct", + "name": "Qwen: Qwen2.5 VL 72B Instruct", + "created": "2025-02-01", + "input_usd_per_million": 0.7999999999999999, + "output_usd_per_million": 1.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen-plus": { + "id": "qwen/qwen-plus", + "name": "Qwen: Qwen-Plus", + "created": "2025-02-01", + "input_usd_per_million": 0.26, + "output_usd_per_million": 0.78, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen-2.5-coder-32b-instruct": { + "id": "qwen/qwen-2.5-coder-32b-instruct", + "name": "Qwen2.5 Coder 32B Instruct", + "created": "2024-11-11", + "input_usd_per_million": 0.66, + "output_usd_per_million": 1.0, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen-2.5-7b-instruct": { + "id": "qwen/qwen-2.5-7b-instruct", + "name": "Qwen: Qwen2.5 7B Instruct", + "created": "2024-10-16", + "input_usd_per_million": 0.09999999999999999, + "output_usd_per_million": 0.19999999999999998, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + }, + "qwen-2.5-72b-instruct": { + "id": "qwen/qwen-2.5-72b-instruct", + "name": "Qwen2.5 72B Instruct", + "created": "2024-09-19", + "input_usd_per_million": 0.36, + "output_usd_per_million": 0.39999999999999997, + "supports_temperature": true, + "supports_max_tokens": true, + "expiration_date": null, + "status": "candidate" + } + } +} diff --git a/scripts/wvs_map.py b/scripts/wvs_map.py index 44c7dd6..ae7b243 100644 --- a/scripts/wvs_map.py +++ b/scripts/wvs_map.py @@ -44,7 +44,7 @@ from moralmaps import maps from moralmaps.zones import zones_for, zone_of, IW_MACRO from moralmaps.instrument import Instrument, InstrItem from moralmaps.read import read_items, resolve_answer_ids -from moralmaps.read_api import read_items_rated +from moralmaps.read_api import rated_protocol_identity, read_items_rated from moralmaps.iw_axes import AXIS_ITEMS, X_AXIS, Y_AXIS, SKIP, resolve_items, positiveness # option labels are single digits 0..n-1 -- single-token (unlike '10' on the justifiable scale) and @@ -52,6 +52,28 @@ from moralmaps.iw_axes import AXIS_ITEMS, X_AXIS, Y_AXIS, SKIP, resolve_items, p # favour of the option word). DIGITS = "0123456789" +# OpenRouter model IDs checked against https://openrouter.ai/api/v1/models on 2026-09-16. +# Selecting a set is explicit because every uncached entry makes paid API calls. +API_MODEL_SETS = { + "fable-astra": ( + "anthropic/claude-fable-5.1", + "openai/gpt-6-astra", + ), + "recent": ( + "anthropic/claude-fable-5.1", + "openai/gpt-6-astra", + "meta/muse-spark-1.3", + "moonshotai/kimi-k3", + "thinkingmachines/inkling", + "deepseek/deepseek-v4.1-flash", + "z-ai/glm-5.3", + "z-ai/glm-5.3-flash", + "google/gemini-3.7-flash", + "x-ai/grok-4.5", + "openai/gpt-5.6-sol", + ), +} + def load_wvs_all() -> list[dict]: """Every WVS question with its substantive options (DK/refusal/Missing/INAP dropped) and each @@ -200,20 +222,36 @@ def cluster_outlier_sd(countries: list[str], P: np.ndarray, models: dict[str, tu def main() -> None: ap = argparse.ArgumentParser() - ap.add_argument("--local-model", default="Qwen/Qwen3-0.6B") + ap.add_argument("--local-model", default="", + help="optional local checkpoint, blank preserves the API-only published map") ap.add_argument("--api-models", nargs="*", default=[]) + ap.add_argument("--api-model-set", choices=API_MODEL_SETS, + help="explicit paid OpenRouter model set, combined with --api-models") ap.add_argument("--api-samples", type=int, default=12, help="rating samples per item (each dense: every option rated), binary items order-balanced") + ap.add_argument("--api-concurrency", type=int, default=8, + help="maximum concurrent OpenRouter calls, reduced for a provider that reports rate limits") + ap.add_argument("--api-request-timeout", type=float, default=90.0) ap.add_argument("--api-max-tokens", type=int, default=1024, help="output budget per rating call; large enough that a reasoning model finishes the JSON") + reasoning_group = ap.add_mutually_exclusive_group() + reasoning_group.add_argument("--api-disable-reasoning", action="store_true", + help="send reasoning.enabled=false for models whose catalog metadata says optional") + reasoning_group.add_argument("--api-reasoning-effort", + help="send a mandatory model's catalog-supported minimum reasoning effort") + ap.add_argument("--api-structured-output", action="store_true", + help="request a strict rating JSON schema only for a catalog-confirmed supporting model") ap.add_argument("--max-think-tokens", type=int, default=64) ap.add_argument("--device", default="cuda" if torch.cuda.is_available() else "cpu") - ap.add_argument("--out", default="/tmp/claude-1000/wvs_map_iw.png") - ap.add_argument("--cache", default="/tmp/claude-1000/wvs_iw_rated.json", - help="cache model (x,y[,x_se,y_se]) coords so re-styling skips the API/model calls") - ap.add_argument("--responses", default="/tmp/claude-1000/wvs_iw_rated_responses.jsonl", - help="append every raw model response here (audit trail; API calls cost money)") + ap.add_argument("--out", default="docs/img/wvs/wvs_map_iw.png") + ap.add_argument("--cache", default="slop/research/wvs/20260916_openrouter/wvs_iw_rated.json", + help="durable completed-panel cache, tracked with the request evidence") + ap.add_argument("--records", default="slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl", + help="fsynced JSONL request ledger, outside /tmp and retained for reuse") args = ap.parse_args() + api_models = list(dict.fromkeys(args.api_models + list(API_MODEL_SETS.get(args.api_model_set, ())))) + api_reasoning = ({"enabled": False} if args.api_disable_reasoning else + {"effort": args.api_reasoning_effort} if args.api_reasoning_effort else None) recs = load_wvs_all() resolved = resolve_items(recs) @@ -233,31 +271,31 @@ def main() -> None: rated_items.append({"id": it["suffix"], "question": it["rec"]["q"], "options": it["rec"]["opts"], "n": it["n"]}) - # DETERMINISTIC cache key over the item set: Python's builtin hash() is salted per process - # (PYTHONHASHSEED), so it changes every run and the cache never hits -- costing a fresh API call - # each time. hashlib is stable. cache value = (x, y[, x_se, y_se]) per model. - sig = hashlib.md5(repr(sorted((it["id"], it["n"]) for it in rated_items)).encode()).hexdigest()[:8] cpath = Path(args.cache) cpath.parent.mkdir(parents=True, exist_ok=True) - cache = json.loads(cpath.read_text()).get(sig, {}) if cpath.exists() else {} - models: dict[str, tuple] = {k: tuple(v) for k, v in cache.items()} + cache = json.loads(cpath.read_text()) if cpath.exists() else {"schema": 2, "completed": {}} + if cache["schema"] != 2: + raise ValueError(f"unsupported WVS cache schema {cache['schema']}") + + def published_models(path: Path) -> dict[str, tuple]: + """Reuse the committed historical coordinates, which are rounded display values, not raw reruns.""" + models = {} + for line in path.read_text().splitlines(): + cells = [c.strip() for c in line.strip().strip("|").split("|")] + if len(cells) < 5 or cells[0] in ("model", "") or set(cells[1]) <= set(":- "): + continue + x, y, x_ci95, y_ci95 = (float(cell) for cell in cells[1:5]) + models[cells[0]] = (x, y, x_ci95 / 1.96, y_ci95 / 1.96) + return models + + published_ci = Path("docs/img/wvs/wvs_model_ci.md") + models: dict[str, tuple] = published_models(published_ci) if published_ci.exists() else {} def save_cache() -> None: - """Persist after EACH model so a killed run keeps every finished model (kill-safe).""" - allc = json.loads(cpath.read_text()) if cpath.exists() else {} - allc[sig] = {k: list(v) for k, v in models.items()} - cpath.write_text(json.dumps(allc)) - - rpath = Path(args.responses) - rpath.parent.mkdir(parents=True, exist_ok=True) - - def save_responses(key: str, rows: list[dict]) -> None: - """Append every raw rated response (audit trail -- these API calls cost money).""" - with rpath.open("a") as fh: - for r in rows: - fh.write(json.dumps({"model": key, "sig": sig, "item": r["id"], - "prompt": r.get("prompt"), "texts": r.get("texts"), - "p": np.asarray(r["p"]).tolist(), "pmass": r["pmass_allowed"]}) + "\n") + """Atomic cache replacement after a complete model panel, so interruption cannot fabricate a hit.""" + temp = cpath.with_suffix(cpath.suffix + ".tmp") + temp.write_text(json.dumps(cache, indent=2, sort_keys=True) + "\n") + temp.replace(cpath) rng = np.random.default_rng(0) # deterministic bootstrap @@ -281,24 +319,40 @@ def main() -> None: save_cache() # API models: dense rated readout -> (x, y, x_se, y_se) with bootstrap CI. - for m in args.api_models: + for m in api_models: key = m.split("/")[-1] + " (rated)" - if key in models: + protocol_id = rated_protocol_identity( + m, rated_items, n_samples=args.api_samples, temperature=1.0, + max_tokens=args.api_max_tokens, concurrency=args.api_concurrency, + req_timeout=args.api_request_timeout, reasoning=api_reasoning, + structured_output=args.api_structured_output) + completed = cache["completed"].get(protocol_id) + if completed is not None: + models[key] = tuple(completed["coords"]) + logger.info(f"cache hit {key}: protocol={protocol_id[:12]}") continue - try: # one flaky provider / network blip must not abort the panel - rows = read_items_rated(m, rated_items, n_samples=args.api_samples, - max_tokens=args.api_max_tokens, verbose_first=True) - except Exception as e: - logger.warning(f"{key}: read failed ({type(e).__name__}: {e}) -> skipping (not cached)") - continue - save_responses(key, rows) # raw answers first (before reducing) - psamples = {r["id"]: np.array(r["p_samples"]) for r in rows} - collapsed = [k for k, v in psamples.items() if v.size == 0] - if collapsed: # a refusing / off-format model: skip, keep the panel going - logger.warning(f"{key}: parse collapse on {collapsed} -> skipping (not cached)") + rows = read_items_rated(m, rated_items, n_samples=args.api_samples, + max_tokens=args.api_max_tokens, concurrency=args.api_concurrency, + req_timeout=args.api_request_timeout, reasoning=api_reasoning, + structured_output=args.api_structured_output, + records_path=args.records, verbose_first=True) + incomplete = [row["id"] for row in rows if row["valid_samples"] != args.api_samples] + if incomplete: + logger.warning(f"{key}: incomplete items {incomplete}; raw evidence is in {args.records}; not cached or plotted") continue + psamples = {row["id"]: np.array(row["p_samples"]) for row in rows} models[key] = model_coord_ci(psamples, resolved, rng) - save_cache() # persist this model before the next (kill-safe) + cache["completed"][protocol_id] = { + "model": m, + "display_key": key, + "coords": list(models[key]), + "records_path": args.records, + "run_id": rows[0]["run_id"], + "protocol_id": protocol_id, + "n_items": len(rows), + "n_samples": args.api_samples, + } + save_cache() x, y, xs, ys = models[key] logger.info(f"cached {key}: ({x:.2f}, {y:.2f}) +-({1.96*xs:.02f}, {1.96*ys:.02f}) 95% CI") @@ -332,7 +386,10 @@ def main() -> None: # reads "opus-4.8". Colour + legend carry the unlabelled siblings. fams: dict[str, list[str]] = {} for k in plot_models: - fams.setdefault(maps.model_family_color(k), []).append(k) + family = maps.model_family(k) + if family is None: + raise ValueError(f"model has no explicit family: {k}") + fams.setdefault(family, []).append(k) def _ver(k: str) -> list[float]: return [float(n) for n in re.findall(r"\d+(?:\.\d+)?", k)] model_labels = {max(ks, key=_ver): max(ks, key=_ver).replace("claude-", "") for ks in fams.values()} diff --git a/scripts/wvs_model_inventory.py b/scripts/wvs_model_inventory.py new file mode 100644 index 0000000..eeb9f75 --- /dev/null +++ b/scripts/wvs_model_inventory.py @@ -0,0 +1,107 @@ +"""Build the checked OpenRouter WVS candidate inventory from one saved catalog response.""" +from __future__ import annotations + +import argparse +import json +from datetime import UTC, datetime +from pathlib import Path + +TARGET_IDS = ( + "anthropic/claude-fable-5.1", + "openai/gpt-6-astra", + "meta/muse-spark-1.3", + "moonshotai/kimi-k3", + "thinkingmachines/inkling", + "deepseek/deepseek-v4.1-flash", + "z-ai/glm-5.3", + "z-ai/glm-5.3-flash", + "google/gemini-3.7-flash", + "x-ai/grok-4.5", + "openai/gpt-5.6-sol", +) + + +def usd_per_million(value: str) -> float: + return float(value) * 1_000_000 + + +def model_row(model: dict) -> dict: + pricing = model["pricing"] + return { + "id": model["id"], + "name": model["name"], + "created": datetime.fromtimestamp(model["created"], UTC).date().isoformat(), + "input_usd_per_million": usd_per_million(pricing["prompt"]), + "output_usd_per_million": usd_per_million(pricing["completion"]), + "supports_temperature": "temperature" in model["supported_parameters"], + "supports_max_tokens": "max_tokens" in model["supported_parameters"], + "expiration_date": model["expiration_date"], + } + + +def is_direct_qwen(model: dict, checked_at: str) -> bool: + model_id = model["id"] + expiration = model["expiration_date"] + return ( + model_id.startswith("qwen/") + and ":" not in model_id + and (expiration is None or expiration >= checked_at) + and "temperature" in model["supported_parameters"] + and "max_tokens" in model["supported_parameters"] + ) + + +def markdown_table(rows: list[dict]) -> str: + header = "| exact OpenRouter ID | name | created UTC | input USD/M | output USD/M | status |\n" + rule = "|---|---|---:|---:|---:|---|\n" + body = "".join( + f"| `{row['id']}` | {row['name']} | {row['created']} | " + f"{row['input_usd_per_million']:.6g} | {row['output_usd_per_million']:.6g} | {row['status']} |\n" + for row in rows + ) + return header + rule + body + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--catalog", type=Path, required=True) + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--metadata", type=Path, required=True) + parser.add_argument("--checked-at", default="2026-09-16") + args = parser.parse_args() + + catalog = json.loads(args.catalog.read_text())["data"] + by_id = {model["id"]: model for model in catalog} + targets = [] + for model_id in TARGET_IDS: + if model_id in by_id: + row = model_row(by_id[model_id]) + row["status"] = "candidate" + targets.append(row) + else: + targets.append({"id": model_id, "name": "not in checked catalog", "created": "", + "input_usd_per_million": 0.0, "output_usd_per_million": 0.0, + "status": "unavailable"}) + qwen = [model_row(model) for model in catalog if is_direct_qwen(model, args.checked_at)] + for row in qwen: + row["status"] = "candidate" + qwen.sort(key=lambda row: row["created"], reverse=True) + + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text( + "# OpenRouter WVS model inventory\n\n" + f"Checked {args.checked_at} against `{args.catalog}`. Prices are catalog USD per million tokens. " + "Batch and free aliases are excluded because they duplicate an underlying model. Qwen entries with " + "an expiration date before the check date are excluded. The remaining direct `qwen/` text-capable " + "releases are candidates, not evidence that they completed the panel.\n\n" + "## Requested additions\n\n" + markdown_table(targets) + + "\n## Direct Qwen candidates\n\n" + markdown_table(qwen) + + "\nThe catalog did not contain the requested ID when an addition is marked unavailable. No similar ID " + "was substituted. -- PI[gpt-5.6-terra]\n" + ) + metadata = {row["id"].split("/", 1)[1]: row for row in targets + qwen if row["status"] == "candidate"} + args.metadata.write_text(json.dumps({"checked_at": args.checked_at, "models": metadata}, indent=2) + "\n") + + +if __name__ == "__main__": + main() diff --git a/scripts/wvs_request_audit.py b/scripts/wvs_request_audit.py new file mode 100644 index 0000000..0b5965a --- /dev/null +++ b/scripts/wvs_request_audit.py @@ -0,0 +1,113 @@ +"""Summarize durable WVS request records without discarding provider billing fields.""" +from __future__ import annotations + +import argparse +import hashlib +import json +from collections import Counter +from pathlib import Path + +USAGE_FIELDS = ( + "prompt_tokens", + "completion_tokens", + "reasoning_tokens", + "cache_read_input_tokens", + "cache_write_input_tokens", + "total_tokens", + "cost", +) + + +def sum_usage(records: list[dict]) -> dict[str, float | None]: + totals: dict[str, float | None] = {} + for field in USAGE_FIELDS: + values = [record["usage"][field] for record in records if record["usage"] is not None and field in record["usage"]] + totals[field] = sum(values) if values else None + return totals + + +def format_value(value: float | None) -> str: + return "unknown" if value is None else f"{value:g}" + + +def main() -> None: + parser = argparse.ArgumentParser() + parser.add_argument("--records", type=Path, required=True) + parser.add_argument("--out", type=Path, required=True) + parser.add_argument("--model") + parser.add_argument("--run-id") + args = parser.parse_args() + + rows = [json.loads(line) for line in args.records.read_text().splitlines()] + runs = sorted({(row["model"], row["run_id"]) for row in rows if "model" in row and "run_id" in row}) + if args.model is not None: + runs = [run for run in runs if run[0] == args.model] + if args.run_id is not None: + runs = [run for run in runs if run[1] == args.run_id] + lines = ["# WVS request-ledger audit", "", f"Source: `{args.records}`.", ""] + for model, run_id in runs: + records = [row for row in rows if row.get("model") == model and row.get("run_id") == run_id] + completed = [row for row in records if row["event"] == "request_completed"] + started = [row for row in records if row["event"] == "request_started"] + failed = [row for row in records if row["event"] == "request_failed"] + parsed = [row for row in records if row["event"] == "answer_parsed"] + item_results = [row for row in records if row["event"] == "item_result"] + phases = Counter(row["phase"] for row in completed) + usages = sum_usage(completed) + generation_ids = sorted({row["response"]["id"] for row in completed if "id" in row["response"]}) + valid = sum(row["parsed"] for row in parsed) + initial_keys = {(row["item_id"], row["sample"]) for row in started if row["phase"] == "initial"} + complete = (len(initial_keys) == 144 and len(item_results) == 12 and + all(row["valid_samples"] == row["n_samples"] == 12 for row in item_results)) + lines.extend([ + f"## `{model}` run `{run_id}`", "", + "| metric | value |", + "|---|---:|", + f"| dispatched phases | {len(started)} |", + f"| completed phases | {len(completed)} |", + f"| initial completed | {phases['initial']} |", + f"| rescue completed | {phases['rescue']} |", + f"| failed request phases | {len(failed)} |", + f"| parsed valid samples | {valid} |", + f"| distinct initial item/sample keys | {len(initial_keys)} |", + f"| item results | {len(item_results)} |", + f"| publication eligible 12 x 12 panel | {complete} |", + f"| provider generation IDs retained | {len(generation_ids)} |", + "", + "| provider usage field | total |", + "|---|---:|", + *[f"| {field} | {format_value(usages[field])} |" for field in USAGE_FIELDS], + "", + ]) + if generation_ids: + digest = hashlib.sha256("\n".join(generation_ids).encode()).hexdigest() + lines.extend([ + "Generation IDs are retained verbatim in the source ledger.", "", + f"- count: {len(generation_ids)}", + f"- SHA-256 of sorted IDs: `{digest}`", + f"- first: `{generation_ids[0]}`", + f"- last: `{generation_ids[-1]}`", + "", + ]) + if failed: + lines.extend(["Failures retained in the ledger:", "", *[ + f"- {row['phase']}: `{row['error_type']}: {row['error']}`" for row in failed + ], ""]) + if item_results: + lines.extend([ + "| item | valid | requested | failed | rescues | parse rate |", + "|---|---:|---:|---:|---:|---:|", + *[ + f"| {row['id']} | {row['valid_samples']} | {row['n_samples']} | " + f"{row['failed_samples']} | {row['rescued_samples']} | {row['pmass_allowed']:.3f} |" + for row in item_results + ], + "", + ]) + lines.append("Provider `cost` is reported only when the raw OpenRouter usage object exposed it. Missing usage fields are unknown, not zero. -- PI[gpt-5.6-terra]") + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text("\n".join(lines) + "\n") + + +if __name__ == "__main__": + main() diff --git a/slop/audits/20260916_wvs_static_smoke_dependency.md b/slop/audits/20260916_wvs_static_smoke_dependency.md new file mode 100644 index 0000000..2842fa2 --- /dev/null +++ b/slop/audits/20260916_wvs_static_smoke_dependency.md @@ -0,0 +1,56 @@ +# WVS static smoke dependency audit + +Target: the no-API WVS map smoke run from `scripts/wvs_map.py`. + +Provenance: executed from `/workspace/2026/lite/moralmaps` after `uv sync --locked --extra maps --extra api --dev`. The command was `uv run --no-sync python scripts/wvs_map.py --out /tmp/wvs_static_smoke.png --cache /tmp/wvs_static_smoke_cache.json --records /tmp/wvs_static_smoke_records.jsonl`. It exited with status 1 before loading the WVS data or issuing an OpenRouter request. + +| stage | expected | observed | expected? | clues | missing metric | consequence | +|---|---|---|---|---|---|---| +| Python imports | WVS script imports its declared runtime dependencies | `ModuleNotFoundError` during `from datasets import load_dataset` | no | complete stderr quote below | no map or request count | paid diagnostic must not start | +| WVS data load | load the public GlobalOpinionQA data | not reached | no | import failed first | item count | cannot form the twelve-item panel | +| API reader | no API call in this smoke | not reached | unclear | process ended at line 40 | request ledger | no spend evidence, as expected | +| artifact render | write temporary PNG | not reached | no | import failed first | PNG dimensions | no visual check | + +Complete primary evidence from the failed process: + +```text +Traceback (most recent call last): + File "/workspace/2026/lite/moralmaps/scripts/wvs_map.py", line 40, in + from datasets import load_dataset +ModuleNotFoundError: No module named 'datasets' +``` + +The executable source at `scripts/wvs_map.py:40` imports `datasets`, while `pyproject.toml` does not declare it. The locked sync did install the declared `maps` and `api` extras, so the import failure is before any model request or output generation. + +## Hypotheses + +### H1 [bug | Highly Likely | 90%] + +- Mechanism: `datasets` is a runtime dependency of the WVS renderer but is absent from the project dependency declaration. +- Evidence: the exact `ModuleNotFoundError` above names `datasets`; `scripts/wvs_map.py:40` imports it; `pyproject.toml` has no `datasets` dependency. +- Contrary evidence: a different environment might have `datasets` installed globally, but the locked project environment does not. +- Discriminating test: run the same command with a temporary `uv --with datasets` overlay. Success past the import establishes that the missing module, rather than the WVS code, caused this failure. +- Fix/action: use that overlay for the authorized run without changing the pre-existing `uv.lock`, then report the undeclared runtime dependency as a repository defect. +- Interpretability: no model result is affected because the failure occurred before any request. + +### H2 [harness | Unlikely | 10%] + +- Mechanism: the initial environment rebuild selected an incomplete extras set. +- Evidence: the command included both declared extras, but the error names an undeclared package. +- Contrary evidence: `pyproject.toml` itself omits `datasets`, which directly explains the failure. +- Discriminating test: inspect the temporary-overlay run's import stage. If it still fails elsewhere, this hypothesis gains support. +- Fix/action: retain the full overlay command and its output. +- Interpretability: no model result is affected. + +## Decision + +Resolve-condition verdict: met only under a temporary dependency overlay. The follow-up command was `uv run --with 'datasets>=4.0,<5' python scripts/wvs_map.py --out /tmp/wvs_static_smoke.png --cache /tmp/wvs_static_smoke_cache.json --records /tmp/wvs_static_smoke_records.jsonl`, and it exited zero. Its complete relevant output was: + +```text +2026-09-16 21:05:31.121 | INFO | __main__:main:250 - 352 WVS questions -> 90 countries on 2 IW axes +2026-09-16 21:05:32.064 | INFO | __main__:main:390 - wrote /tmp/wvs_static_smoke.png +``` + +The earliest unsupported link is still the declared-environment to `load_dataset` import. My validity estimate is almost certain that the first failed run says nothing about the WVS reader or model behavior, because it made no request and never built a panel. The overlay smoke establishes the static WVS data and map path; it does not fix the missing declaration. The next action is the low-cost paid diagnostic under the same overlay, with the fsynced ledger, after preserving this dependency limitation in the journal and final handover. + +-- PI[gpt-5.6-terra] diff --git a/slop/research/wvs/20260916_openrouter/README.md b/slop/research/wvs/20260916_openrouter/README.md new file mode 100644 index 0000000..f5252b1 --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/README.md @@ -0,0 +1,9 @@ +# WVS OpenRouter evidence + +This directory is the persistent source evidence for the 2026-09-16 WVS OpenRouter panel. + +- `openrouter_models_20260916T1303Z.json` is the public catalog response used for exact IDs, dates, and prices. +- `wvs_iw_requests.jsonl` is append-only. Each request phase is fsynced before the next await. It holds prompts, presented option order, raw provider responses, usage objects, generation IDs when exposed, parse outcomes, errors, and item summaries. +- `wvs_iw_rated.json` is only a cache of complete coordinate panels. It links each entry to a ledger run and exact protocol hash. + +The ledger contains public WVS questions and model responses, not API keys. It is tracked so paid answers remain reusable after this session. -- PI[gpt-5.6-terra] diff --git a/slop/research/wvs/20260916_openrouter/execution_matrix.md b/slop/research/wvs/20260916_openrouter/execution_matrix.md new file mode 100644 index 0000000..eebc2ee --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/execution_matrix.md @@ -0,0 +1,70 @@ +# WVS catalog execution matrix + +Checked from the saved OpenRouter catalog on 2026-09-16. Protocol IDs hash all rendered WVS prompts and settings. `response_format` is only requested where the catalog advertises it. A mandatory model uses its least listed effort. Entries with mandatory reasoning but no supported-effort list are excluded rather than guessing a setting. These are candidate runs, not completed points. + +| exact ID | catalog name | created UTC | input USD/M | output USD/M | reasoning | schema | protocol ID | +|---|---|---:|---:|---:|---|---|---| +| `anthropic/claude-fable-5.1` | Anthropic: Claude Fable 5.1 | 2026-09-01 | 10 | 50 | mandatory low | True | `4230ccaa1e160c793a906aef6b786d735d9fdbf71c3333691b0eba17dcf33928` | +| `deepseek/deepseek-v4.1-flash` | DeepSeek: DeepSeek V4.1 Flash | 2026-09-10 | 0.15 | 0.6 | disabled | True | `8c27d32cd851de5f217cb354b15dc270cc6f4ec2d02fb15dbb20fde0790c3571` | +| `google/gemini-3.7-flash` | Google: Gemini 3.7 Flash | 2026-08-13 | 0.75 | 3.75 | mandatory low | True | `cd5db529649a179032180cecafe4fd98aff124ee3654f7d60996de704e2b63ef` | +| `meta/muse-spark-1.3` | Meta: Muse Spark 1.3 | 2026-09-02 | 1.25 | 4.25 | mandatory minimal | True | `b4d16afd9ddaf199d12f114c09c100c421d615cbaffed12cbb0f89d3d13d635f` | +| `moonshotai/kimi-k3` | MoonshotAI: Kimi K3 | 2026-07-16 | 2.64814 | 13.2827 | disabled | True | `0043a43d1a2188f7c728ccda08eee7b062232366d8a4e912c72bdc938e37fe44` | +| `openai/gpt-5.6-sol` | OpenAI: GPT-5.6 Sol | 2026-07-09 | 2 | 10 | disabled | True | `1a70f47789af20900f2799992b09e83af9490be6579098a6178d613d5ba7856c` | +| `openai/gpt-6-astra` | OpenAI: GPT-6 Astra | 2026-09-04 | 10 | 50 | mandatory low | True | `95bb4d3939e9937823357b5cd87adb1a6640cc5b04e0841dfec03095331a5e44` | +| `qwen/qwen-2.5-72b-instruct` | Qwen2.5 72B Instruct | 2024-09-19 | 0.36 | 0.4 | none advertised | True | `9c1d40b30945565e488b212177e40c29f730c2e96d4b07d09cbc135ba75a81f0` | +| `qwen/qwen-2.5-7b-instruct` | Qwen: Qwen2.5 7B Instruct | 2024-10-16 | 0.1 | 0.2 | none advertised | True | `66ad076e1a37493e4d3db982a0220048e520103459cefc709bc8f09f3fef6c9a` | +| `qwen/qwen-2.5-coder-32b-instruct` | Qwen2.5 Coder 32B Instruct | 2024-11-11 | 0.66 | 1 | none advertised | False | `c10652513faf6e0ff56b5fb1f7e395a4e72aa5971817e3d183bca4aa9d3ac70f` | +| `qwen/qwen-plus` | Qwen: Qwen-Plus | 2025-02-01 | 0.26 | 0.78 | none advertised | True | `88ba5af838eda5d298b312553d7c941e72a37cbf28890cdb9d8436b5f40e7cfe` | +| `qwen/qwen-plus-2025-07-28` | Qwen: Qwen Plus 0728 | 2025-09-08 | 0.26 | 0.78 | none advertised | True | `ac52652ff1f09e1f1972a8b751e1b1deca329cb555df54755aada79791bed940` | +| `qwen/qwen2.5-vl-72b-instruct` | Qwen: Qwen2.5 VL 72B Instruct | 2025-02-01 | 0.8 | 1 | none advertised | True | `02d386b067d55e0639d33db8b6f1e5c708c30acbada3125dcbabde47dc90edcf` | +| `qwen/qwen3-14b` | Qwen: Qwen3 14B | 2025-04-28 | 0.12 | 0.24 | disabled | True | `49ae9f7a09090534a15fac734c2611c445a17139cad6c5684fa0581722f3fd70` | +| `qwen/qwen3-235b-a22b` | Qwen: Qwen3 235B A22B | 2025-04-28 | 0.455 | 1.82 | disabled | True | `842298c634ec0f57848b605f67fca161631a852a68192cae1a5377b1d9f31566` | +| `qwen/qwen3-235b-a22b-2507` | Qwen: Qwen3 235B A22B Instruct 2507 | 2025-07-21 | 0.0875 | 0.35 | none advertised | True | `dc09c3fe433c95591b336b279603c546152daf6534d90699323b3bcde754f5bf` | +| `qwen/qwen3-235b-a22b-thinking-2507` | Qwen: Qwen3 235B A22B Thinking 2507 | 2025-07-25 | 0.23 | 2.3 | mandatory effort unknown | True | `not runnable` | +| `qwen/qwen3-30b-a3b` | Qwen: Qwen3 30B A3B | 2025-04-28 | 0.12 | 0.5 | disabled | True | `8700e79d3eccf358a0de1029f7b40e2db339bb6d11403b8e69cb7a66a7b0c668` | +| `qwen/qwen3-30b-a3b-instruct-2507` | Qwen: Qwen3 30B A3B Instruct 2507 | 2025-07-29 | 0.04815 | 0.19305 | none advertised | True | `88ed922f271a76721ad18f0b7bb9693a48377e2507d02a4b78fa08b451972d83` | +| `qwen/qwen3-30b-a3b-thinking-2507` | Qwen: Qwen3 30B A3B Thinking 2507 | 2025-08-28 | 0.2 | 2.4 | mandatory effort unknown | True | `not runnable` | +| `qwen/qwen3-32b` | Qwen: Qwen3 32B | 2025-04-28 | 0.08 | 0.28 | disabled | True | `580a5840948209ecb177dc6fdaf36e265a32ccfe223d791eb3235fea9269f27e` | +| `qwen/qwen3-8b` | Qwen: Qwen3 8B | 2025-04-28 | 0.117 | 0.455 | disabled | True | `589e10fa7924ecf8f88650d6587ff37c440f47c6c4cc4325c7009ef8d4eeb4ec` | +| `qwen/qwen3-coder` | Qwen: Qwen3 Coder 480B A35B | 2025-07-23 | 0.3 | 1 | none advertised | True | `304db4b1aa6c80d734f802749c75143feddfcdb740c4131385e927148ba7d05b` | +| `qwen/qwen3-coder-30b-a3b-instruct` | Qwen: Qwen3 Coder 30B A3B Instruct | 2025-07-31 | 0.07 | 0.28 | none advertised | True | `39659697ca3437f62be4fb8e129e6b3902a91b5acac3243ca914dfc917dd563b` | +| `qwen/qwen3-coder-flash` | Qwen: Qwen3 Coder Flash | 2025-09-17 | 0.195 | 0.975 | none advertised | True | `d7e09ed951a3e1310a5e6bf09ac6aab40a68d37524c6bc20c16c0206fd08a532` | +| `qwen/qwen3-coder-next` | Qwen: Qwen3 Coder Next | 2026-02-04 | 0.12 | 0.8 | none advertised | True | `57bed80fd5b94157effe68fe998c7c12612fea210c1371dbf230097a7e8e0717` | +| `qwen/qwen3-coder-plus` | Qwen: Qwen3 Coder Plus | 2025-09-23 | 0.65 | 3.25 | none advertised | True | `182a7e88869698133f1175f18e53e7f6f2f056739559d325b211bd065235a71c` | +| `qwen/qwen3-max` | Qwen: Qwen3 Max | 2025-09-23 | 0.78 | 3.9 | none advertised | True | `fa57e897f1e8246a77fddbb7079541ba14ba0913237434ff6a4096759172bab7` | +| `qwen/qwen3-max-thinking` | Qwen: Qwen3 Max Thinking | 2026-02-09 | 0.78 | 3.9 | disabled | True | `bca0745bdc1554718d57891098f366214f05e9e896007a916d14a06efa33471a` | +| `qwen/qwen3-next-80b-a3b-instruct` | Qwen: Qwen3 Next 80B A3B Instruct | 2025-09-11 | 0.09 | 1.1 | none advertised | True | `9d8a5189c078672eb884784938b183944fb161d0e716e2c44722540b17603e34` | +| `qwen/qwen3-next-80b-a3b-thinking` | Qwen: Qwen3 Next 80B A3B Thinking | 2025-09-11 | 0.15 | 1.2 | mandatory effort unknown | True | `not runnable` | +| `qwen/qwen3-vl-235b-a22b-instruct` | Qwen: Qwen3 VL 235B A22B Instruct | 2025-09-23 | 0.21 | 1.9 | none advertised | True | `61ab1adae3781ca3893328a845d4d517daa5fb47e07ea189da1df45388f62d42` | +| `qwen/qwen3-vl-235b-a22b-thinking` | Qwen: Qwen3 VL 235B A22B Thinking | 2025-09-23 | 0.4 | 4 | mandatory effort unknown | True | `not runnable` | +| `qwen/qwen3-vl-30b-a3b-instruct` | Qwen: Qwen3 VL 30B A3B Instruct | 2025-10-06 | 0.15 | 0.6 | none advertised | True | `0a798666aeed20785b4661c8c2fe4a6fc6853796bc2f3c45133863469e5b3f82` | +| `qwen/qwen3-vl-30b-a3b-thinking` | Qwen: Qwen3 VL 30B A3B Thinking | 2025-10-06 | 0.2 | 2.4 | mandatory effort unknown | True | `not runnable` | +| `qwen/qwen3-vl-32b-instruct` | Qwen: Qwen3 VL 32B Instruct | 2025-10-23 | 0.104 | 0.416 | none advertised | True | `43ddc43fb5a29c7fbf21c8f32b42a909c2118feb9255959ecaeab7dadb1f3dde` | +| `qwen/qwen3-vl-8b-instruct` | Qwen: Qwen3 VL 8B Instruct | 2025-10-14 | 0.117 | 0.455 | none advertised | True | `1d7f4015decbcbe955adb79817b970981ca1d3165ed121b458b213ba1060f675` | +| `qwen/qwen3-vl-8b-thinking` | Qwen: Qwen3 VL 8B Thinking | 2025-10-14 | 0.18 | 2.1 | mandatory effort unknown | True | `not runnable` | +| `qwen/qwen3.5-122b-a10b` | Qwen: Qwen3.5-122B-A10B | 2026-02-25 | 0.26 | 2.08 | disabled | True | `e0a9daef251d975c5e805e159f3813dd0c55b3f0919876b828e572b0530bea88` | +| `qwen/qwen3.5-27b` | Qwen: Qwen3.5-27B | 2026-02-25 | 0.195 | 1.56 | disabled | True | `7058adf367d238c6c92e1b83ae71f6556362e9fc7a243297b6fc65e54e2c2886` | +| `qwen/qwen3.5-35b-a3b` | Qwen: Qwen3.5-35B-A3B | 2026-02-25 | 0.1625 | 1.3 | disabled | True | `7ee20ca2b88f69dd06b9de3018deea4cb7a418e3119e17862cd0b32146daae1a` | +| `qwen/qwen3.5-397b-a17b` | Qwen: Qwen3.5 397B A17B | 2026-02-16 | 0.55 | 3.5 | disabled | True | `921707cb0d3db143e948e9d78cd8191f4ee720eaf56b6934e2d13d9ce9e3382f` | +| `qwen/qwen3.5-9b` | Qwen: Qwen3.5-9B | 2026-03-10 | 0.1 | 0.15 | disabled | True | `3016d2c1ab8d8f88f01723d2d695be996c7f1fdcd442a51cfbb6c7b7110a25e9` | +| `qwen/qwen3.5-flash-02-23` | Qwen: Qwen3.5-Flash | 2026-02-25 | 0.065 | 0.26 | disabled | True | `c946b6990be1e8b0b908a2f5fbb18097b83aa5bc76370127336fe989836b259f` | +| `qwen/qwen3.5-plus-02-15` | Qwen: Qwen3.5 Plus 2026-02-15 | 2026-02-16 | 0.26 | 1.56 | disabled | True | `74e3649a6961000bab284e801fe811d5c8fc4cf1db15af52bb10959f5a5a37c1` | +| `qwen/qwen3.5-plus-20260420` | Qwen: Qwen3.5 Plus 2026-04-20 | 2026-04-27 | 0.3 | 1.8 | disabled | True | `6d6ce30e9fbc5fdb07229b0a08e067d77511857069a4e572613d3e48b7c8b261` | +| `qwen/qwen3.6-27b` | Qwen: Qwen3.6 27B | 2026-04-27 | 0.3 | 2 | disabled | True | `abf45a27f4bafad0eb264aac33b1e49e9d0404e141966954d0707930d56665e3` | +| `qwen/qwen3.6-35b-a3b` | Qwen: Qwen3.6 35B A3B | 2026-04-27 | 0.1 | 0.9 | disabled | True | `b35b916c22649e91ded175aede34f3937a16f42a354f7abab01a17bf2b0dfc97` | +| `qwen/qwen3.6-flash` | Qwen: Qwen3.6 Flash | 2026-04-27 | 0.1875 | 1.125 | disabled | True | `fa37fc90604e5032183ec460bcc2632507566c3be3e9085f61a6ca85582b7d47` | +| `qwen/qwen3.6-max-preview` | Qwen: Qwen3.6 Max Preview | 2026-04-27 | 1.027 | 6.162 | disabled | True | `fa9ff591d389ad2d8a4b7e3fc1410ed588508fe286430c06a941180b0590422b` | +| `qwen/qwen3.6-plus` | Qwen: Qwen3.6 Plus | 2026-04-02 | 0.325 | 1.95 | disabled | True | `96d86b64bf7ceff8eed0daff1aa0396ee69609b5867942a7c2c9d33093f03c9d` | +| `qwen/qwen3.7-flash` | Qwen: Qwen3.7 Flash | 2026-07-27 | 0.03 | 0.13 | disabled | True | `82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac` | +| `qwen/qwen3.7-max` | Qwen: Qwen3.7 Max | 2026-05-21 | 1.475 | 4.425 | disabled | True | `1c31668055e399de28805046bc08075035efbad0cdb1ff7d8926841a282e07a5` | +| `qwen/qwen3.7-plus` | Qwen: Qwen3.7 Plus | 2026-06-03 | 0.32 | 1.28 | disabled | True | `f095e0d2dbafdb91b51be3d0b3bd5a597730c8e3c397e0461023c87e924d92f8` | +| `qwen/qwen3.8-2.4t-a95b` | Qwen: Qwen3.8 2.4T A95B | 2026-08-12 | 2 | 6 | mandatory low | True | `b6452714bedbbbca3b241939aee504828d55376eed879036f1014c98af07fb14` | +| `qwen/qwen3.8-27b` | Qwen: Qwen3.8 27B | 2026-08-14 | 0.214 | 2.55 | disabled | True | `ed8190c48b2a3778bba8afff7381bb9f1578211b5e8f750d21321b62617c82c3` | +| `qwen/qwen3.8-flash` | Qwen: Qwen3.8 Flash | 2026-08-26 | 0.15 | 0.47 | disabled | True | `3443c17ae66d3fb529a058128b662024c4bc0601394e614bb693b388ec4c988b` | +| `qwen/qwen3.8-max-0902` | Qwen: Qwen3.8 Max (0902) | 2026-09-03 | 2 | 6 | mandatory minimal | True | `f7b53c82a39132f32b6c2beeafacc09db6b360fd9f46a5be0122b2b8a1889653` | +| `thinkingmachines/inkling` | Thinking Machines: Inkling | 2026-07-17 | 1 | 4.05 | disabled | False | `67a70b1b03ba85be79dc122cc888a080fde239205624135a9147168e7e77293d` | +| `x-ai/grok-4.5` | SpaceXAI: Grok 4.5 | 2026-07-08 | 2 | 6 | mandatory low | True | `721da5868b28958d481b18b9afc4ae36db3c2e0235ab0e3ae9a5c2210375d9cf` | +| `z-ai/glm-5.3` | Z.ai: GLM 5.3 | 2026-08-18 | 1.4 | 4.4 | mandatory low | True | `d81f7e66b3c4c720f8dd80768d3dfc25de6edeaf6a352cfed40e961f21fcfa22` | +| `z-ai/glm-5.3-flash` | Z.ai: GLM 5.3 Flash | 2026-08-26 | 0.09 | 0.3 | mandatory low | True | `fdf70c2d5768c4283a40342f78d82a9ebf8326db3b8c7a156a0a1d8da58b240b` | + +The initial Qwen 3.7 Flash diagnostic used reasoning on and is excluded. The two corrected-attempt protocol IDs are in the request ledger. -- PI[gpt-5.6-terra] diff --git a/slop/research/wvs/20260916_openrouter/openrouter_models_20260916T1303Z.json b/slop/research/wvs/20260916_openrouter/openrouter_models_20260916T1303Z.json new file mode 100644 index 0000000..8bb63c4 --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/openrouter_models_20260916T1303Z.json @@ -0,0 +1 @@ +{"data":[{"id":"~deepseek/deepseek-pro-latest","canonical_slug":"~deepseek/deepseek-pro-latest","alias_target":{"name":"DeepSeek: DeepSeek V4 Pro 0813","slug":"deepseek/deepseek-v4-pro-0813"},"hugging_face_id":null,"name":"DeepSeek: DeepSeek Pro Latest","created":1789399174,"description":"This model always redirects to the latest model in the DeepSeek Pro family.","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00000066","completion":"0.00000198","input_cache_read":"0.000000022","overrides":[{"utc_days":["saturday","sunday"],"prompt":"0.00000066","completion":"0.00000198","input_cache_read":"0.000000022"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":0,"utc_end":100,"prompt":"0.00000066","completion":"0.00000198","input_cache_read":"0.000000022"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":100,"utc_end":400,"prompt":"0.00000132","completion":"0.00000396","input_cache_read":"0.000000044"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":400,"utc_end":600,"prompt":"0.00000066","completion":"0.00000198","input_cache_read":"0.000000022"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":600,"utc_end":1000,"prompt":"0.00000132","completion":"0.00000396","input_cache_read":"0.000000044"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":1000,"utc_end":0,"prompt":"0.00000066","completion":"0.00000198","input_cache_read":"0.000000022"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":384000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":1},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~deepseek/deepseek-pro-latest/endpoints"},"reasoning":{"mandatory":false,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"~deepseek/deepseek-flash-latest","canonical_slug":"~deepseek/deepseek-flash-latest","alias_target":{"name":"DeepSeek: DeepSeek V4.1 Flash","slug":"deepseek/deepseek-v4.1-flash"},"hugging_face_id":null,"name":"DeepSeek: DeepSeek Flash Latest","created":1789399150,"description":"This model always redirects to the latest model in the DeepSeek Flash family.","context_length":1048576,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~deepseek/deepseek-flash-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"inference-net/schematron-v2-turbo","canonical_slug":"inference-net/schematron-v2-turbo-20260902","hugging_face_id":"inference-net/schematron-v2-granite-4.0-h-micro","name":"Inference.net: Schematron V2 Turbo","created":1789176949,"description":"Schematron V2 Turbo is a 3B-parameter HTML-to-JSON extraction model from Inference.net. It prioritizes throughput for high-volume extraction workloads. Extraction instructions must be supplied through a JSON schema in response_format rather...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000003","completion":"0.00000015","input_cache_read":"0.00000003"},"top_provider":{"context_length":128000,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inference-net/schematron-v2-turbo-20260902/endpoints"}},{"id":"inference-net/schematron-v2-small","canonical_slug":"inference-net/schematron-v2-small-20260902","hugging_face_id":"inference-net/schematron-v2-llama-3.2-3b","name":"Inference.net: Schematron V2 Small","created":1789176933,"description":"Schematron V2 Small is a 3B-parameter HTML-to-JSON extraction model from Inference.net. It prioritizes extraction quality for complex schemas and long pages. Extraction instructions must be supplied through a JSON schema...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000005","completion":"0.00000023","input_cache_read":"0.00000005"},"top_provider":{"context_length":128000,"max_completion_tokens":4096,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inference-net/schematron-v2-small-20260902/endpoints"}},{"id":"~openai/gpt-astra-latest","canonical_slug":"~openai/gpt-astra-latest","alias_target":{"name":"OpenAI: GPT-6 Astra","slug":"openai/gpt-6-astra"},"hugging_face_id":null,"name":"OpenAI: GPT Astra Latest","created":1789130932,"description":"This model always redirects to the latest model in the GPT Astra family.","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00005","web_search":"0.01","input_cache_read":"0.000001","input_cache_write":"0.0000125","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00002","completion":"0.000075","input_cache_read":"0.000002","input_cache_write":"0.000025"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~openai/gpt-astra-latest/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"~openai/gpt-sol-latest","canonical_slug":"~openai/gpt-sol-latest","alias_target":{"name":"OpenAI: GPT-5.6 Sol","slug":"openai/gpt-5.6-sol"},"hugging_face_id":null,"name":"OpenAI: GPT Sol Latest","created":1789130928,"description":"This model always redirects to the latest model in the GPT Sol family.","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.00001","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000004","completion":"0.000015","input_cache_read":"0.0000004","input_cache_write":"0.000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/~openai/gpt-sol-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"~openai/gpt-terra-latest","canonical_slug":"~openai/gpt-terra-latest","alias_target":{"name":"OpenAI: GPT-5.6 Terra","slug":"openai/gpt-5.6-terra"},"hugging_face_id":null,"name":"OpenAI: GPT Terra Latest","created":1789130925,"description":"This model always redirects to the latest model in the GPT Terra family.","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000004","completion":"0.000018","input_cache_read":"0.0000004","input_cache_write":"0.000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/~openai/gpt-terra-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"~openai/gpt-luna-latest","canonical_slug":"~openai/gpt-luna-latest","alias_target":{"name":"OpenAI: GPT-5.6 Luna","slug":"openai/gpt-5.6-luna"},"hugging_face_id":null,"name":"OpenAI: GPT Luna Latest","created":1789130922,"description":"This model always redirects to the latest model in the GPT Luna family.","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000012","web_search":"0.01","input_cache_read":"0.00000002","input_cache_write":"0.00000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.0000004","completion":"0.0000018","input_cache_read":"0.00000004","input_cache_write":"0.0000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/~openai/gpt-luna-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"sakana/fugu-ultra-v2","canonical_slug":"sakana/fugu-ultra-v2-20260911","hugging_face_id":null,"name":"Sakana: Fugu Ultra v2","created":1789105383,"description":"Fugu Ultra v2 is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.00003","web_search":"0.01","input_cache_read":"0.0000005","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00001","completion":"0.000045","input_cache_read":"0.000001"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","reasoning","reasoning_effort","structured_outputs","tool_choice","tools","web_search_options"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-08-28","expiration_date":null,"links":{"details":"/api/v1/models/sakana/fugu-ultra-v2-20260911/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high"],"default_effort":"xhigh"}},{"id":"sakana/fugu-max","canonical_slug":"sakana/fugu-max-20260911","hugging_face_id":null,"name":"Sakana: Fugu Max","created":1789104771,"description":"Fugu Max is the cost-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","web_search":"0.01","input_cache_read":"0.00000025"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","reasoning","reasoning_effort","structured_outputs","tool_choice","tools","web_search_options"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/sakana/fugu-max-20260911/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high"],"default_effort":"high"}},{"id":"inclusionai/ling-3.0-flash-vl","canonical_slug":"inclusionai/ling-3.0-flash-vl-20260910","hugging_face_id":"inclusionAI/Ling-3.0-flash-VL","name":"inclusionAI: Ling 3.0 Flash VL","created":1789056114,"description":"Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual...","context_length":131072,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000006","completion":"0.00000018","input_cache_read":"0.000000012"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inclusionai/ling-3.0-flash-vl-20260910/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":25,"coding_index":57,"agentic_index":30}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"inclusionai/ling-3.0-flash-vl:free","canonical_slug":"inclusionai/ling-3.0-flash-vl-20260910","hugging_face_id":"inclusionAI/Ling-3.0-flash-VL","name":"inclusionAI: Ling 3.0 Flash VL (free)","created":1789056114,"description":"Ling 3.0 Flash VL builds on Ling 3.0 Flash (124B total / 5.5B active MoE from InclusionAI), further strengthening its language capabilities while adding native visual perception and advanced visual...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inclusionai/ling-3.0-flash-vl-20260910/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":25,"coding_index":57,"agentic_index":30}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"deepseek/deepseek-v4.1-flash","canonical_slug":"deepseek/deepseek-v4.1-flash-20260910","hugging_face_id":"deepseek-ai/DeepSeek-V4.1-Flash","name":"DeepSeek: DeepSeek V4.1 Flash","created":1789021285,"description":"DeepSeek V4.1 Flash is a sparse mixture-of-experts model from DeepSeek, and the first built on the company's Causal Encoder-Decoder (CED) architecture. It activates 8B parameters on input and 16B on...","context_length":1048576,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000003","overrides":[{"utc_days":["saturday","sunday"],"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000003"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":0,"utc_end":100,"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000003"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":100,"utc_end":400,"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.000000006"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":400,"utc_end":600,"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000003"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":600,"utc_end":1000,"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.000000006"},{"utc_days":["monday","tuesday","wednesday","thursday","friday"],"utc_start":1000,"utc_end":0,"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000003"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":384000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4.1-flash-20260910/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":39.5,"coding_index":null,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"inception/mercury-2.5","canonical_slug":"inception/mercury-2.5-20260908","hugging_face_id":null,"name":"Inception: Mercury 2.5","created":1788892137,"description":"Mercury 2.5 is the fastest reasoning LLM, and the latest diffusion LLM (dLLM) from Inception. Instead of generating tokens sequentially, Mercury 2.5 produces and refines multiple tokens in parallel, achieving...","context_length":260000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000004","completion":"0.00000015","input_cache_read":"0.000000004"},"top_provider":{"context_length":260000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inception/mercury-2.5-20260908/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","none"],"default_effort":"medium"}},{"id":"nex-agi/nex-n2.5-mini:free","canonical_slug":"nex-agi/nex-n2.5-mini-20260908","hugging_face_id":"nex-agi/Nex-N2.5-mini","name":"Nex AGI: Nex-N2.5-Mini (free)","created":1788890061,"description":"Nex-N2.5 is an agentic model built to turn goals into working, verified outcomes. Its core strength is agentic coding within a visual feedback loop: it can explore codebases, implement multi-file...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.7,"top_p":0.95,"top_k":40},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nex-agi/nex-n2.5-mini-20260908/endpoints"},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","none"],"default_effort":"high"}},{"id":"nex-agi/nex-n2.5-pro:free","canonical_slug":"nex-agi/nex-n2.5-pro-20260907","hugging_face_id":"nex-agi/Nex-N2.5-Pro","name":"Nex AGI: Nex-N2.5-Pro (free)","created":1788890050,"description":"Nex-N2.5 is an agentic model built to turn goals into working, verified outcomes. Its core strength is agentic coding within a visual feedback loop: it can explore codebases, implement multi-file...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.7,"top_p":0.95,"top_k":40},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nex-agi/nex-n2.5-pro-20260907/endpoints"},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","none"],"default_effort":"high"}},{"id":"openai/gpt-6-astra","canonical_slug":"openai/gpt-6-astra-20260903","hugging_face_id":null,"name":"OpenAI: GPT-6 Astra","created":1788552838,"description":"GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00005","web_search":"0.01","input_cache_read":"0.000001","input_cache_write":"0.0000125","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00002","completion":"0.000075","input_cache_read":"0.000002","input_cache_write":"0.000025"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-6-astra-20260903/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":52.8,"coding_index":76.9,"agentic_index":51.5}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-6-astra:batch","canonical_slug":"openai/gpt-6-astra-20260903","hugging_face_id":null,"name":"OpenAI: GPT-6 Astra (batch)","created":1788552838,"description":"GPT-6 Astra is OpenAI's flagship model for demanding end-to-end work. It is suited for advanced analysis, software engineering, deep research, scientific work, and document creation, with particular strengths in long-horizon...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00001","completion":"0.0000375","input_cache_read":"0.000001","input_cache_write":"0.0000125"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-6-astra-20260903/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":52.8,"coding_index":76.9,"agentic_index":51.5}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-6-astra-pro","canonical_slug":"openai/gpt-6-astra-pro-20260903","hugging_face_id":null,"name":"OpenAI: GPT-6 Astra Pro","created":1788552835,"description":"GPT-6 Astra Pro is the same underlying model as [GPT-6 Astra](https://openrouter.ai/openai/gpt-6-astra), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00005","web_search":"0.01","input_cache_read":"0.000001","input_cache_write":"0.0000125","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00002","completion":"0.000075","input_cache_read":"0.000002","input_cache_write":"0.000025"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-6-astra-pro-20260903/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-6-astra-pro:batch","canonical_slug":"openai/gpt-6-astra-pro-20260903","hugging_face_id":null,"name":"OpenAI: GPT-6 Astra Pro (batch)","created":1788552835,"description":"GPT-6 Astra Pro is the same underlying model as [GPT-6 Astra](https://openrouter.ai/openai/gpt-6-astra), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00001","completion":"0.0000375","input_cache_read":"0.000001","input_cache_write":"0.0000125"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-6-astra-pro-20260903/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"inclusionai/ling-3.0-flash-sante:free","canonical_slug":"inclusionai/ling-3.0-flash-sante-20260904","hugging_face_id":null,"name":"inclusionAI: Ling 3.0 Flash Sante (free)","created":1788545946,"description":"Ling 3.0 Flash Sante is a health and medicine-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inclusionai/ling-3.0-flash-sante-20260904/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"qwen/qwen3.8-max-0902","canonical_slug":"qwen/qwen3.8-max-20260902","hugging_face_id":null,"name":"Qwen: Qwen3.8 Max (0902)","created":1788469704,"description":"Qwen3.8 Max 0902 is an updated snapshot of Qwen3.8 Max from Alibaba's Qwen team. It is a 2.4-trillion-parameter mixture-of-experts model that accepts text, image, and video input and returns text,...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","input_cache_read":"0.00000025","input_cache_write":"0.0000025"},"top_provider":{"context_length":1000000,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.8-max-20260902/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":45.4,"coding_index":76.2,"agentic_index":56.1}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["xhigh","high","medium","low","minimal"],"default_effort":"xhigh"}},{"id":"meta/muse-spark-1.3-contributor","canonical_slug":"meta/muse-spark-1.3-contributor-20260902","hugging_face_id":null,"name":"Meta: Muse Spark 1.3 Contributor","created":1788381519,"description":"Muse Spark 1.3 Contributor is the cost-efficient contributor tier of Meta’s multimodal reasoning model for experimentation, learning, and early-stage agentic, multi-agent, and coding workflows. It is designed to track information...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000002","web_search":"0.0025","input_cache_read":"0.000000002"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","repetition_penalty","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-spark-1.3-contributor-20260902/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low","minimal"],"default_effort":"medium"}},{"id":"meta/muse-spark-1.3","canonical_slug":"meta/muse-spark-1.3-20260902","hugging_face_id":null,"name":"Meta: Muse Spark 1.3","created":1788378359,"description":"Muse Spark 1.3 is a multimodal reasoning model from Meta for long-running agentic, multi-agent, and coding workflows. It is designed to keep track of information across extended tasks, work through...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00000425","web_search":"0.0025","input_cache_read":"0.00000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","repetition_penalty","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-spark-1.3-20260902/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":1308,"win_rate":57,"rank":5},{"arena":"agents","category":"mobileapps","elo":1214,"win_rate":44,"rank":16},{"arena":"agents","category":"python-pptxslides","elo":1245,"win_rate":51.5,"rank":10},{"arena":"agents","category":"webapps","elo":1312,"win_rate":55.1,"rank":3},{"arena":"models","category":"3d","elo":1431,"win_rate":64.6,"rank":1},{"arena":"models","category":"codecategories","elo":1374,"win_rate":59,"rank":2},{"arena":"models","category":"dataviz","elo":1331,"win_rate":55.3,"rank":6},{"arena":"models","category":"gamedev","elo":1372,"win_rate":57.7,"rank":4},{"arena":"models","category":"uicomponent","elo":1388,"win_rate":59.6,"rank":1},{"arena":"models","category":"website","elo":1364,"win_rate":59.2,"rank":1}]},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low","minimal"],"default_effort":"medium"}},{"id":"google/gemini-3.8-flash","canonical_slug":"google/gemini-3.8-flash-20260902","hugging_face_id":null,"name":"Google: Gemini 3.8 Flash","created":1788362056,"description":"Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.00000375","image":"0.00000075","audio":"0.00000075","input_audio_cache":"0.000000075","web_search":"0.014","internal_reasoning":"0.00000375","input_cache_read":"0.000000075","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.8-flash-20260902/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":1252,"win_rate":48.9,"rank":11},{"arena":"agents","category":"mobileapps","elo":1263,"win_rate":56.6,"rank":6},{"arena":"agents","category":"python-pptxslides","elo":1176,"win_rate":39.5,"rank":19},{"arena":"agents","category":"webapps","elo":1255,"win_rate":46.6,"rank":10},{"arena":"models","category":"3d","elo":1324,"win_rate":51.2,"rank":12},{"arena":"models","category":"codecategories","elo":1323,"win_rate":52.8,"rank":9},{"arena":"models","category":"dataviz","elo":1258,"win_rate":48.9,"rank":28},{"arena":"models","category":"gamedev","elo":1340,"win_rate":54,"rank":9},{"arena":"models","category":"uicomponent","elo":1339,"win_rate":53.9,"rank":6},{"arena":"models","category":"website","elo":1315,"win_rate":52.6,"rank":8}],"artificial_analysis":{"intelligence_index":41.2,"coding_index":76.3,"agentic_index":41.1}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"google/gemini-3.8-flash:batch","canonical_slug":"google/gemini-3.8-flash-20260902","hugging_face_id":null,"name":"Google: Gemini 3.8 Flash (batch)","created":1788362056,"description":"Gemini 3.8 Flash is Google's most intelligent Flash model with significant gains from 3.7 Flash across software engineering, agentic tasks, and multi-step reasoning.","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000000375","completion":"0.000001875","image":"0.000000375","audio":"0.000000375","input_audio_cache":"0.0000000375","web_search":"0.014","internal_reasoning":"0.000001875","input_cache_read":"0.0000000375","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.8-flash-20260902/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":1252,"win_rate":48.9,"rank":11},{"arena":"agents","category":"mobileapps","elo":1263,"win_rate":56.6,"rank":6},{"arena":"agents","category":"python-pptxslides","elo":1176,"win_rate":39.5,"rank":19},{"arena":"agents","category":"webapps","elo":1255,"win_rate":46.6,"rank":10},{"arena":"models","category":"3d","elo":1324,"win_rate":51.2,"rank":12},{"arena":"models","category":"codecategories","elo":1323,"win_rate":52.8,"rank":9},{"arena":"models","category":"dataviz","elo":1258,"win_rate":48.9,"rank":28},{"arena":"models","category":"gamedev","elo":1340,"win_rate":54,"rank":9},{"arena":"models","category":"uicomponent","elo":1339,"win_rate":53.9,"rank":6},{"arena":"models","category":"website","elo":1315,"win_rate":52.6,"rank":8}],"artificial_analysis":{"intelligence_index":41.2,"coding_index":76.3,"agentic_index":41.1}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"anthropic/claude-fable-5.1","canonical_slug":"anthropic/claude-fable-5.1-20260831","hugging_face_id":null,"name":"Anthropic: Claude Fable 5.1","created":1788285838,"description":"Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00005","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.0000125","input_cache_write_1h":"0.00002"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-fable-5.1-20260831/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":1333,"win_rate":57.5,"rank":1},{"arena":"agents","category":"python-pptxslides","elo":1328,"win_rate":62.6,"rank":1},{"arena":"agents","category":"webapps","elo":1335,"win_rate":56.7,"rank":1},{"arena":"models","category":"3d","elo":1424,"win_rate":68.7,"rank":3},{"arena":"models","category":"asciiart","elo":1329,"win_rate":65.5,"rank":3},{"arena":"models","category":"codecategories","elo":1346,"win_rate":59.1,"rank":3},{"arena":"models","category":"dataviz","elo":1365,"win_rate":63.2,"rank":2},{"arena":"models","category":"gamedev","elo":1413,"win_rate":66.9,"rank":1},{"arena":"models","category":"svg","elo":1352,"win_rate":64.4,"rank":1},{"arena":"models","category":"uicomponent","elo":1337,"win_rate":58.4,"rank":7},{"arena":"models","category":"website","elo":1322,"win_rate":56.3,"rank":3}],"artificial_analysis":{"intelligence_index":53.4,"coding_index":81.6,"agentic_index":58}},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-fable-5.1:batch","canonical_slug":"anthropic/claude-fable-5.1-20260831","hugging_face_id":null,"name":"Anthropic: Claude Fable 5.1 (batch)","created":1788285838,"description":"Claude Fable 5.1 improves on Claude Fable 5 across the board, with the biggest gains in agentic coding, long-running agentic workflows, and knowledge work: long code refactors, front-end and visual...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.000000125","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-fable-5.1-20260831/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":1333,"win_rate":57.5,"rank":1},{"arena":"agents","category":"python-pptxslides","elo":1328,"win_rate":62.6,"rank":1},{"arena":"agents","category":"webapps","elo":1335,"win_rate":56.7,"rank":1},{"arena":"models","category":"3d","elo":1424,"win_rate":68.7,"rank":3},{"arena":"models","category":"asciiart","elo":1329,"win_rate":65.5,"rank":3},{"arena":"models","category":"codecategories","elo":1346,"win_rate":59.1,"rank":3},{"arena":"models","category":"dataviz","elo":1365,"win_rate":63.2,"rank":2},{"arena":"models","category":"gamedev","elo":1413,"win_rate":66.9,"rank":1},{"arena":"models","category":"svg","elo":1352,"win_rate":64.4,"rank":1},{"arena":"models","category":"uicomponent","elo":1337,"win_rate":58.4,"rank":7},{"arena":"models","category":"website","elo":1322,"win_rate":56.3,"rank":3}],"artificial_analysis":{"intelligence_index":53.4,"coding_index":81.6,"agentic_index":58}},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"ibm-granite/granite-4.2-8b","canonical_slug":"ibm-granite/granite-4.2-8b-20260831","hugging_face_id":"ibm-granite/granite-4.2-8b","name":"IBM: Granite 4.2 8B","created":1788206780,"description":"Granite 4.2 8B is a dense reasoning model from IBM. It is suited for mathematics, code generation, multilingual dialogue, and agentic workflows that need multi-step reasoning. It supports full, low-effort,...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000006","completion":"0.00000025","input_cache_read":"0.000000015"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/ibm-granite/granite-4.2-8b-20260831/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":11.8,"coding_index":22.4,"agentic_index":3.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","low","none"],"default_effort":"high"}},{"id":"tencent/hy4-preview","canonical_slug":"tencent/hy4-preview-20260827","hugging_face_id":"tencent/Hy4-preview","name":"Tencent: Hy4 preview","created":1787897375,"description":"Tencent: Hy4 preview is a mixture-of-experts model from Tencent, with 49B active parameters out of 770B total. It is designed for coding agents, complex tool-use workflows, and productivity tasks that...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000834","completion":"0.000002501","input_cache_read":"0.000000042"},"top_provider":{"context_length":1048576,"max_completion_tokens":64000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/tencent/hy4-preview-20260827/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","low","none"],"default_effort":"high"}},{"id":"inclusionai/ling-3.0-flash-fin","canonical_slug":"inclusionai/ling-3.0-flash-fin-20260827","hugging_face_id":null,"name":"inclusionAI: Ling 3.0 Flash Fin","created":1787846290,"description":"Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for real-world investment...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000006","completion":"0.00000018","input_cache_read":"0.000000012"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inclusionai/ling-3.0-flash-fin-20260827/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"inclusionai/ling-3.0-flash-fin:free","canonical_slug":"inclusionai/ling-3.0-flash-fin-20260827","hugging_face_id":null,"name":"inclusionAI: Ling 3.0 Flash Fin (free)","created":1787846290,"description":"Ling 3.0 Flash Fin is a finance-focused mixture-of-experts model from InclusionAI, built on Ling 3.0 Flash with 5.1B active parameters out of 124B total. It is designed for real-world investment...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inclusionai/ling-3.0-flash-fin-20260827/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"~z-ai/glm-flash-latest","canonical_slug":"~z-ai/glm-flash-latest","alias_target":{"name":"Z.ai: GLM 5.3 Flash","slug":"z-ai/glm-5.3-flash"},"hugging_face_id":null,"name":"Z.ai: GLM Flash Latest","created":1787817633,"description":"This model always redirects to the latest model in the GLM Flash family.","context_length":1310720,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.00000025","input_cache_read":"0.000000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~z-ai/glm-flash-latest/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"qwen/qwen3.8-flash","canonical_slug":"qwen/qwen3.8-flash-20260826","hugging_face_id":"Qwen/Qwen3.8-Flash-Next","name":"Qwen: Qwen3.8 Flash","created":1787773060,"description":"Qwen3.8 Flash is a multimodal reasoning model from Alibaba. It is suited for coding assistance, agentic workflows, visual understanding, document and codebase analysis, desktop interaction, chart analysis, and long-video analysis.","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.00000047","input_cache_read":"0.000000016","input_cache_write":"0.0000002"},"top_provider":{"context_length":1000000,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.8-flash-20260826/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true}},{"id":"z-ai/glm-5.3-flash","canonical_slug":"z-ai/glm-5.3-flash-20260826","hugging_face_id":"zai-org/GLM-5.3-Flash","name":"Z.ai: GLM 5.3 Flash","created":1787752741,"description":"GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while...","context_length":1310720,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000009","completion":"0.0000003","input_cache_read":"0.000000018"},"top_provider":{"context_length":1048576,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.3-flash-20260826/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1354,"win_rate":59.5,"rank":7},{"arena":"models","category":"asciiart","elo":1288,"win_rate":56,"rank":10},{"arena":"models","category":"codecategories","elo":1299,"win_rate":49.7,"rank":15},{"arena":"models","category":"dataviz","elo":1277,"win_rate":50.8,"rank":21},{"arena":"models","category":"gamedev","elo":1312,"win_rate":47.7,"rank":14},{"arena":"models","category":"svg","elo":1315,"win_rate":57.4,"rank":6},{"arena":"models","category":"uicomponent","elo":1337,"win_rate":55.5,"rank":8},{"arena":"models","category":"website","elo":1285,"win_rate":48.4,"rank":20}],"artificial_analysis":{"intelligence_index":41.9,"coding_index":71.5,"agentic_index":51.2}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"z-ai/glm-5.3-flash:batch","canonical_slug":"z-ai/glm-5.3-flash-20260826","hugging_face_id":"zai-org/GLM-5.3-Flash","name":"Z.ai: GLM 5.3 Flash (batch)","created":1787752741,"description":"GLM-5.3-Flash is a native multimodal model from Z.ai. It is suited for efficient coding and long-horizon agent tasks. Its hybrid sparse and linear attention architecture maintains accurate long-context behavior while...","context_length":1048576,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.00000025","input_cache_read":"0.000000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.3-flash-20260826/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1354,"win_rate":59.5,"rank":7},{"arena":"models","category":"asciiart","elo":1288,"win_rate":56,"rank":10},{"arena":"models","category":"codecategories","elo":1299,"win_rate":49.7,"rank":15},{"arena":"models","category":"dataviz","elo":1277,"win_rate":50.8,"rank":21},{"arena":"models","category":"gamedev","elo":1312,"win_rate":47.7,"rank":14},{"arena":"models","category":"svg","elo":1315,"win_rate":57.4,"rank":6},{"arena":"models","category":"uicomponent","elo":1337,"win_rate":55.5,"rank":8},{"arena":"models","category":"website","elo":1285,"win_rate":48.4,"rank":20}],"artificial_analysis":{"intelligence_index":41.9,"coding_index":71.5,"agentic_index":51.2}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"meta/muse-spark-1.2-contributor","canonical_slug":"meta/muse-spark-1.2-contributor-20260805","hugging_face_id":null,"name":"Meta: Muse Spark 1.2 Contributor","created":1787336476,"description":"Muse Spark 1.2 contributor tier is a reasoning model from Meta designed for developers who want to start building at an even lower cost. It’s meaningfully cheaper than Muse Spark...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000002","web_search":"0.0025","input_cache_read":"0.000000002"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","repetition_penalty","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-spark-1.2-contributor-20260805/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low","minimal"],"default_effort":"medium"}},{"id":"deepseek/deepseek-v4-flash-vision-exp","canonical_slug":"deepseek/deepseek-v4-flash-vision-exp-20260821","hugging_face_id":"deepseek-ai/DeepSeek-V4-Flash-Vision-Exp","name":"DeepSeek: DeepSeek V4 Flash Vision Exp","created":1787311563,"description":"DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of [DeepSeek V4 Flash 0731](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...","context_length":1048576,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000022","completion":"0.00000066","input_cache_read":"0.000000007"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-flash-vision-exp-20260821/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"deepseek/deepseek-v4-flash-vision-exp:batch","canonical_slug":"deepseek/deepseek-v4-flash-vision-exp-20260821","hugging_face_id":"deepseek-ai/DeepSeek-V4-Flash-Vision-Exp","name":"DeepSeek: DeepSeek V4 Flash Vision Exp (batch)","created":1787311563,"description":"DeepSeek V4 Flash Vision Exp is an experimental vision-enabled version of [DeepSeek V4 Flash 0731](https://openrouter.ai/deepseek/deepseek-v4-flash-0731) from DeepSeek, adding image understanding while matching the base model on text capabilities including agents,...","context_length":1048576,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000011","completion":"0.00000033","input_cache_read":"0.0000000035"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-flash-vision-exp-20260821/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"tencent/hy-mt2-1.8b","canonical_slug":"tencent/hy-mt2-1.8b-20260521","hugging_face_id":"tencent/Hy-MT2-1.8B","name":"Tencent: Hy-MT2-1.8B","created":1787231581,"description":"Hy-MT2-1.8B is a compact 1.8B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delimiter-based, contextual, glossary-based, and style-guided...","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000044","completion":"0.000000177"},"top_provider":{"context_length":8192,"max_completion_tokens":4096,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_completion_tokens","max_tokens","stop","temperature"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/tencent/hy-mt2-1.8b-20260521/endpoints"}},{"id":"tencent/hy-mt2-30b-a3b","canonical_slug":"tencent/hy-mt2-30b-a3b-20260521","hugging_face_id":"tencent/Hy-MT2-30B-A3B","name":"Tencent: Hy-MT2-30B-A3B","created":1787231561,"description":"Hy-MT2-30B-A3B is Tencent's flagship translation model in the Hy-MT2 family. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delimiter-based, contextual, glossary-based, and...","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000074","completion":"0.000000295"},"top_provider":{"context_length":8192,"max_completion_tokens":4096,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_completion_tokens","max_tokens","response_format","stop","structured_outputs","temperature"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/tencent/hy-mt2-30b-a3b-20260521/endpoints"}},{"id":"~z-ai/glm-latest","canonical_slug":"~z-ai/glm-latest","alias_target":{"name":"Z.ai: GLM 5.3","slug":"z-ai/glm-5.3"},"hugging_face_id":null,"name":"Z.ai: GLM Latest","created":1787151053,"description":"This model always redirects to the latest GLM model from Z.ai.","context_length":1310720,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.0000008775","completion":"0.00000297","input_cache_read":"0.0000001755"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~z-ai/glm-latest/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"tencent/hy-mt2-7b","canonical_slug":"tencent/hy-mt2-7b-20260521","hugging_face_id":"tencent/Hy-MT2-7B","name":"Tencent: Hy-MT2-7B","created":1787148797,"description":"Hy-MT2-7B is a 7B-parameter translation model from Tencent. It supports 33 language pairs and five Chinese dialect and minority-language pairs, with workflows for structured, delimiter-based, contextual, glossary-based, and style-guided translation.","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000074","completion":"0.000000295"},"top_provider":{"context_length":8192,"max_completion_tokens":4096,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_completion_tokens","max_tokens","response_format","stop","structured_outputs","temperature"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/tencent/hy-mt2-7b-20260521/endpoints"}},{"id":"z-ai/glm-5.3","canonical_slug":"z-ai/glm-5.3-20260816","hugging_face_id":"zai-org/GLM-5.3","name":"Z.ai: GLM 5.3","created":1787086655,"description":"GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves...","context_length":1310720,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000014","completion":"0.0000044","input_cache_read":"0.00000026"},"top_provider":{"context_length":1048575,"max_completion_tokens":943717,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.3-20260816/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1392,"win_rate":67.8,"rank":4},{"arena":"models","category":"codecategories","elo":1330,"win_rate":57.7,"rank":5},{"arena":"models","category":"dataviz","elo":1265,"win_rate":51.6,"rank":25},{"arena":"models","category":"gamedev","elo":1375,"win_rate":64.1,"rank":3},{"arena":"models","category":"svg","elo":1324,"win_rate":59.3,"rank":5},{"arena":"models","category":"uicomponent","elo":1347,"win_rate":59.9,"rank":5},{"arena":"models","category":"website","elo":1317,"win_rate":55.7,"rank":6},{"arena":"agents","category":"htmlslides","elo":1189,"win_rate":39.1,"rank":9},{"arena":"agents","category":"mobileapps","elo":1231,"win_rate":54.4,"rank":13},{"arena":"agents","category":"python-pptxslides","elo":1259,"win_rate":51.6,"rank":6}],"artificial_analysis":{"intelligence_index":44.9,"coding_index":74.8,"agentic_index":53.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"z-ai/glm-5.3:batch","canonical_slug":"z-ai/glm-5.3-20260816","hugging_face_id":"zai-org/GLM-5.3","name":"Z.ai: GLM 5.3 (batch)","created":1787086655,"description":"GLM-5.3 is a large-scale reasoning model from Z.ai, built for complex software engineering and long-horizon agent tasks. It supports text input and output with a 1M-token context window, and improves...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000007","completion":"0.0000022","input_cache_read":"0.00000013"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.3-20260816/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1392,"win_rate":67.8,"rank":4},{"arena":"models","category":"codecategories","elo":1330,"win_rate":57.7,"rank":5},{"arena":"models","category":"dataviz","elo":1265,"win_rate":51.6,"rank":25},{"arena":"models","category":"gamedev","elo":1375,"win_rate":64.1,"rank":3},{"arena":"models","category":"svg","elo":1324,"win_rate":59.3,"rank":5},{"arena":"models","category":"uicomponent","elo":1347,"win_rate":59.9,"rank":5},{"arena":"models","category":"website","elo":1317,"win_rate":55.7,"rank":6},{"arena":"agents","category":"htmlslides","elo":1189,"win_rate":39.1,"rank":9},{"arena":"agents","category":"mobileapps","elo":1231,"win_rate":54.4,"rank":13},{"arena":"agents","category":"python-pptxslides","elo":1259,"win_rate":51.6,"rank":6}],"artificial_analysis":{"intelligence_index":44.9,"coding_index":74.8,"agentic_index":53.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"qwen/qwen3.8-27b","canonical_slug":"qwen/qwen3.8-27b-20260814","hugging_face_id":"Qwen/Qwen3.8-27B","name":"Qwen: Qwen3.8 27B","created":1786722910,"description":"Qwen3.8 27B is an open-weight dense vision-language model from Qwen. It is suited for coding, professional workflows, research, multimodal interaction, and long-running agent tasks, with flexible thinking that can be...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000000214","completion":"0.00000255","input_cache_read":"0.00000015"},"top_provider":{"context_length":262144,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":20},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.8-27b-20260814/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":33.9,"coding_index":68.1,"agentic_index":46.5}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["xhigh","medium","low"],"default_effort":"xhigh"}},{"id":"dots-studio/dots-3-note-preview:free","canonical_slug":"dots-studio/dots-3-note-preview-20260813","hugging_face_id":null,"name":"Dots Studio: Dots3-Note Preview (free)","created":1786680361,"description":"Dots3-Note Preview is an open-weight mixture-of-experts model from Dots Studio, with 16B active parameters out of 280B total. It is the lightest model in the Dots 3 family and is...","context_length":512000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":512000,"max_completion_tokens":460800,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":"2026-09-30","links":{"details":"/api/v1/models/dots-studio/dots-3-note-preview-20260813/endpoints"},"reasoning":{"mandatory":false}},{"id":"google/gemini-3.7-flash","canonical_slug":"google/gemini-3.7-flash-20260813","hugging_face_id":null,"name":"Google: Gemini 3.7 Flash","created":1786640581,"description":"Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.00000375","image":"0.00000075","audio":"0.00000075","input_audio_cache":"0.000000075","web_search":"0.014","internal_reasoning":"0.00000375","input_cache_read":"0.000000075","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.7-flash-20260813/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1235,"win_rate":52.1,"rank":5},{"arena":"agents","category":"androidnative","elo":1256,"win_rate":53.6,"rank":6},{"arena":"agents","category":"fullstack","elo":1225,"win_rate":45.8,"rank":14},{"arena":"agents","category":"mobileapps","elo":1264,"win_rate":54.1,"rank":5},{"arena":"agents","category":"webapps","elo":1234,"win_rate":47.4,"rank":14},{"arena":"models","category":"3d","elo":1339,"win_rate":56.3,"rank":9},{"arena":"models","category":"asciiart","elo":1264,"win_rate":51,"rank":15},{"arena":"models","category":"codecategories","elo":1323,"win_rate":56.2,"rank":8},{"arena":"models","category":"dataviz","elo":1330,"win_rate":57.1,"rank":7},{"arena":"models","category":"gamedev","elo":1352,"win_rate":56.2,"rank":7},{"arena":"models","category":"uicomponent","elo":1316,"win_rate":51.7,"rank":12},{"arena":"models","category":"website","elo":1315,"win_rate":56.3,"rank":7}],"artificial_analysis":{"intelligence_index":39.4,"coding_index":76.1,"agentic_index":36.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"google/gemini-3.7-flash:batch","canonical_slug":"google/gemini-3.7-flash-20260813","hugging_face_id":null,"name":"Google: Gemini 3.7 Flash (batch)","created":1786640581,"description":"Gemini 3.7 Flash is a multimodal model from Google for fast agentic workflows, coding, and complex multi-step reasoning. It is designed for tasks that require responsive performance and reliable multi-step...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000000375","completion":"0.000001875","image":"0.000000375","audio":"0.000000375","input_audio_cache":"0.0000000375","web_search":"0.014","internal_reasoning":"0.000001875","input_cache_read":"0.0000000375","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.7-flash-20260813/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1235,"win_rate":52.1,"rank":5},{"arena":"agents","category":"androidnative","elo":1256,"win_rate":53.6,"rank":6},{"arena":"agents","category":"fullstack","elo":1225,"win_rate":45.8,"rank":14},{"arena":"agents","category":"mobileapps","elo":1264,"win_rate":54.1,"rank":5},{"arena":"agents","category":"webapps","elo":1234,"win_rate":47.4,"rank":14},{"arena":"models","category":"3d","elo":1339,"win_rate":56.3,"rank":9},{"arena":"models","category":"asciiart","elo":1264,"win_rate":51,"rank":15},{"arena":"models","category":"codecategories","elo":1323,"win_rate":56.2,"rank":8},{"arena":"models","category":"dataviz","elo":1330,"win_rate":57.1,"rank":7},{"arena":"models","category":"gamedev","elo":1352,"win_rate":56.2,"rank":7},{"arena":"models","category":"uicomponent","elo":1316,"win_rate":51.7,"rank":12},{"arena":"models","category":"website","elo":1315,"win_rate":56.3,"rank":7}],"artificial_analysis":{"intelligence_index":39.4,"coding_index":76.1,"agentic_index":36.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"bytedance-seed/seed-2-1-turbo","canonical_slug":"bytedance-seed/seed-2-1-turbo-20260810","hugging_face_id":null,"name":"ByteDance Seed: Seed 2.1 Turbo","created":1786552176,"description":"Seed 2.1 Turbo is a multimodal model from ByteDance Seed for coding and long-horizon agent workflows. It is suited for end-to-end software delivery, multi-step task execution, and understanding visual and...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.0000025"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/bytedance-seed/seed-2-1-turbo-20260810/endpoints"},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.8-2.4t-a95b","canonical_slug":"qwen/qwen3.8-2.4t-a95b-20260812","hugging_face_id":"Qwen/Qwen3.8-2.4T-A95B","name":"Qwen: Qwen3.8 2.4T A95B","created":1786551702,"description":"Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total. It is...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","input_cache_read":"0.00000025"},"top_provider":{"context_length":1000000,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":20},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.8-2.4t-a95b-20260812/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":40,"coding_index":71.9,"agentic_index":50.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["xhigh","medium","low"],"default_effort":"xhigh"}},{"id":"qwen/qwen3.8-2.4t-a95b:batch","canonical_slug":"qwen/qwen3.8-2.4t-a95b-20260812","hugging_face_id":"Qwen/Qwen3.8-2.4T-A95B","name":"Qwen: Qwen3.8 2.4T A95B (batch)","created":1786551702,"description":"Qwen3.8 2.4T A95B is an open-weight sparse mixture-of-experts model from Qwen and the open-weight variant of [Qwen3.8 Max](/qwen/qwen3.8-max), with 95 billion active parameters out of 2.4 trillion total. It is...","context_length":1010000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","input_cache_read":"0.00000025"},"top_provider":{"context_length":1010000,"max_completion_tokens":909000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":20},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.8-2.4t-a95b-20260812/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":40,"coding_index":71.9,"agentic_index":50.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["xhigh","medium","low"],"default_effort":"xhigh"}},{"id":"bytedance-seed/seed-2.0-code","canonical_slug":"bytedance-seed/seed-2.0-code-20260730","hugging_face_id":null,"name":"ByteDance Seed: Seed-2.0-Code","created":1786550701,"description":"Seed 2.0 Code is a model from ByteDance Seed optimized for agentic coding. It is suited for frontend development, multilingual programming tasks, and coding-agent workflows in tools such as Claude...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.000003","overrides":[{"min_prompt_tokens":128000,"prompt":"0.000001","completion":"0.000006"}]},"top_provider":{"context_length":262144,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/bytedance-seed/seed-2.0-code-20260730/endpoints"},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"deepseek/deepseek-v4-pro-0813","canonical_slug":"deepseek/deepseek-v4-pro-20260813","hugging_face_id":"deepseek-ai/DeepSeek-V4-Pro-0813","name":"DeepSeek: DeepSeek V4 Pro 0813","created":1786549364,"description":"DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.0000009834","completion":"0.0000029502","input_cache_read":"0.00000003278"},"top_provider":{"context_length":1024000,"max_completion_tokens":384000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":1},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-pro-20260813/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":36.3,"coding_index":68.8,"agentic_index":42.3}},"reasoning":{"mandatory":false,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"deepseek/deepseek-v4-pro-0813:batch","canonical_slug":"deepseek/deepseek-v4-pro-20260813","hugging_face_id":"deepseek-ai/DeepSeek-V4-Pro-0813","name":"DeepSeek: DeepSeek V4 Pro 0813 (batch)","created":1786549364,"description":"DeepSeek V4 Pro 0813 is a large-scale mixture-of-experts model from DeepSeek. This is the GA release of DeepSeek V4 Pro.","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000066","completion":"0.00000198","input_cache_read":"0.000000022"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":1},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-pro-20260813/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":36.3,"coding_index":68.8,"agentic_index":42.3}},"reasoning":{"mandatory":false,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"x-ai/grok-4.6","canonical_slug":"x-ai/grok-4.6-20260810","hugging_face_id":null,"name":"SpaceXAI: Grok 4.6","created":1786548957,"description":"Grok 4.6 is SpaceXAI's smartest model with frontier performance on coding, knowledge work, and STEM.","context_length":500000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","web_search":"0.005","input_cache_read":"0.0000005","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000004","completion":"0.000012","input_cache_read":"0.000001"}]},"top_provider":{"context_length":500000,"max_completion_tokens":450000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-4.6-20260810/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1210,"win_rate":47.2,"rank":9},{"arena":"agents","category":"androidnative","elo":1288,"win_rate":59.1,"rank":2},{"arena":"agents","category":"fullstack","elo":1273,"win_rate":55,"rank":7},{"arena":"agents","category":"htmlslides","elo":1253,"win_rate":60.3,"rank":3},{"arena":"agents","category":"mobileapps","elo":1265,"win_rate":57.4,"rank":4},{"arena":"agents","category":"python-pptxslides","elo":1241,"win_rate":52.6,"rank":11},{"arena":"agents","category":"webapps","elo":1264,"win_rate":56.5,"rank":8},{"arena":"models","category":"3d","elo":1306,"win_rate":52.8,"rank":14},{"arena":"models","category":"asciiart","elo":1296,"win_rate":58.8,"rank":7},{"arena":"models","category":"codecategories","elo":1309,"win_rate":53,"rank":11},{"arena":"models","category":"dataviz","elo":1303,"win_rate":51.9,"rank":11},{"arena":"models","category":"gamedev","elo":1322,"win_rate":54.6,"rank":11},{"arena":"models","category":"svg","elo":1268,"win_rate":51.6,"rank":11},{"arena":"models","category":"uicomponent","elo":1314,"win_rate":55.1,"rank":13},{"arena":"models","category":"website","elo":1305,"win_rate":52.9,"rank":12}],"artificial_analysis":{"intelligence_index":44.4,"coding_index":76.8,"agentic_index":53.4}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"high"}},{"id":"liquid/lfm-2.5-2.6b:free","canonical_slug":"liquid/lfm-2.5-2.6b-20260811","hugging_face_id":"LiquidAI/LFM2.5-2.6B","name":"LiquidAI: LFM2.5-2.6B (free)","created":1786470519,"description":"LFM2.5-2.6B is a compact reasoning model from Liquid AI. It is suited for agent workflows, data extraction, RAG, and long-context processing. Liquid advises against using it for agentic coding or...","context_length":65536,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":65536,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_completion_tokens","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs"],"default_parameters":{"temperature":0.1,"top_k":50,"repetition_penalty":1.1},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/liquid/lfm-2.5-2.6b-20260811/endpoints"},"reasoning":{"mandatory":true}},{"id":"nvidia/nemotron-3.5-lightning","canonical_slug":"nvidia/nemotron-3.5-lightning-20260807","hugging_face_id":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","name":"NVIDIA: Nemotron 3.5 Lightning","created":1786452751,"description":"NVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000008","completion":"0.0000002","input_cache_read":"0.00000004"},"top_provider":{"context_length":262144,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3.5-lightning-20260807/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":13.6,"coding_index":26.8,"agentic_index":6.1}},"reasoning":{"mandatory":false}},{"id":"nvidia/nemotron-3.5-lightning:free","canonical_slug":"nvidia/nemotron-3.5-lightning-20260807","hugging_face_id":"nvidia/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-BF16","name":"NVIDIA: Nemotron 3.5 Lightning (free)","created":1786452751,"description":"NVIDIA Nemotron 3.5 Lightning is an open mixture-of-experts model from NVIDIA, with 3B active parameters out of 30B total. It is suited for high-throughput agentic workloads and specialized tasks that...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","seed","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3.5-lightning-20260807/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":13.6,"coding_index":26.8,"agentic_index":6.1}},"reasoning":{"mandatory":false}},{"id":"sakana/sakana-namazu","canonical_slug":"sakana/namazu-20260811","hugging_face_id":null,"name":"Sakana: Sakana Namazu","created":1786410129,"description":"Sakana Namazu is a Japanese-specialized reasoning model from Sakana AI, based on Kimi K2.6 with additional training for Japanese language and business contexts. It is suited for Japanese instruction following,...","context_length":262144,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000095","completion":"0.000004","web_search":"0.007","input_cache_read":"0.00000015"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","reasoning","reasoning_effort","structured_outputs","tool_choice","tools","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/sakana/namazu-20260811/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","none"],"default_effort":"high"}},{"id":"upstage/solar-pro4","canonical_slug":"upstage/solar-pro4-20260810","hugging_face_id":null,"name":"Upstage: Solar Pro 4","created":1786371636,"description":"Solar Pro 4 is Upstage's cost-efficient large language model, featuring a 524K context window. It is built for long-horizon tasks and agentic workflows, with strong capabilities in office productivity, document-intensive...","context_length":524288,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000009","completion":"0.00000036","input_cache_read":"0.000000018"},"top_provider":{"context_length":524288,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","parallel_tool_calls","presence_penalty","reasoning","response_format","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/upstage/solar-pro4-20260810/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"webapps","elo":1121,"win_rate":30.2,"rank":33},{"arena":"models","category":"3d","elo":1207,"win_rate":46.4,"rank":46},{"arena":"models","category":"codecategories","elo":1197,"win_rate":39,"rank":54},{"arena":"models","category":"dataviz","elo":1196,"win_rate":41.5,"rank":56},{"arena":"models","category":"gamedev","elo":1194,"win_rate":39.9,"rank":54},{"arena":"models","category":"uicomponent","elo":1166,"win_rate":35.5,"rank":66},{"arena":"models","category":"website","elo":1188,"win_rate":37.4,"rank":65}],"artificial_analysis":{"intelligence_index":null,"coding_index":52.7,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"meta/muse-glimmer-30b","canonical_slug":"meta/muse-glimmer-30b-20260810","hugging_face_id":"meta-models/Muse-Glimmer-30B","name":"Meta: Muse Glimmer 30B","created":1786302394,"description":"Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000035","completion":"0.0000015","input_cache_read":"0.00000004"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":64},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-glimmer-30b-20260810/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"meta/muse-glimmer-30b:batch","canonical_slug":"meta/muse-glimmer-30b-20260810","hugging_face_id":"meta-models/Muse-Glimmer-30B","name":"Meta: Muse Glimmer 30B (batch)","created":1786302394,"description":"Muse Glimmer 30B is a dense, open-weight multimodal model from Meta Superintelligence Labs, distilled from Muse Spark and optimized for autonomous agents on consumer hardware. It is suited for long-horizon...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000175","completion":"0.00000075","input_cache_read":"0.00000002"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":64},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-glimmer-30b-20260810/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"meta/muse-spark-1.2","canonical_slug":"meta/muse-spark-1.2-20260805","hugging_face_id":null,"name":"Meta: Muse Spark 1.2","created":1785959287,"description":"Muse Spark 1.2 is a reasoning model from Meta, designed for complex agentic tasks. It accepts text, images, video, audio, and PDF documents, returns text, and offers a 1M-token context...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00000425","web_search":"0.0025","input_cache_read":"0.00000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","repetition_penalty","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-spark-1.2-20260805/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1179,"win_rate":42.7,"rank":15},{"arena":"agents","category":"androidnative","elo":1238,"win_rate":53.5,"rank":10},{"arena":"agents","category":"fullstack","elo":1221,"win_rate":49,"rank":16},{"arena":"agents","category":"htmlslides","elo":1137,"win_rate":40,"rank":20},{"arena":"agents","category":"mobileapps","elo":1194,"win_rate":46.8,"rank":19},{"arena":"agents","category":"python-pptxslides","elo":1198,"win_rate":48.6,"rank":15},{"arena":"agents","category":"webapps","elo":1240,"win_rate":52.1,"rank":12},{"arena":"models","category":"3d","elo":1331,"win_rate":54.1,"rank":11},{"arena":"models","category":"asciiart","elo":1319,"win_rate":58.6,"rank":4},{"arena":"models","category":"codecategories","elo":1326,"win_rate":53.8,"rank":6},{"arena":"models","category":"dataviz","elo":1364,"win_rate":61.6,"rank":4},{"arena":"models","category":"gamedev","elo":1324,"win_rate":51.8,"rank":10},{"arena":"models","category":"svg","elo":1312,"win_rate":56.1,"rank":7},{"arena":"models","category":"uicomponent","elo":1326,"win_rate":53.2,"rank":10},{"arena":"models","category":"website","elo":1322,"win_rate":53.7,"rank":4}],"artificial_analysis":{"intelligence_index":39.8,"coding_index":72.2,"agentic_index":44}},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low","minimal"],"default_effort":"medium"}},{"id":"~deepseek/deepseek-v4-flash-latest","canonical_slug":"~deepseek/deepseek-v4-flash-latest","alias_target":{"name":"DeepSeek: DeepSeek V4 Flash 0731","slug":"deepseek/deepseek-v4-flash-0731"},"hugging_face_id":null,"name":"DeepSeek: DeepSeek V4 Flash Latest","created":1785606009,"description":"This model always redirects to the latest model in the DeepSeek V4 Flash family.","context_length":1310720,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00000004","completion":"0.0000001","input_cache_read":"0.00000001"},"top_provider":{"context_length":1048576,"max_completion_tokens":393216,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":[],"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~deepseek/deepseek-v4-flash-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"deepseek/deepseek-v4-flash-0731","canonical_slug":"deepseek/deepseek-v4-flash-20260731","hugging_face_id":"deepseek-ai/DeepSeek-V4-Flash-0731","name":"DeepSeek: DeepSeek V4 Flash 0731","created":1785478908,"description":"DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....","context_length":1310720,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000006","completion":"0.00000012","input_cache_read":"0.000000012"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":[],"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-flash-20260731/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1237,"win_rate":48.6,"rank":36},{"arena":"models","category":"asciiart","elo":1110,"win_rate":33.3,"rank":58},{"arena":"models","category":"codecategories","elo":1245,"win_rate":46.5,"rank":40},{"arena":"models","category":"dataviz","elo":1198,"win_rate":41.2,"rank":53},{"arena":"models","category":"gamedev","elo":1233,"win_rate":44.7,"rank":38},{"arena":"models","category":"svg","elo":1213,"win_rate":45.1,"rank":25},{"arena":"models","category":"uicomponent","elo":1253,"win_rate":47,"rank":35},{"arena":"models","category":"website","elo":1251,"win_rate":46.8,"rank":38}],"artificial_analysis":{"intelligence_index":34.5,"coding_index":69.1,"agentic_index":41.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"deepseek/deepseek-v4-flash-0731:batch","canonical_slug":"deepseek/deepseek-v4-flash-20260731","hugging_face_id":"deepseek-ai/DeepSeek-V4-Flash-0731","name":"DeepSeek: DeepSeek V4 Flash 0731 (batch)","created":1785478908,"description":"DeepSeek V4 Flash 0731 is a sparse mixture-of-experts model from DeepSeek, with 13B active parameters out of 284B total. This re-post-trained revision is suited for coding, reasoning, and agent workflows....","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000011","completion":"0.00000033","input_cache_read":"0.0000000035"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":[],"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-flash-20260731/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1237,"win_rate":48.6,"rank":36},{"arena":"models","category":"asciiart","elo":1110,"win_rate":33.3,"rank":58},{"arena":"models","category":"codecategories","elo":1245,"win_rate":46.5,"rank":40},{"arena":"models","category":"dataviz","elo":1198,"win_rate":41.2,"rank":53},{"arena":"models","category":"gamedev","elo":1233,"win_rate":44.7,"rank":38},{"arena":"models","category":"svg","elo":1213,"win_rate":45.1,"rank":25},{"arena":"models","category":"uicomponent","elo":1253,"win_rate":47,"rank":35},{"arena":"models","category":"website","elo":1251,"win_rate":46.8,"rank":38}],"artificial_analysis":{"intelligence_index":34.5,"coding_index":69.1,"agentic_index":41.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"high"}},{"id":"thinkingmachines/inkling-small","canonical_slug":"thinkingmachines/inkling-small-20260730","hugging_face_id":"thinkingmachines/Inkling-Small","name":"Thinking Machines: Inkling Small","created":1785443117,"description":"Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...","context_length":1048576,"architecture":{"modality":"text+image+audio->text","input_modalities":["text","image","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000045","completion":"0.0000012","input_cache_read":"0.0000001"},"top_provider":{"context_length":524288,"max_completion_tokens":262144,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/thinkingmachines/inkling-small-20260730/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":26.1,"coding_index":52.9,"agentic_index":25}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","medium","low","minimal","none"],"default_effort":"high"}},{"id":"thinkingmachines/inkling-small:free","canonical_slug":"thinkingmachines/inkling-small-20260730","hugging_face_id":"thinkingmachines/Inkling-Small","name":"Thinking Machines: Inkling Small (free)","created":1785443117,"description":"Inkling Small is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 12B active parameters out of 276B total. It is positioned as the smaller, more efficient member of...","context_length":1048576,"architecture":{"modality":"text+image+audio->text","input_modalities":["text","image","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":1048576,"max_completion_tokens":262144,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","reasoning_effort","seed","stop","temperature","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/thinkingmachines/inkling-small-20260730/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":26.1,"coding_index":52.9,"agentic_index":25}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","medium","low","minimal","none"],"default_effort":"high"}},{"id":"qwen/qwen3.7-flash","canonical_slug":"qwen/qwen3.7-flash-20260727","hugging_face_id":null,"name":"Qwen: Qwen3.7 Flash","created":1785190561,"description":"Qwen3.7 Flash is a vision-language reasoning model from Alibaba. It is suited for multimodal agents, visual coding, search, and computer interaction, with strengths in object recognition, spatial understanding, and real-world...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.00000003","completion":"0.00000013","input_cache_read":"0.000000006","input_cache_write":"0.000000038","overrides":[{"min_prompt_tokens":32000,"prompt":"0.0000001","completion":"0.0000004","input_cache_read":"0.00000002","input_cache_write":"0.000000125"},{"min_prompt_tokens":256000,"prompt":"0.0000002","completion":"0.0000008","input_cache_read":"0.00000004","input_cache_write":"0.00000025"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.7-flash-20260727/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true}},{"id":"anthropic/claude-opus-5","canonical_slug":"anthropic/claude-opus-5-20260723","hugging_face_id":null,"name":"Anthropic: Claude Opus 5","created":1784912544,"description":"Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It is particularly strong at end-to-end software tasks, code review and bug finding, visual analysis...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-opus-5-20260723/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1261,"win_rate":53.8,"rank":2},{"arena":"agents","category":"androidnative","elo":1267,"win_rate":55.2,"rank":3},{"arena":"agents","category":"fullstack","elo":1315,"win_rate":58.4,"rank":4},{"arena":"agents","category":"mobileapps","elo":1348,"win_rate":67.1,"rank":1},{"arena":"agents","category":"python-pptxslides","elo":1265,"win_rate":54.1,"rank":5},{"arena":"agents","category":"webapps","elo":1278,"win_rate":54.8,"rank":5},{"arena":"models","category":"3d","elo":1363,"win_rate":61.7,"rank":5},{"arena":"models","category":"asciiart","elo":1390,"win_rate":70.9,"rank":1},{"arena":"models","category":"codecategories","elo":1338,"win_rate":58.2,"rank":4},{"arena":"models","category":"dataviz","elo":1355,"win_rate":61.2,"rank":5},{"arena":"models","category":"gamedev","elo":1364,"win_rate":59.5,"rank":5},{"arena":"models","category":"svg","elo":1350,"win_rate":62.1,"rank":2},{"arena":"models","category":"uicomponent","elo":1360,"win_rate":61.3,"rank":3},{"arena":"models","category":"website","elo":1319,"win_rate":56.3,"rank":5}],"artificial_analysis":{"intelligence_index":50.7,"coding_index":78,"agentic_index":56.2}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-opus-5:batch","canonical_slug":"anthropic/claude-opus-5-20260723","hugging_face_id":null,"name":"Anthropic: Claude Opus 5 (batch)","created":1784912544,"description":"Claude Opus 5 is Anthropic’s flagship model for demanding reasoning, coding, and long-horizon agentic work. It is particularly strong at end-to-end software tasks, code review and bug finding, visual analysis...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.0000125","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.000003125","input_cache_write_1h":"0.000005"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-opus-5-20260723/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1261,"win_rate":53.8,"rank":2},{"arena":"agents","category":"androidnative","elo":1267,"win_rate":55.2,"rank":3},{"arena":"agents","category":"fullstack","elo":1315,"win_rate":58.4,"rank":4},{"arena":"agents","category":"mobileapps","elo":1348,"win_rate":67.1,"rank":1},{"arena":"agents","category":"python-pptxslides","elo":1265,"win_rate":54.1,"rank":5},{"arena":"agents","category":"webapps","elo":1278,"win_rate":54.8,"rank":5},{"arena":"models","category":"3d","elo":1363,"win_rate":61.7,"rank":5},{"arena":"models","category":"asciiart","elo":1390,"win_rate":70.9,"rank":1},{"arena":"models","category":"codecategories","elo":1338,"win_rate":58.2,"rank":4},{"arena":"models","category":"dataviz","elo":1355,"win_rate":61.2,"rank":5},{"arena":"models","category":"gamedev","elo":1364,"win_rate":59.5,"rank":5},{"arena":"models","category":"svg","elo":1350,"win_rate":62.1,"rank":2},{"arena":"models","category":"uicomponent","elo":1360,"win_rate":61.3,"rank":3},{"arena":"models","category":"website","elo":1319,"win_rate":56.3,"rank":5}],"artificial_analysis":{"intelligence_index":50.7,"coding_index":78,"agentic_index":56.2}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"inclusionai/ling-3.0-flash","canonical_slug":"inclusionai/ling-3.0-flash-20260723","hugging_face_id":"inclusionAI/Ling-3.0-flash","name":"inclusionAI: Ling 3.0 Flash","created":1784818580,"description":"*Ling-3.0-flash* is a *124B-parameter Mixture-of-Experts (MoE) model*, with approximately *5.1B parameters activated per token*. The model is designed with *token efficiency and production-scale agentic inference* as key priorities, enabling developers...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000021","completion":"0.000000063","input_cache_read":"0.0000000042"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inclusionai/ling-3.0-flash-20260723/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":20.6,"coding_index":50.6,"agentic_index":21}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"poolside/laguna-s-2.1","canonical_slug":"poolside/laguna-s-2.1-20260720","hugging_face_id":"poolside/Laguna-S-2.1","name":"Poolside: Laguna S 2.1","created":1784652683,"description":"Laguna S 2.1 is the latest coding agent model from [Poolside](). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000009","completion":"0.00000018","input_cache_read":"0.000000009"},"top_provider":{"context_length":1048576,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","temperature","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/poolside/laguna-s-2.1-20260720/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"poolside/laguna-s-2.1:free","canonical_slug":"poolside/laguna-s-2.1-20260720","hugging_face_id":"poolside/Laguna-S-2.1","name":"Poolside: Laguna S 2.1 (free)","created":1784652683,"description":"Laguna S 2.1 is the latest coding agent model from [Poolside](). Laguna S 2.1 is a 118B total parameter model with 8B active parameters, scoring 70.2% on Terminal-Bench 2.1 and...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","temperature","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/poolside/laguna-s-2.1-20260720/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"google/gemini-3.6-flash","canonical_slug":"google/gemini-3.6-flash-20260721","hugging_face_id":null,"name":"Google: Gemini 3.6 Flash","created":1784646733,"description":"Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.00000375","image":"0.00000075","audio":"0.00000075","input_audio_cache":"0.000000075","web_search":"0.014","internal_reasoning":"0.00000375","input_cache_read":"0.000000075","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.6-flash-20260721/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1180,"win_rate":47.8,"rank":13},{"arena":"agents","category":"androidnative","elo":1213,"win_rate":53.4,"rank":12},{"arena":"agents","category":"fullstack","elo":1191,"win_rate":45.1,"rank":21},{"arena":"agents","category":"htmlslides","elo":1152,"win_rate":41.9,"rank":17},{"arena":"agents","category":"mobileapps","elo":1229,"win_rate":51.4,"rank":14},{"arena":"agents","category":"python-pptxslides","elo":1146,"win_rate":38.8,"rank":24},{"arena":"agents","category":"webapps","elo":1213,"win_rate":46.4,"rank":21},{"arena":"models","category":"3d","elo":1302,"win_rate":53.2,"rank":16},{"arena":"models","category":"asciiart","elo":1302,"win_rate":56.8,"rank":5},{"arena":"models","category":"codecategories","elo":1305,"win_rate":53.7,"rank":13},{"arena":"models","category":"dataviz","elo":1312,"win_rate":53.5,"rank":9},{"arena":"models","category":"gamedev","elo":1284,"win_rate":51.1,"rank":24},{"arena":"models","category":"uicomponent","elo":1318,"win_rate":55.3,"rank":11},{"arena":"models","category":"website","elo":1313,"win_rate":55.7,"rank":9}],"artificial_analysis":{"intelligence_index":34.3,"coding_index":69.2,"agentic_index":30.2}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"google/gemini-3.6-flash:batch","canonical_slug":"google/gemini-3.6-flash-20260721","hugging_face_id":null,"name":"Google: Gemini 3.6 Flash (batch)","created":1784646733,"description":"Gemini 3.6 Flash is a high-efficiency model from Google for coding, agentic workflows, and web and app development. It is designed to produce polished outputs with fewer unnecessary edits and...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000000375","completion":"0.000001875","image":"0.000000375","audio":"0.000000375","input_audio_cache":"0.0000000375","web_search":"0.014","internal_reasoning":"0.000001875","input_cache_read":"0.0000000375","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.6-flash-20260721/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1180,"win_rate":47.8,"rank":13},{"arena":"agents","category":"androidnative","elo":1213,"win_rate":53.4,"rank":12},{"arena":"agents","category":"fullstack","elo":1191,"win_rate":45.1,"rank":21},{"arena":"agents","category":"htmlslides","elo":1152,"win_rate":41.9,"rank":17},{"arena":"agents","category":"mobileapps","elo":1229,"win_rate":51.4,"rank":14},{"arena":"agents","category":"python-pptxslides","elo":1146,"win_rate":38.8,"rank":24},{"arena":"agents","category":"webapps","elo":1213,"win_rate":46.4,"rank":21},{"arena":"models","category":"3d","elo":1302,"win_rate":53.2,"rank":16},{"arena":"models","category":"asciiart","elo":1302,"win_rate":56.8,"rank":5},{"arena":"models","category":"codecategories","elo":1305,"win_rate":53.7,"rank":13},{"arena":"models","category":"dataviz","elo":1312,"win_rate":53.5,"rank":9},{"arena":"models","category":"gamedev","elo":1284,"win_rate":51.1,"rank":24},{"arena":"models","category":"uicomponent","elo":1318,"win_rate":55.3,"rank":11},{"arena":"models","category":"website","elo":1313,"win_rate":55.7,"rank":9}],"artificial_analysis":{"intelligence_index":34.3,"coding_index":69.2,"agentic_index":30.2}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"google/gemini-3.5-flash-lite","canonical_slug":"google/gemini-3.5-flash-lite-20260721","hugging_face_id":null,"name":"Google: Gemini 3.5 Flash Lite","created":1784646726,"description":"Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000025","image":"0.0000003","audio":"0.0000003","input_audio_cache":"0.00000003","web_search":"0.014","internal_reasoning":"0.0000025","input_cache_read":"0.00000003","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.5-flash-lite-20260721/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":22.7,"coding_index":49.3,"agentic_index":15.9}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"minimal"}},{"id":"google/gemini-3.5-flash-lite:batch","canonical_slug":"google/gemini-3.5-flash-lite-20260721","hugging_face_id":null,"name":"Google: Gemini 3.5 Flash Lite (batch)","created":1784646726,"description":"Gemini 3.5 Flash Lite is a high-efficiency model from Google with upgraded agentic capabilities. It is suited for subagents that execute focused tasks within complex, multi-agent workflows.","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.00000125","image":"0.00000015","audio":"0.00000015","input_audio_cache":"0.000000015","web_search":"0.014","internal_reasoning":"0.00000125","input_cache_read":"0.000000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.5-flash-lite-20260721/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":22.7,"coding_index":49.3,"agentic_index":15.9}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"minimal"}},{"id":"meituan/longcat-2.0","canonical_slug":"meituan/longcat-2.0-20260720","hugging_face_id":"meituan-longcat/LongCat-2.0","name":"Meituan: LongCat 2.0","created":1784554658,"description":"LongCat 2.0 is a sparse mixture-of-experts language model from Meituan, with 48B active parameters out of 1.6T total. It is suited for coding, repository-level changes, long-horizon problem solving, and agentic...","context_length":1048756,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.000000006"},"top_provider":{"context_length":1048756,"max_completion_tokens":262144,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meituan/longcat-2.0-20260720/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":19.7,"coding_index":45.3,"agentic_index":15.9}},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true}},{"id":"thinkingmachines/inkling","canonical_slug":"thinkingmachines/inkling-20260715","hugging_face_id":"thinkingmachines/Inkling","name":"Thinking Machines: Inkling","created":1784325956,"description":"Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning, coding, agentic and tool-use systems,...","context_length":1048576,"architecture":{"modality":"text+image+audio->text","input_modalities":["text","image","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.00000405","input_cache_read":"0.00000017"},"top_provider":{"context_length":524288,"max_completion_tokens":471859,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/thinkingmachines/inkling-20260715/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1186,"win_rate":41.3,"rank":54},{"arena":"models","category":"asciiart","elo":1119,"win_rate":34.6,"rank":55},{"arena":"models","category":"codecategories","elo":1216,"win_rate":42.1,"rank":48},{"arena":"models","category":"dataviz","elo":1191,"win_rate":40.1,"rank":57},{"arena":"models","category":"gamedev","elo":1177,"win_rate":37.1,"rank":59},{"arena":"models","category":"svg","elo":1134,"win_rate":34.8,"rank":52},{"arena":"models","category":"uicomponent","elo":1212,"win_rate":42.1,"rank":48},{"arena":"models","category":"website","elo":1229,"win_rate":43.7,"rank":47}],"artificial_analysis":{"intelligence_index":25.5,"coding_index":52.1,"agentic_index":24.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","medium","low","minimal","none"],"default_effort":"high"}},{"id":"thinkingmachines/inkling:batch","canonical_slug":"thinkingmachines/inkling-20260715","hugging_face_id":"thinkingmachines/Inkling","name":"Thinking Machines: Inkling (batch)","created":1784325956,"description":"Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning, coding, agentic and tool-use systems,...","context_length":524288,"architecture":{"modality":"text+image+audio->text","input_modalities":["text","image","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.00000405","input_cache_read":"0.00000017"},"top_provider":{"context_length":524288,"max_completion_tokens":471859,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/thinkingmachines/inkling-20260715/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1186,"win_rate":41.3,"rank":54},{"arena":"models","category":"asciiart","elo":1119,"win_rate":34.6,"rank":55},{"arena":"models","category":"codecategories","elo":1216,"win_rate":42.1,"rank":48},{"arena":"models","category":"dataviz","elo":1191,"win_rate":40.1,"rank":57},{"arena":"models","category":"gamedev","elo":1177,"win_rate":37.1,"rank":59},{"arena":"models","category":"svg","elo":1134,"win_rate":34.8,"rank":52},{"arena":"models","category":"uicomponent","elo":1212,"win_rate":42.1,"rank":48},{"arena":"models","category":"website","elo":1229,"win_rate":43.7,"rank":47}],"artificial_analysis":{"intelligence_index":25.5,"coding_index":52.1,"agentic_index":24.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","medium","low","minimal","none"],"default_effort":"high"}},{"id":"thinkingmachines/inkling:free","canonical_slug":"thinkingmachines/inkling-20260715","hugging_face_id":"thinkingmachines/Inkling","name":"Thinking Machines: Inkling (free)","created":1784325956,"description":"Inkling is an open-weight multimodal mixture-of-experts model from Thinking Machines Lab, with 41B active parameters out of 975B total. It is designed for general-purpose reasoning, coding, agentic and tool-use systems,...","context_length":1048576,"architecture":{"modality":"text+image+audio->text","input_modalities":["text","image","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":1048576,"max_completion_tokens":262144,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","reasoning_effort","seed","stop","temperature","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/thinkingmachines/inkling-20260715/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1186,"win_rate":41.3,"rank":54},{"arena":"models","category":"asciiart","elo":1119,"win_rate":34.6,"rank":55},{"arena":"models","category":"codecategories","elo":1216,"win_rate":42.1,"rank":48},{"arena":"models","category":"dataviz","elo":1191,"win_rate":40.1,"rank":57},{"arena":"models","category":"gamedev","elo":1177,"win_rate":37.1,"rank":59},{"arena":"models","category":"svg","elo":1134,"win_rate":34.8,"rank":52},{"arena":"models","category":"uicomponent","elo":1212,"win_rate":42.1,"rank":48},{"arena":"models","category":"website","elo":1229,"win_rate":43.7,"rank":47}],"artificial_analysis":{"intelligence_index":25.5,"coding_index":52.1,"agentic_index":24.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","medium","low","minimal","none"],"default_effort":"high"}},{"id":"openrouter/auto-beta","canonical_slug":"openrouter/auto-beta","hugging_face_id":null,"name":"Auto Router (Beta)","created":1784311165,"description":"Auto Router (Beta) is a task-aware router from OpenRouter. It classifies each request, then routes it the [most popular model](/rankings#task-spend) for that task based on aggregate spend, filtered by your...","context_length":2000000,"architecture":{"modality":"text+image+file+audio+video->text+image","input_modalities":["text","image","audio","file","video"],"output_modalities":["text","image"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"-1","completion":"-1"},"top_provider":{"context_length":null,"max_completion_tokens":null,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","prediction","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p","web_search_options"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openrouter/auto-beta/endpoints"}},{"id":"moonshotai/kimi-k3","canonical_slug":"moonshotai/kimi-k3-20260715","hugging_face_id":"moonshotai/Kimi-K3","name":"MoonshotAI: Kimi K3","created":1784215858,"description":"Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...","context_length":1048576,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000002648138063","completion":"0.00001328272425","input_cache_read":"0.00000030264435"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k3-20260715/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1252,"win_rate":54.9,"rank":3},{"arena":"agents","category":"androidnative","elo":1258,"win_rate":55,"rank":5},{"arena":"agents","category":"fullstack","elo":1332,"win_rate":63.6,"rank":2},{"arena":"agents","category":"godotgamedev","elo":1199,"win_rate":48.5,"rank":10},{"arena":"agents","category":"htmlslides","elo":1259,"win_rate":59.6,"rank":2},{"arena":"agents","category":"mobileapps","elo":1279,"win_rate":57.8,"rank":3},{"arena":"agents","category":"python-pptxslides","elo":1273,"win_rate":59.2,"rank":4},{"arena":"agents","category":"webapps","elo":1309,"win_rate":59.9,"rank":4},{"arena":"models","category":"3d","elo":1425,"win_rate":68.9,"rank":2},{"arena":"models","category":"codecategories","elo":1388,"win_rate":64.7,"rank":1},{"arena":"models","category":"dataviz","elo":1365,"win_rate":64.1,"rank":3},{"arena":"models","category":"gamedev","elo":1400,"win_rate":62.6,"rank":2},{"arena":"models","category":"svg","elo":1335,"win_rate":63,"rank":3},{"arena":"models","category":"uicomponent","elo":1369,"win_rate":63,"rank":2},{"arena":"models","category":"website","elo":1353,"win_rate":60.9,"rank":2}],"artificial_analysis":{"intelligence_index":43.8,"coding_index":76.2,"agentic_index":50.6}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"moonshotai/kimi-k3:batch","canonical_slug":"moonshotai/kimi-k3-20260715","hugging_face_id":"moonshotai/Kimi-K3","name":"MoonshotAI: Kimi K3 (batch)","created":1784215858,"description":"Kimi K3 is a 2.8T parameter open-weight multimodal reasoning model from Moonshot AI. It is suited for complex coding, knowledge work, and long-horizon agentic workflows, and is particularly strong at...","context_length":1048576,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000015","input_cache_read":"0.0000003"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k3-20260715/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1252,"win_rate":54.9,"rank":3},{"arena":"agents","category":"androidnative","elo":1258,"win_rate":55,"rank":5},{"arena":"agents","category":"fullstack","elo":1332,"win_rate":63.6,"rank":2},{"arena":"agents","category":"godotgamedev","elo":1199,"win_rate":48.5,"rank":10},{"arena":"agents","category":"htmlslides","elo":1259,"win_rate":59.6,"rank":2},{"arena":"agents","category":"mobileapps","elo":1279,"win_rate":57.8,"rank":3},{"arena":"agents","category":"python-pptxslides","elo":1273,"win_rate":59.2,"rank":4},{"arena":"agents","category":"webapps","elo":1309,"win_rate":59.9,"rank":4},{"arena":"models","category":"3d","elo":1425,"win_rate":68.9,"rank":2},{"arena":"models","category":"codecategories","elo":1388,"win_rate":64.7,"rank":1},{"arena":"models","category":"dataviz","elo":1365,"win_rate":64.1,"rank":3},{"arena":"models","category":"gamedev","elo":1400,"win_rate":62.6,"rank":2},{"arena":"models","category":"svg","elo":1335,"win_rate":63,"rank":3},{"arena":"models","category":"uicomponent","elo":1369,"win_rate":63,"rank":2},{"arena":"models","category":"website","elo":1353,"win_rate":60.9,"rank":2}],"artificial_analysis":{"intelligence_index":43.8,"coding_index":76.2,"agentic_index":50.6}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"meta/muse-spark-1.1","canonical_slug":"meta/muse-spark-1.1-20260709","hugging_face_id":null,"name":"Meta: Muse Spark 1.1","created":1784215741,"description":"Muse Spark 1.1 is a multimodal reasoning model from Meta, built for agentic tasks. It accepts text, images, video, audio, and PDF documents and returns text, with a 1M-token context...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00000425","web_search":"0.0025","input_cache_read":"0.00000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","repetition_penalty","response_format","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/meta/muse-spark-1.1-20260709/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1183,"win_rate":48.3,"rank":11},{"arena":"agents","category":"androidnative","elo":1188,"win_rate":47.4,"rank":18},{"arena":"agents","category":"fullstack","elo":1212,"win_rate":47,"rank":17},{"arena":"agents","category":"godotgamedev","elo":1141,"win_rate":39.4,"rank":19},{"arena":"agents","category":"htmlslides","elo":1197,"win_rate":51.8,"rank":8},{"arena":"agents","category":"mobileapps","elo":1202,"win_rate":46.9,"rank":17},{"arena":"agents","category":"python-pptxslides","elo":1156,"win_rate":44.1,"rank":21},{"arena":"agents","category":"webapps","elo":1217,"win_rate":49.4,"rank":19},{"arena":"models","category":"3d","elo":1293,"win_rate":51.6,"rank":19},{"arena":"models","category":"asciiart","elo":1301,"win_rate":57.6,"rank":6},{"arena":"models","category":"codecategories","elo":1292,"win_rate":51.9,"rank":18},{"arena":"models","category":"dataviz","elo":1292,"win_rate":52,"rank":15},{"arena":"models","category":"gamedev","elo":1301,"win_rate":49.1,"rank":16},{"arena":"models","category":"svg","elo":1275,"win_rate":51.6,"rank":10},{"arena":"models","category":"uicomponent","elo":1308,"win_rate":51.7,"rank":15},{"arena":"models","category":"website","elo":1282,"win_rate":51.9,"rank":22}],"artificial_analysis":{"intelligence_index":34.3,"coding_index":71.3,"agentic_index":27.5}},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low","minimal"],"default_effort":"medium"}},{"id":"kwaipilot/kat-coder-pro-v2.5","canonical_slug":"kwaipilot/kat-coder-pro-v2.5-20260710","hugging_face_id":null,"name":"Kwaipilot: KAT-Coder-Pro V2.5","created":1783714589,"description":"KAT-Coder-Pro V2.5 is a flagship-level Agentic Coding model that can directly hand over an entire issue or an entire business workflow to it, allowing it to autonomously locate and make...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000074","completion":"0.00000296","input_cache_read":"0.00000015"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/kwaipilot/kat-coder-pro-v2.5-20260710/endpoints"}},{"id":"openai/gpt-5.6-luna-pro","canonical_slug":"openai/gpt-5.6-luna-pro-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Luna Pro","created":1783590867,"description":"GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000012","web_search":"0.01","input_cache_read":"0.00000002","input_cache_write":"0.00000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.0000004","completion":"0.0000018","input_cache_read":"0.00000004","input_cache_write":"0.0000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-luna-pro-20260709/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-luna-pro:batch","canonical_slug":"openai/gpt-5.6-luna-pro-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Luna Pro (batch)","created":1783590867,"description":"GPT-5.6 Luna Pro is the same underlying model as [GPT-5.6 Luna](https://openrouter.ai/openai/gpt-5.6-luna), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000006","web_search":"0.01","input_cache_read":"0.00000001","overrides":[{"min_prompt_tokens":272000,"prompt":"0.0000002","completion":"0.0000009","input_cache_read":"0.00000002"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-luna-pro-20260709/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-luna","canonical_slug":"openai/gpt-5.6-luna-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Luna","created":1783590864,"description":"GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000012","web_search":"0.01","input_cache_read":"0.00000002","input_cache_write":"0.00000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.0000004","completion":"0.0000018","input_cache_read":"0.00000004","input_cache_write":"0.0000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-luna-20260709/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":37.5,"coding_index":71.4,"agentic_index":42.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-luna:batch","canonical_slug":"openai/gpt-5.6-luna-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Luna (batch)","created":1783590864,"description":"GPT-5.6 Luna is a fast, cost-efficient model in OpenAI's GPT-5.6 series. It is suited for high-volume, latency-sensitive tasks such as chat, classification, and lightweight agentic workflows, providing capable reasoning for...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000006","web_search":"0.01","input_cache_read":"0.00000001","overrides":[{"min_prompt_tokens":272000,"prompt":"0.0000002","completion":"0.0000009","input_cache_read":"0.00000002"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-luna-20260709/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":37.5,"coding_index":71.4,"agentic_index":42.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-terra-pro","canonical_slug":"openai/gpt-5.6-terra-pro-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Terra Pro","created":1783590861,"description":"GPT-5.6 Terra Pro is the same underlying model as [GPT-5.6 Terra](https://openrouter.ai/openai/gpt-5.6-terra), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000004","completion":"0.000018","input_cache_read":"0.0000004","input_cache_write":"0.000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-terra-pro-20260709/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-terra-pro:batch","canonical_slug":"openai/gpt-5.6-terra-pro-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Terra Pro (batch)","created":1783590861,"description":"GPT-5.6 Terra Pro is the same underlying model as [GPT-5.6 Terra](https://openrouter.ai/openai/gpt-5.6-terra), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000006","web_search":"0.01","input_cache_read":"0.0000001","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000002","completion":"0.000009","input_cache_read":"0.0000002"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-terra-pro-20260709/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-terra","canonical_slug":"openai/gpt-5.6-terra-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Terra","created":1783590857,"description":"GPT-5.6 Terra is a balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is suited for everyday coding, reasoning, and agentic...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000004","completion":"0.000018","input_cache_read":"0.0000004","input_cache_write":"0.000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-terra-20260709/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":42.3,"coding_index":76.7,"agentic_index":43.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-terra:batch","canonical_slug":"openai/gpt-5.6-terra-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Terra (batch)","created":1783590857,"description":"GPT-5.6 Terra is a balanced model in OpenAI's GPT-5.6 series, positioned between the flagship Sol tier and the cost-efficient Luna tier. It is suited for everyday coding, reasoning, and agentic...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000006","web_search":"0.01","input_cache_read":"0.0000001","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000002","completion":"0.000009","input_cache_read":"0.0000002"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-terra-20260709/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":42.3,"coding_index":76.7,"agentic_index":43.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-sol-pro","canonical_slug":"openai/gpt-5.6-sol-pro-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Sol Pro","created":1783590854,"description":"GPT-5.6 Sol Pro is the same underlying model as [GPT-5.6 Sol](https://openrouter.ai/openai/gpt-5.6-sol), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.00001","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000004","completion":"0.000015","input_cache_read":"0.0000004","input_cache_write":"0.000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-sol-pro-20260709/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-sol-pro:batch","canonical_slug":"openai/gpt-5.6-sol-pro-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Sol Pro (batch)","created":1783590854,"description":"GPT-5.6 Sol Pro is the same underlying model as [GPT-5.6 Sol](https://openrouter.ai/openai/gpt-5.6-sol), served with `reasoning.mode` set to `pro` for higher-quality responses on complex tasks.\n\nLearn more in OpenAI's docs: https://developers.openai.com/api/docs/guides/reasoning#reasoning-mode","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000001","input_cache_write":"0.00000125","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000002","completion":"0.0000075","input_cache_read":"0.0000002","input_cache_write":"0.0000025"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-sol-pro-20260709/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-sol","canonical_slug":"openai/gpt-5.6-sol-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Sol","created":1783590850,"description":"GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.00001","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000004","completion":"0.000015","input_cache_read":"0.0000004","input_cache_write":"0.000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-sol-20260709/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":47.1,"coding_index":77.4,"agentic_index":50.5}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.6-sol:batch","canonical_slug":"openai/gpt-5.6-sol-20260709","hugging_face_id":null,"name":"OpenAI: GPT-5.6 Sol (batch)","created":1783590850,"description":"GPT-5.6 Sol is the flagship model in OpenAI's GPT-5.6 series. It is suited for complex reasoning, coding, and agentic workflows, and is particularly strong at command-line and multi-step coding tasks...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000001","input_cache_write":"0.00000125","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000002","completion":"0.0000075","input_cache_read":"0.0000002","input_cache_write":"0.0000025"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2026-02-16","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.6-sol-20260709/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":47.1,"coding_index":77.4,"agentic_index":50.5}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"x-ai/grok-4.5","canonical_slug":"x-ai/grok-4.5-20260708","hugging_face_id":null,"name":"SpaceXAI: Grok 4.5","created":1783523154,"description":"Grok 4.5 is a model from SpaceXAI with frontier performance on coding, knowledge work, and STEM.","context_length":500000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","web_search":"0.005","input_cache_read":"0.0000003","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000004","completion":"0.000012","input_cache_read":"0.0000006"}]},"top_provider":{"context_length":500000,"max_completion_tokens":450000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-4.5-20260708/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1235,"win_rate":55.9,"rank":6},{"arena":"agents","category":"androidnative","elo":1247,"win_rate":64,"rank":8},{"arena":"agents","category":"fullstack","elo":1261,"win_rate":61.8,"rank":8},{"arena":"agents","category":"godotgamedev","elo":1270,"win_rate":62.1,"rank":3},{"arena":"agents","category":"htmlslides","elo":1208,"win_rate":52.5,"rank":6},{"arena":"agents","category":"mobileapps","elo":1245,"win_rate":53.8,"rank":10},{"arena":"agents","category":"python-pptxslides","elo":1230,"win_rate":53.9,"rank":12},{"arena":"agents","category":"webapps","elo":1231,"win_rate":53.9,"rank":15},{"arena":"models","category":"3d","elo":1288,"win_rate":50.6,"rank":20},{"arena":"models","category":"asciiart","elo":1268,"win_rate":54.3,"rank":14},{"arena":"models","category":"codecategories","elo":1293,"win_rate":51.3,"rank":16},{"arena":"models","category":"dataviz","elo":1286,"win_rate":49.8,"rank":17},{"arena":"models","category":"gamedev","elo":1288,"win_rate":49.3,"rank":20},{"arena":"models","category":"svg","elo":1245,"win_rate":48.9,"rank":15},{"arena":"models","category":"uicomponent","elo":1294,"win_rate":52.1,"rank":19},{"arena":"models","category":"website","elo":1296,"win_rate":53.2,"rank":15}],"artificial_analysis":{"intelligence_index":39.1,"coding_index":72.4,"agentic_index":42.1}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low"],"default_effort":"high"}},{"id":"~x-ai/grok-latest","canonical_slug":"~x-ai/grok-latest","alias_target":{"name":"SpaceXAI: Grok 4.6","slug":"x-ai/grok-4.6"},"hugging_face_id":null,"name":"xAI: Grok Latest","created":1783519360,"description":"This model always redirects to the latest Grok model from xAI.","context_length":500000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","web_search":"0.005","input_cache_read":"0.0000005","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000004","completion":"0.000012","input_cache_read":"0.000001"}]},"top_provider":{"context_length":500000,"max_completion_tokens":450000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~x-ai/grok-latest/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"high"}},{"id":"aion-labs/aion-3.0-mini","canonical_slug":"aion-labs/aion-3.0-mini-20260707","hugging_face_id":null,"name":"AionLabs: Aion-3.0-Mini","created":1783443096,"description":"Aion-3.0 Mini is a multi-model roleplaying and storytelling system from AionLabs, built on the DeepSeek family of models. It uses a collaborative generation process in which multiple specialized models each...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000007","completion":"0.0000014","input_cache_read":"0.00000018"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/aion-labs/aion-3.0-mini-20260707/endpoints"},"reasoning":{"mandatory":true}},{"id":"aion-labs/aion-3.0","canonical_slug":"aion-labs/aion-3.0-20260707","hugging_face_id":null,"name":"AionLabs: Aion-3.0","created":1783443095,"description":"Aion-3.0 is a multi-model roleplaying and storytelling system from AionLabs, built on the GLM family of models. It uses a collaborative generation process in which multiple specialized models each contribute...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000006","input_cache_read":"0.00000075"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/aion-labs/aion-3.0-20260707/endpoints"},"reasoning":{"mandatory":true}},{"id":"tencent/hy3","canonical_slug":"tencent/hy3-20260706","hugging_face_id":"tencent/Hy3","name":"Tencent: Hy3","created":1783344048,"description":"Hy3 is a 295B-parameter Mixture-of-Experts model from Tencent (21B active, 192 experts with top-8 routing) built for reasoning, agentic workflows, and real-world production use. It supports a configurable reasoning effort:...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000132","completion":"0.000000528","input_cache_read":"0.000000033","overrides":[{"utc_start":0,"utc_end":1600,"prompt":"0.000000132","completion":"0.000000528","input_cache_read":"0.000000033"},{"utc_start":1600,"utc_end":0,"prompt":"0.0000000825","completion":"0.00000033","input_cache_read":"0.000000020625"}]},"top_provider":{"context_length":262144,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_completion_tokens","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.9,"top_p":1,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/tencent/hy3-20260706/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1205,"win_rate":43.8,"rank":47},{"arena":"models","category":"codecategories","elo":1192,"win_rate":41.1,"rank":58},{"arena":"models","category":"dataviz","elo":1141,"win_rate":36.1,"rank":81},{"arena":"models","category":"gamedev","elo":1159,"win_rate":38.6,"rank":67},{"arena":"models","category":"uicomponent","elo":1178,"win_rate":40.2,"rank":63},{"arena":"models","category":"website","elo":1194,"win_rate":41.3,"rank":62}]},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","low","none"],"default_effort":"high"}},{"id":"poolside/laguna-xs-2.1","canonical_slug":"poolside/laguna-xs-2.1-20260625","hugging_face_id":"poolside/Laguna-XS-2.1","name":"Poolside: Laguna XS 2.1","created":1783002429,"description":"Laguna XS 2.1 is the latest coding agent model in the 33B-A3B category from [Poolside](https://poolside.ai/) and a step forward from their Laguna XS.2 model (released in April 2026). It combines...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000006","completion":"0.00000012","input_cache_read":"0.00000003"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","temperature","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/poolside/laguna-xs-2.1-20260625/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"poolside/laguna-xs-2.1:free","canonical_slug":"poolside/laguna-xs-2.1-20260625","hugging_face_id":"poolside/Laguna-XS-2.1","name":"Poolside: Laguna XS 2.1 (free)","created":1783002429,"description":"Laguna XS 2.1 is the latest coding agent model in the 33B-A3B category from [Poolside](https://poolside.ai/) and a step forward from their Laguna XS.2 model (released in April 2026). It combines...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","temperature","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/poolside/laguna-xs-2.1-20260625/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"anthropic/claude-sonnet-5","canonical_slug":"anthropic/claude-sonnet-5-20260630","hugging_face_id":null,"name":"Anthropic: Claude Sonnet 5","created":1782843083,"description":"Sonnet 5 is Anthropic's most capable Sonnet-class model, with frontier performance across coding, agents, and professional work. It supports adaptive thinking with selectable reasoning effort levels (low, medium, high, max,...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.00001","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","input_cache_write_1h":"0.000004"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-sonnet-5-20260630/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1226,"win_rate":52.3,"rank":7},{"arena":"agents","category":"androidnative","elo":1244,"win_rate":55,"rank":9},{"arena":"agents","category":"fullstack","elo":1257,"win_rate":54.3,"rank":9},{"arena":"agents","category":"godotgamedev","elo":1270,"win_rate":60.6,"rank":2},{"arena":"agents","category":"htmlslides","elo":1220,"win_rate":53.7,"rank":5},{"arena":"agents","category":"mobileapps","elo":1253,"win_rate":55,"rank":8},{"arena":"agents","category":"python-pptxslides","elo":1216,"win_rate":50.1,"rank":13},{"arena":"agents","category":"webapps","elo":1260,"win_rate":54.3,"rank":9},{"arena":"models","category":"3d","elo":1287,"win_rate":54.8,"rank":21},{"arena":"models","category":"asciiart","elo":1225,"win_rate":52.1,"rank":20},{"arena":"models","category":"codecategories","elo":1292,"win_rate":53.7,"rank":17},{"arena":"models","category":"dataviz","elo":1260,"win_rate":52.8,"rank":26},{"arena":"models","category":"gamedev","elo":1313,"win_rate":54,"rank":13},{"arena":"models","category":"svg","elo":1215,"win_rate":51.6,"rank":23},{"arena":"models","category":"uicomponent","elo":1297,"win_rate":55.1,"rank":17},{"arena":"models","category":"website","elo":1289,"win_rate":53.5,"rank":18}],"artificial_analysis":{"intelligence_index":38.4,"coding_index":71.5,"agentic_index":44.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-sonnet-5:batch","canonical_slug":"anthropic/claude-sonnet-5-20260630","hugging_face_id":null,"name":"Anthropic: Claude Sonnet 5 (batch)","created":1782843083,"description":"Sonnet 5 is Anthropic's most capable Sonnet-class model, with frontier performance across coding, agents, and professional work. It supports adaptive thinking with selectable reasoning effort levels (low, medium, high, max,...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000001","input_cache_write":"0.00000125","input_cache_write_1h":"0.000002"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-sonnet-5-20260630/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1226,"win_rate":52.3,"rank":7},{"arena":"agents","category":"androidnative","elo":1244,"win_rate":55,"rank":9},{"arena":"agents","category":"fullstack","elo":1257,"win_rate":54.3,"rank":9},{"arena":"agents","category":"godotgamedev","elo":1270,"win_rate":60.6,"rank":2},{"arena":"agents","category":"htmlslides","elo":1220,"win_rate":53.7,"rank":5},{"arena":"agents","category":"mobileapps","elo":1253,"win_rate":55,"rank":8},{"arena":"agents","category":"python-pptxslides","elo":1216,"win_rate":50.1,"rank":13},{"arena":"agents","category":"webapps","elo":1260,"win_rate":54.3,"rank":9},{"arena":"models","category":"3d","elo":1287,"win_rate":54.8,"rank":21},{"arena":"models","category":"asciiart","elo":1225,"win_rate":52.1,"rank":20},{"arena":"models","category":"codecategories","elo":1292,"win_rate":53.7,"rank":17},{"arena":"models","category":"dataviz","elo":1260,"win_rate":52.8,"rank":26},{"arena":"models","category":"gamedev","elo":1313,"win_rate":54,"rank":13},{"arena":"models","category":"svg","elo":1215,"win_rate":51.6,"rank":23},{"arena":"models","category":"uicomponent","elo":1297,"win_rate":55.1,"rank":17},{"arena":"models","category":"website","elo":1289,"win_rate":53.5,"rank":18}],"artificial_analysis":{"intelligence_index":38.4,"coding_index":71.5,"agentic_index":44.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"google/gemini-3.1-flash-lite-image","canonical_slug":"google/gemini-3.1-flash-lite-image-20260630","hugging_face_id":null,"name":"Google: Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image)","created":1782837225,"description":"Nano Banana 2 Lite (Gemini 3.1 Flash Lite Image) is Google's fastest, most cost-efficient Gemini image model, built for high-velocity developer pipelines and rapid-fire visual exploration. It delivers text-to-image generation...","context_length":65536,"architecture":{"modality":"text+image->text+image","input_modalities":["image","text"],"output_modalities":["image","text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.0000015","image_output":"0.00003","web_search":"0.014"},"top_provider":{"context_length":65536,"max_completion_tokens":58982,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","temperature","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-01-01","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-flash-lite-image-20260630/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","minimal"],"default_effort":"minimal"}},{"id":"sakana/fugu-ultra","canonical_slug":"sakana/fugu-ultra-20260615","hugging_face_id":null,"name":"Sakana: Fugu Ultra","created":1782276303,"description":"Fugu Ultra is the higher-performance model in Sakana AI's Fugu family. Rather than a single monolithic model, Fugu is a learned multi-agent orchestration system: a language model trained to route...","context_length":1000000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.00003","web_search":"0.01","input_cache_read":"0.0000005","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00001","completion":"0.000045","input_cache_read":"0.000001"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","reasoning","reasoning_effort","structured_outputs","tool_choice","tools","web_search_options"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/sakana/fugu-ultra-20260615/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["max","xhigh","high"],"default_effort":"xhigh"}},{"id":"google/gemini-3.1-flash-image","canonical_slug":"google/gemini-3.1-flash-image-20260528","hugging_face_id":null,"name":"Google: Nano Banana 2 (Gemini 3.1 Flash Image)","created":1781754065,"description":"Gemini 3.1 Flash Image, a.k.a. \"Nano Banana 2,\" is Google’s latest state of the art image generation and editing model, delivering Pro-level visual quality at Flash speed. It combines advanced...","context_length":131072,"architecture":{"modality":"text+image->text+image","input_modalities":["image","text"],"output_modalities":["image","text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.000003","image_output":"0.00006","web_search":"0.014"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-flash-image-20260528/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","minimal"],"default_effort":"minimal"}},{"id":"google/gemini-3-pro-image","canonical_slug":"google/gemini-3-pro-image-20260528","hugging_face_id":null,"name":"Google: Nano Banana Pro (Gemini 3 Pro Image)","created":1781754054,"description":"Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and...","context_length":131072,"architecture":{"modality":"text+image->text+image","input_modalities":["image","text"],"output_modalities":["image","text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","image":"0.000002","image_output":"0.00012","audio":"0.000002","input_audio_cache":"0.0000002","web_search":"0.014","internal_reasoning":"0.000012","input_cache_read":"0.0000002","input_cache_write":"0.000000375"},"top_provider":{"context_length":65536,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3-pro-image-20260528/endpoints"},"reasoning":{"mandatory":true}},{"id":"cohere/north-mini-code:free","canonical_slug":"cohere/north-mini-code-20260617","hugging_face_id":"CohereLabs/North-Mini-Code-1.0","name":"Cohere: North Mini Code (free)","created":1781723748,"description":"North Mini Code is Cohere's first agentic coding model and the debut of its North family. A sparse mixture-of-experts model with 30B total parameters and 3B active, it is optimized...","context_length":256000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Cohere","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":256000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/cohere/north-mini-code-20260617/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":9.9,"coding_index":36.5,"agentic_index":1.1}},"reasoning":{"mandatory":false}},{"id":"z-ai/glm-5.2","canonical_slug":"z-ai/glm-5.2-20260616","hugging_face_id":"zai-org/GLM-5.2","name":"Z.ai: GLM 5.2","created":1781631930,"description":"GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000014","completion":"0.0000044","input_cache_read":"0.00000014"},"top_provider":{"context_length":1048576,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.2-20260616/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1205,"win_rate":49.1,"rank":10},{"arena":"agents","category":"androidnative","elo":1190,"win_rate":53,"rank":17},{"arena":"agents","category":"fullstack","elo":1250,"win_rate":61.1,"rank":12},{"arena":"agents","category":"godotgamedev","elo":1142,"win_rate":40.1,"rank":16},{"arena":"agents","category":"htmlslides","elo":1178,"win_rate":49.6,"rank":13},{"arena":"agents","category":"mobileapps","elo":1190,"win_rate":51,"rank":23},{"arena":"agents","category":"python-pptxslides","elo":1194,"win_rate":48.7,"rank":16},{"arena":"agents","category":"webapps","elo":1236,"win_rate":56.2,"rank":13},{"arena":"models","category":"3d","elo":1322,"win_rate":54,"rank":13},{"arena":"models","category":"asciiart","elo":1227,"win_rate":48.7,"rank":19},{"arena":"models","category":"codecategories","elo":1309,"win_rate":53,"rank":10},{"arena":"models","category":"dataviz","elo":1312,"win_rate":53.8,"rank":10},{"arena":"models","category":"gamedev","elo":1297,"win_rate":49.9,"rank":17},{"arena":"models","category":"svg","elo":1248,"win_rate":50.4,"rank":14},{"arena":"models","category":"uicomponent","elo":1309,"win_rate":55.7,"rank":14},{"arena":"models","category":"website","elo":1305,"win_rate":53.3,"rank":11}],"artificial_analysis":{"intelligence_index":34,"coding_index":68.8,"agentic_index":39.4}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["xhigh","high"],"default_effort":"high"}},{"id":"z-ai/glm-5.2:batch","canonical_slug":"z-ai/glm-5.2-20260616","hugging_face_id":"zai-org/GLM-5.2","name":"Z.ai: GLM 5.2 (batch)","created":1781631930,"description":"GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000007","completion":"0.0000022","input_cache_read":"0.00000007"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.2-20260616/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1205,"win_rate":49.1,"rank":10},{"arena":"agents","category":"androidnative","elo":1190,"win_rate":53,"rank":17},{"arena":"agents","category":"fullstack","elo":1250,"win_rate":61.1,"rank":12},{"arena":"agents","category":"godotgamedev","elo":1142,"win_rate":40.1,"rank":16},{"arena":"agents","category":"htmlslides","elo":1178,"win_rate":49.6,"rank":13},{"arena":"agents","category":"mobileapps","elo":1190,"win_rate":51,"rank":23},{"arena":"agents","category":"python-pptxslides","elo":1194,"win_rate":48.7,"rank":16},{"arena":"agents","category":"webapps","elo":1236,"win_rate":56.2,"rank":13},{"arena":"models","category":"3d","elo":1322,"win_rate":54,"rank":13},{"arena":"models","category":"asciiart","elo":1227,"win_rate":48.7,"rank":19},{"arena":"models","category":"codecategories","elo":1309,"win_rate":53,"rank":10},{"arena":"models","category":"dataviz","elo":1312,"win_rate":53.8,"rank":10},{"arena":"models","category":"gamedev","elo":1297,"win_rate":49.9,"rank":17},{"arena":"models","category":"svg","elo":1248,"win_rate":50.4,"rank":14},{"arena":"models","category":"uicomponent","elo":1309,"win_rate":55.7,"rank":14},{"arena":"models","category":"website","elo":1305,"win_rate":53.3,"rank":11}],"artificial_analysis":{"intelligence_index":34,"coding_index":68.8,"agentic_index":39.4}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["xhigh","high"],"default_effort":"high"}},{"id":"z-ai/glm-5.2:free","canonical_slug":"z-ai/glm-5.2-20260616","hugging_face_id":"zai-org/GLM-5.2","name":"Z.ai: GLM 5.2 (free)","created":1781631930,"description":"GLM 5.2 is a large-scale reasoning model from Z.ai. It supports text input and output with a 1M-token context window, and is suited for long-horizon agent workflows, project-level software engineering,...","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":32768,"max_completion_tokens":29491,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.2-20260616/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1205,"win_rate":49.1,"rank":10},{"arena":"agents","category":"androidnative","elo":1190,"win_rate":53,"rank":17},{"arena":"agents","category":"fullstack","elo":1250,"win_rate":61.1,"rank":12},{"arena":"agents","category":"godotgamedev","elo":1142,"win_rate":40.1,"rank":16},{"arena":"agents","category":"htmlslides","elo":1178,"win_rate":49.6,"rank":13},{"arena":"agents","category":"mobileapps","elo":1190,"win_rate":51,"rank":23},{"arena":"agents","category":"python-pptxslides","elo":1194,"win_rate":48.7,"rank":16},{"arena":"agents","category":"webapps","elo":1236,"win_rate":56.2,"rank":13},{"arena":"models","category":"3d","elo":1322,"win_rate":54,"rank":13},{"arena":"models","category":"asciiart","elo":1227,"win_rate":48.7,"rank":19},{"arena":"models","category":"codecategories","elo":1309,"win_rate":53,"rank":10},{"arena":"models","category":"dataviz","elo":1312,"win_rate":53.8,"rank":10},{"arena":"models","category":"gamedev","elo":1297,"win_rate":49.9,"rank":17},{"arena":"models","category":"svg","elo":1248,"win_rate":50.4,"rank":14},{"arena":"models","category":"uicomponent","elo":1309,"win_rate":55.7,"rank":14},{"arena":"models","category":"website","elo":1305,"win_rate":53.3,"rank":11}],"artificial_analysis":{"intelligence_index":34,"coding_index":68.8,"agentic_index":39.4}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["xhigh","high"],"default_effort":"high"}},{"id":"openrouter/fusion","canonical_slug":"openrouter/fusion","hugging_face_id":null,"name":"OpenRouter: Fusion","created":1781371647,"description":"Fusion turns your prompt into a small multi-model deliberation. A panel of expert models (see below) analyzes your prompt in parallel with web search and web fetch enabled, then a...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"-1","completion":"-1"},"top_provider":{"context_length":null,"max_completion_tokens":null,"is_moderated":false},"per_request_limits":null,"supported_parameters":[],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openrouter/fusion/endpoints"}},{"id":"moonshotai/kimi-k2.7-code","canonical_slug":"moonshotai/kimi-k2.7-code-20260612","hugging_face_id":"moonshotai/Kimi-K2.7-Code","name":"MoonshotAI: Kimi K2.7 Code","created":1781266361,"description":"MoonshotAI: Kimi K2.7 Code is a coding-focused model in Moonshot AI's Kimi K2 family, built to complete end-to-end programming tasks reliably over long contexts. It uses a native multimodal mixture-of-experts...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000007062","completion":"0.00000321","input_cache_read":"0.00000018"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k2.7-code-20260612/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1130,"win_rate":43,"rank":21},{"arena":"agents","category":"androidnative","elo":1151,"win_rate":46.1,"rank":25},{"arena":"agents","category":"fullstack","elo":1185,"win_rate":51.6,"rank":22},{"arena":"agents","category":"godotgamedev","elo":1185,"win_rate":49.5,"rank":11},{"arena":"agents","category":"htmlslides","elo":1204,"win_rate":53.6,"rank":7},{"arena":"agents","category":"mobileapps","elo":1172,"win_rate":47.1,"rank":27},{"arena":"agents","category":"python-pptxslides","elo":1154,"win_rate":44.7,"rank":22},{"arena":"agents","category":"webapps","elo":1190,"win_rate":47.3,"rank":24},{"arena":"models","category":"3d","elo":1269,"win_rate":51.2,"rank":27},{"arena":"models","category":"asciiart","elo":1218,"win_rate":48.8,"rank":22},{"arena":"models","category":"codecategories","elo":1271,"win_rate":51.2,"rank":29},{"arena":"models","category":"dataviz","elo":1238,"win_rate":49.4,"rank":38},{"arena":"models","category":"gamedev","elo":1242,"win_rate":48.5,"rank":35},{"arena":"models","category":"svg","elo":1208,"win_rate":47.6,"rank":28},{"arena":"models","category":"uicomponent","elo":1281,"win_rate":52.5,"rank":25},{"arena":"models","category":"website","elo":1280,"win_rate":52.5,"rank":25}],"artificial_analysis":{"intelligence_index":26.3,"coding_index":60.8,"agentic_index":22.5}},"reasoning":{"mandatory":true,"default_enabled":true}},{"id":"~anthropic/claude-fable-latest","canonical_slug":"~anthropic/claude-fable-latest","alias_target":{"name":"Anthropic: Claude Fable 5.1","slug":"anthropic/claude-fable-5.1"},"hugging_face_id":null,"name":"Anthropic: Claude Fable Latest","created":1781029944,"description":"This model always redirects to the latest model in the Claude Fable family.","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00005","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.0000125","input_cache_write_1h":"0.00002"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~anthropic/claude-fable-latest/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-fable-5","canonical_slug":"anthropic/claude-5-fable-20260609","hugging_face_id":null,"name":"Anthropic: Claude Fable 5","created":1781007515,"description":"Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00005","web_search":"0.01","input_cache_read":"0.000001","input_cache_write":"0.0000125","input_cache_write_1h":"0.00002"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-5-fable-20260609/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1285,"win_rate":61.9,"rank":1},{"arena":"agents","category":"agentichtmlslides","elo":1254,"win_rate":59.4,"rank":1},{"arena":"agents","category":"agenticslides(html)","elo":1252,"win_rate":59.5,"rank":1},{"arena":"agents","category":"androidnative","elo":1294,"win_rate":64.8,"rank":1},{"arena":"agents","category":"fullstack","elo":1283,"win_rate":58.4,"rank":6},{"arena":"agents","category":"godotgamedev","elo":1343,"win_rate":70,"rank":1},{"arena":"agents","category":"htmlslides","elo":1280,"win_rate":63.1,"rank":1},{"arena":"agents","category":"mobileapps","elo":1296,"win_rate":62,"rank":2},{"arena":"agents","category":"python-pptxslides","elo":1293,"win_rate":61.2,"rank":3},{"arena":"agents","category":"webapps","elo":1273,"win_rate":56.4,"rank":6},{"arena":"models","category":"3d","elo":1347,"win_rate":61.9,"rank":8},{"arena":"models","category":"asciiart","elo":1340,"win_rate":67.6,"rank":2},{"arena":"models","category":"codecategories","elo":1325,"win_rate":58,"rank":7},{"arena":"models","category":"dataviz","elo":1325,"win_rate":58.1,"rank":8},{"arena":"models","category":"gamedev","elo":1361,"win_rate":61.5,"rank":6},{"arena":"models","category":"svg","elo":1332,"win_rate":64.5,"rank":4},{"arena":"models","category":"uicomponent","elo":1331,"win_rate":58.1,"rank":9},{"arena":"models","category":"website","elo":1308,"win_rate":56.5,"rank":10}],"artificial_analysis":{"intelligence_index":49.7,"coding_index":76.5,"agentic_index":51}},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-fable-5:batch","canonical_slug":"anthropic/claude-5-fable-20260609","hugging_face_id":null,"name":"Anthropic: Claude Fable 5 (batch)","created":1781007515,"description":"Claude Fable 5 is a Mythos-class model from Anthropic, built for autonomous knowledge work and coding. It supports text, image, and file inputs with text output, with reasoning support and...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-5-fable-20260609/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1285,"win_rate":61.9,"rank":1},{"arena":"agents","category":"agentichtmlslides","elo":1254,"win_rate":59.4,"rank":1},{"arena":"agents","category":"agenticslides(html)","elo":1252,"win_rate":59.5,"rank":1},{"arena":"agents","category":"androidnative","elo":1294,"win_rate":64.8,"rank":1},{"arena":"agents","category":"fullstack","elo":1283,"win_rate":58.4,"rank":6},{"arena":"agents","category":"godotgamedev","elo":1343,"win_rate":70,"rank":1},{"arena":"agents","category":"htmlslides","elo":1280,"win_rate":63.1,"rank":1},{"arena":"agents","category":"mobileapps","elo":1296,"win_rate":62,"rank":2},{"arena":"agents","category":"python-pptxslides","elo":1293,"win_rate":61.2,"rank":3},{"arena":"agents","category":"webapps","elo":1273,"win_rate":56.4,"rank":6},{"arena":"models","category":"3d","elo":1347,"win_rate":61.9,"rank":8},{"arena":"models","category":"asciiart","elo":1340,"win_rate":67.6,"rank":2},{"arena":"models","category":"codecategories","elo":1325,"win_rate":58,"rank":7},{"arena":"models","category":"dataviz","elo":1325,"win_rate":58.1,"rank":8},{"arena":"models","category":"gamedev","elo":1361,"win_rate":61.5,"rank":6},{"arena":"models","category":"svg","elo":1332,"win_rate":64.5,"rank":4},{"arena":"models","category":"uicomponent","elo":1331,"win_rate":58.1,"rank":9},{"arena":"models","category":"website","elo":1308,"win_rate":56.5,"rank":10}],"artificial_analysis":{"intelligence_index":49.7,"coding_index":76.5,"agentic_index":51}},"reasoning":{"mandatory":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"nvidia/nemotron-3.5-content-safety","canonical_slug":"nvidia/nemotron-3.5-content-safety-20260604","hugging_face_id":"nvidia/Nemotron-3.5-Content-Safety","name":"NVIDIA: Nemotron 3.5 Content Safety","created":1780581864,"description":"NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B. It moderates both inputs to and responses from LLMs and VLMs, accepting...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000002"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3.5-content-safety-20260604/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"nvidia/nemotron-3.5-content-safety:free","canonical_slug":"nvidia/nemotron-3.5-content-safety-20260604","hugging_face_id":"nvidia/Nemotron-3.5-Content-Safety","name":"NVIDIA: Nemotron 3.5 Content Safety (free)","created":1780581864,"description":"NVIDIA Nemotron 3.5 Content Safety is a compact 4B-parameter multimodal guardrail model from NVIDIA, fine-tuned from Google Gemma-3-4B. It moderates both inputs to and responses from LLMs and VLMs, accepting...","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":128000,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","seed","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3.5-content-safety-20260604/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"nvidia/nemotron-3-ultra-550b-a55b","canonical_slug":"nvidia/nemotron-3-ultra-550b-a55b-20260604","hugging_face_id":"nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16","name":"NVIDIA: Nemotron 3 Ultra","created":1780551208,"description":"NVIDIA Nemotron 3 Ultra is an open frontier-reasoning and orchestration model from NVIDIA, with 55B active parameters out of 550B total (MoE). Built on a hybrid Transformer-Mamba mixture-of-experts architecture, it...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000625","completion":"0.000003125","input_cache_read":"0.0000001875"},"top_provider":{"context_length":256000,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3-ultra-550b-a55b-20260604/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1163,"win_rate":40.5,"rank":62},{"arena":"models","category":"asciiart","elo":1097,"win_rate":36.5,"rank":60},{"arena":"models","category":"codecategories","elo":1155,"win_rate":36.3,"rank":77},{"arena":"models","category":"dataviz","elo":1157,"win_rate":38.5,"rank":73},{"arena":"models","category":"gamedev","elo":1155,"win_rate":36.9,"rank":70},{"arena":"models","category":"svg","elo":1099,"win_rate":35.5,"rank":60},{"arena":"models","category":"uicomponent","elo":1151,"win_rate":37.2,"rank":73},{"arena":"models","category":"website","elo":1145,"win_rate":34.5,"rank":84}],"artificial_analysis":{"intelligence_index":23.4,"coding_index":49.3,"agentic_index":21.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true,"supported_efforts":["high","medium"],"default_effort":"high"}},{"id":"nvidia/nemotron-3-ultra-550b-a55b:free","canonical_slug":"nvidia/nemotron-3-ultra-550b-a55b-20260604","hugging_face_id":"nvidia/NVIDIA-Nemotron-3-Ultra-550B-A55B-BF16","name":"NVIDIA: Nemotron 3 Ultra (free)","created":1780551208,"description":"NVIDIA Nemotron 3 Ultra is an open frontier-reasoning and orchestration model from NVIDIA, with 55B active parameters out of 550B total (MoE). Built on a hybrid Transformer-Mamba mixture-of-experts architecture, it...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","seed","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3-ultra-550b-a55b-20260604/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1163,"win_rate":40.5,"rank":62},{"arena":"models","category":"asciiart","elo":1097,"win_rate":36.5,"rank":60},{"arena":"models","category":"codecategories","elo":1155,"win_rate":36.3,"rank":77},{"arena":"models","category":"dataviz","elo":1157,"win_rate":38.5,"rank":73},{"arena":"models","category":"gamedev","elo":1155,"win_rate":36.9,"rank":70},{"arena":"models","category":"svg","elo":1099,"win_rate":35.5,"rank":60},{"arena":"models","category":"uicomponent","elo":1151,"win_rate":37.2,"rank":73},{"arena":"models","category":"website","elo":1145,"win_rate":34.5,"rank":84}],"artificial_analysis":{"intelligence_index":23.4,"coding_index":49.3,"agentic_index":21.7}},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true,"supported_efforts":["high","medium"],"default_effort":"high"}},{"id":"qwen/qwen3.7-plus","canonical_slug":"qwen/qwen3.7-plus-20260602","hugging_face_id":null,"name":"Qwen: Qwen3.7 Plus","created":1780491783,"description":"Qwen3.7-Plus is a cost-effective model in Alibaba's Qwen3.7 series. It supports text and image input with text output, building on the series' text capabilities with a comprehensive upgrade to its...","context_length":1000000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.00000032","completion":"0.00000128","input_cache_read":"0.000000064","input_cache_write":"0.0000004","overrides":[{"min_prompt_tokens":256000,"prompt":"0.00000096","completion":"0.00000384","input_cache_read":"0.000000192","input_cache_write":"0.0000012"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.7-plus-20260602/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1267,"win_rate":48.9,"rank":28},{"arena":"models","category":"asciiart","elo":1146,"win_rate":40.3,"rank":47},{"arena":"models","category":"codecategories","elo":1281,"win_rate":50.5,"rank":25},{"arena":"models","category":"dataviz","elo":1295,"win_rate":52.3,"rank":13},{"arena":"models","category":"gamedev","elo":1286,"win_rate":50.3,"rank":21},{"arena":"models","category":"svg","elo":1241,"win_rate":50.2,"rank":18},{"arena":"models","category":"uicomponent","elo":1269,"win_rate":49.2,"rank":31},{"arena":"models","category":"website","elo":1281,"win_rate":51.1,"rank":24}],"artificial_analysis":{"intelligence_index":25.8,"coding_index":55.9,"agentic_index":19.7}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"minimax/minimax-m3","canonical_slug":"minimax/minimax-m3-20260531","hugging_face_id":"MiniMaxAI/Minimax-M3","name":"MiniMax: MiniMax M3","created":1780245374,"description":"MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...","context_length":1048576,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.00000006"},"top_provider":{"context_length":524288,"max_completion_tokens":512000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m3-20260531/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1159,"win_rate":44.7,"rank":19},{"arena":"agents","category":"androidnative","elo":1167,"win_rate":43.8,"rank":23},{"arena":"agents","category":"fullstack","elo":1207,"win_rate":48.3,"rank":19},{"arena":"agents","category":"htmlslides","elo":1183,"win_rate":46.5,"rank":11},{"arena":"agents","category":"mobileapps","elo":1193,"win_rate":46.4,"rank":20},{"arena":"agents","category":"python-pptxslides","elo":1207,"win_rate":47.1,"rank":14},{"arena":"agents","category":"webapps","elo":1215,"win_rate":48.4,"rank":20},{"arena":"models","category":"3d","elo":1239,"win_rate":51.5,"rank":35},{"arena":"models","category":"asciiart","elo":1180,"win_rate":47.6,"rank":31},{"arena":"models","category":"codecategories","elo":1265,"win_rate":52,"rank":31},{"arena":"models","category":"dataviz","elo":1249,"win_rate":51.6,"rank":34},{"arena":"models","category":"gamedev","elo":1237,"win_rate":47.1,"rank":37},{"arena":"models","category":"svg","elo":1194,"win_rate":48.2,"rank":32},{"arena":"models","category":"uicomponent","elo":1261,"win_rate":51.6,"rank":32},{"arena":"models","category":"website","elo":1271,"win_rate":52.6,"rank":29}],"artificial_analysis":{"intelligence_index":29.6,"coding_index":58.6,"agentic_index":30.8}},"reasoning":{"mandatory":false}},{"id":"minimax/minimax-m3:batch","canonical_slug":"minimax/minimax-m3-20260531","hugging_face_id":"MiniMaxAI/Minimax-M3","name":"MiniMax: MiniMax M3 (batch)","created":1780245374,"description":"MiniMax-M3 is a multimodal foundation model from MiniMax. It supports text, image, and video inputs with text output, a 1M-token context window, and is suited for long-horizon agentic work, coding,...","context_length":524288,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.00000006"},"top_provider":{"context_length":524288,"max_completion_tokens":471859,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m3-20260531/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1159,"win_rate":44.7,"rank":19},{"arena":"agents","category":"androidnative","elo":1167,"win_rate":43.8,"rank":23},{"arena":"agents","category":"fullstack","elo":1207,"win_rate":48.3,"rank":19},{"arena":"agents","category":"htmlslides","elo":1183,"win_rate":46.5,"rank":11},{"arena":"agents","category":"mobileapps","elo":1193,"win_rate":46.4,"rank":20},{"arena":"agents","category":"python-pptxslides","elo":1207,"win_rate":47.1,"rank":14},{"arena":"agents","category":"webapps","elo":1215,"win_rate":48.4,"rank":20},{"arena":"models","category":"3d","elo":1239,"win_rate":51.5,"rank":35},{"arena":"models","category":"asciiart","elo":1180,"win_rate":47.6,"rank":31},{"arena":"models","category":"codecategories","elo":1265,"win_rate":52,"rank":31},{"arena":"models","category":"dataviz","elo":1249,"win_rate":51.6,"rank":34},{"arena":"models","category":"gamedev","elo":1237,"win_rate":47.1,"rank":37},{"arena":"models","category":"svg","elo":1194,"win_rate":48.2,"rank":32},{"arena":"models","category":"uicomponent","elo":1261,"win_rate":51.6,"rank":32},{"arena":"models","category":"website","elo":1271,"win_rate":52.6,"rank":29}],"artificial_analysis":{"intelligence_index":29.6,"coding_index":58.6,"agentic_index":30.8}},"reasoning":{"mandatory":false}},{"id":"stepfun/step-3.7-flash","canonical_slug":"stepfun/step-3.7-flash-20260528","hugging_face_id":"stepfun-ai/Step-3.7-Flash","name":"StepFun: Step 3.7 Flash","created":1779985069,"description":"Step 3.7 Flash is StepFun's latest high-efficiency multimodal Mixture-of-Experts model. It pairs a 196B-parameter language backbone with a vision encoder for native image and video understanding, activating roughly 11B parameters...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.00000115","input_cache_read":"0.00000004"},"top_provider":{"context_length":256000,"max_completion_tokens":230400,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/stepfun/step-3.7-flash-20260528/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1156,"win_rate":41.3,"rank":65},{"arena":"models","category":"asciiart","elo":1164,"win_rate":44.6,"rank":38},{"arena":"models","category":"codecategories","elo":1196,"win_rate":43.4,"rank":55},{"arena":"models","category":"dataviz","elo":1186,"win_rate":43.5,"rank":60},{"arena":"models","category":"gamedev","elo":1176,"win_rate":39.3,"rank":60},{"arena":"models","category":"svg","elo":1099,"win_rate":37.5,"rank":61},{"arena":"models","category":"uicomponent","elo":1197,"win_rate":43.7,"rank":53},{"arena":"models","category":"website","elo":1207,"win_rate":44.8,"rank":54}],"artificial_analysis":{"intelligence_index":null,"coding_index":39.6,"agentic_index":null}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"anthropic/claude-opus-4.8","canonical_slug":"anthropic/claude-4.8-opus-20260528","hugging_face_id":null,"name":"Anthropic: Claude Opus 4.8","created":1779905091,"description":"Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.8-opus-20260528/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1238,"win_rate":55.8,"rank":4},{"arena":"agents","category":"agentichtmlslides","elo":1227,"win_rate":55.6,"rank":3},{"arena":"agents","category":"agenticslides","elo":1294,"win_rate":64.8,"rank":1},{"arena":"agents","category":"agenticslides(html)","elo":1230,"win_rate":56,"rank":3},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1310,"win_rate":68.9,"rank":1},{"arena":"agents","category":"androidnative","elo":1247,"win_rate":56.5,"rank":7},{"arena":"agents","category":"fullstack","elo":1256,"win_rate":55.6,"rank":10},{"arena":"agents","category":"godotgamedev","elo":1255,"win_rate":59,"rank":4},{"arena":"agents","category":"htmlslides","elo":1223,"win_rate":58,"rank":4},{"arena":"agents","category":"mobileapps","elo":1253,"win_rate":57,"rank":7},{"arena":"agents","category":"pptxslides","elo":1306,"win_rate":67.9,"rank":1},{"arena":"agents","category":"python-pptxslides","elo":1298,"win_rate":65.9,"rank":2},{"arena":"agents","category":"webapps","elo":1246,"win_rate":52,"rank":11},{"arena":"models","category":"3d","elo":1251,"win_rate":52.8,"rank":32},{"arena":"models","category":"asciiart","elo":1291,"win_rate":62.3,"rank":8},{"arena":"models","category":"codecategories","elo":1266,"win_rate":53.3,"rank":30},{"arena":"models","category":"dataviz","elo":1253,"win_rate":53.7,"rank":30},{"arena":"models","category":"gamedev","elo":1279,"win_rate":53.6,"rank":26},{"arena":"models","category":"svg","elo":1213,"win_rate":52.7,"rank":24},{"arena":"models","category":"uicomponent","elo":1271,"win_rate":54.3,"rank":29},{"arena":"models","category":"website","elo":1267,"win_rate":53.5,"rank":31}],"artificial_analysis":{"intelligence_index":42,"coding_index":74.3,"agentic_index":42.6}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-opus-4.8:batch","canonical_slug":"anthropic/claude-4.8-opus-20260528","hugging_face_id":null,"name":"Anthropic: Claude Opus 4.8 (batch)","created":1779905091,"description":"Claude Opus 4.8 is Anthropic's most capable generally available model in the Opus family. It supports text, image, and file inputs with text output, with reasoning support and a 1M-token...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.0000125","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.000003125","input_cache_write_1h":"0.000005"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.8-opus-20260528/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1238,"win_rate":55.8,"rank":4},{"arena":"agents","category":"agentichtmlslides","elo":1227,"win_rate":55.6,"rank":3},{"arena":"agents","category":"agenticslides","elo":1294,"win_rate":64.8,"rank":1},{"arena":"agents","category":"agenticslides(html)","elo":1230,"win_rate":56,"rank":3},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1310,"win_rate":68.9,"rank":1},{"arena":"agents","category":"androidnative","elo":1247,"win_rate":56.5,"rank":7},{"arena":"agents","category":"fullstack","elo":1256,"win_rate":55.6,"rank":10},{"arena":"agents","category":"godotgamedev","elo":1255,"win_rate":59,"rank":4},{"arena":"agents","category":"htmlslides","elo":1223,"win_rate":58,"rank":4},{"arena":"agents","category":"mobileapps","elo":1253,"win_rate":57,"rank":7},{"arena":"agents","category":"pptxslides","elo":1306,"win_rate":67.9,"rank":1},{"arena":"agents","category":"python-pptxslides","elo":1298,"win_rate":65.9,"rank":2},{"arena":"agents","category":"webapps","elo":1246,"win_rate":52,"rank":11},{"arena":"models","category":"3d","elo":1251,"win_rate":52.8,"rank":32},{"arena":"models","category":"asciiart","elo":1291,"win_rate":62.3,"rank":8},{"arena":"models","category":"codecategories","elo":1266,"win_rate":53.3,"rank":30},{"arena":"models","category":"dataviz","elo":1253,"win_rate":53.7,"rank":30},{"arena":"models","category":"gamedev","elo":1279,"win_rate":53.6,"rank":26},{"arena":"models","category":"svg","elo":1213,"win_rate":52.7,"rank":24},{"arena":"models","category":"uicomponent","elo":1271,"win_rate":54.3,"rank":29},{"arena":"models","category":"website","elo":1267,"win_rate":53.5,"rank":31}],"artificial_analysis":{"intelligence_index":42,"coding_index":74.3,"agentic_index":42.6}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"qwen/qwen3.7-max","canonical_slug":"qwen/qwen3.7-max-20260520","hugging_face_id":null,"name":"Qwen: Qwen3.7 Max","created":1779376861,"description":"Qwen3.7-Max is the flagship model in Alibaba's Qwen3.7 series. It supports text input and output and is designed for agent-centric workloads, with particular strengths in coding, office and productivity tasks,...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000001475","completion":"0.000004425","input_cache_read":"0.000000295","input_cache_write":"0.00000184375"},"top_provider":{"context_length":1000000,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.7-max-20260520/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1161,"win_rate":46.6,"rank":18},{"arena":"agents","category":"androidnative","elo":1170,"win_rate":48.6,"rank":22},{"arena":"agents","category":"fullstack","elo":1202,"win_rate":49.6,"rank":20},{"arena":"agents","category":"godotgamedev","elo":1225,"win_rate":55.6,"rank":7},{"arena":"agents","category":"htmlslides","elo":1161,"win_rate":43.5,"rank":15},{"arena":"agents","category":"mobileapps","elo":1177,"win_rate":45.5,"rank":25},{"arena":"agents","category":"python-pptxslides","elo":1194,"win_rate":47.2,"rank":17},{"arena":"agents","category":"webapps","elo":1225,"win_rate":50.2,"rank":16},{"arena":"models","category":"3d","elo":1296,"win_rate":55.4,"rank":18},{"arena":"models","category":"asciiart","elo":1231,"win_rate":53.6,"rank":18},{"arena":"models","category":"codecategories","elo":1291,"win_rate":55,"rank":20},{"arena":"models","category":"dataviz","elo":1291,"win_rate":54.5,"rank":16},{"arena":"models","category":"gamedev","elo":1292,"win_rate":54.5,"rank":18},{"arena":"models","category":"svg","elo":1244,"win_rate":57.9,"rank":16},{"arena":"models","category":"uicomponent","elo":1285,"win_rate":54.5,"rank":23},{"arena":"models","category":"website","elo":1285,"win_rate":55.1,"rank":21}],"artificial_analysis":{"intelligence_index":29.9,"coding_index":66,"agentic_index":23.9}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"x-ai/grok-build-0.1","canonical_slug":"x-ai/grok-build-0.1-20260520","hugging_face_id":null,"name":"SpaceXAI: Grok Build 0.1","created":1779298123,"description":"Grok Build 0.1 is SpaceXAI’s fast coding model trained specifically for agentic software engineering workflows. It supports text and image inputs with text output, and is optimized for interactive coding...","context_length":256000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000002","web_search":"0.005","input_cache_read":"0.0000002","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000002","completion":"0.000004","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":256000,"max_completion_tokens":230400,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-build-0.1-20260520/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":51.5,"agentic_index":null}},"reasoning":{"mandatory":true}},{"id":"google/gemini-3.5-flash","canonical_slug":"google/gemini-3.5-flash-20260519","hugging_face_id":null,"name":"Google: Gemini 3.5 Flash","created":1779193800,"description":"Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000015","completion":"0.000009","image":"0.0000015","audio":"0.000003","input_audio_cache":"0.0000003","web_search":"0.014","internal_reasoning":"0.000009","input_cache_read":"0.00000015","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-01","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.5-flash-20260519/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1180,"win_rate":55.4,"rank":12},{"arena":"agents","category":"agentichtmlslides","elo":1162,"win_rate":45.8,"rank":6},{"arena":"agents","category":"agenticslides","elo":1244,"win_rate":57.5,"rank":3},{"arena":"agents","category":"agenticslides(html)","elo":1162,"win_rate":45.7,"rank":6},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1242,"win_rate":57.8,"rank":2},{"arena":"agents","category":"androidnative","elo":1198,"win_rate":57.8,"rank":14},{"arena":"agents","category":"fullstack","elo":1208,"win_rate":57,"rank":18},{"arena":"agents","category":"godotgamedev","elo":1137,"win_rate":43.1,"rank":20},{"arena":"agents","category":"htmlslides","elo":1145,"win_rate":46.5,"rank":19},{"arena":"agents","category":"mobileapps","elo":1201,"win_rate":51.9,"rank":18},{"arena":"agents","category":"pptxslides","elo":1244,"win_rate":57.7,"rank":2},{"arena":"agents","category":"python-pptxslides","elo":1247,"win_rate":57.4,"rank":9},{"arena":"agents","category":"webapps","elo":1219,"win_rate":53.1,"rank":18},{"arena":"models","category":"3d","elo":1270,"win_rate":56.8,"rank":26},{"arena":"models","category":"asciiart","elo":1273,"win_rate":59.5,"rank":12},{"arena":"models","category":"codecategories","elo":1278,"win_rate":54.7,"rank":26},{"arena":"models","category":"dataviz","elo":1250,"win_rate":53.6,"rank":32},{"arena":"models","category":"gamedev","elo":1288,"win_rate":54.1,"rank":19},{"arena":"models","category":"svg","elo":1281,"win_rate":60.5,"rank":9},{"arena":"models","category":"uicomponent","elo":1287,"win_rate":55.4,"rank":22},{"arena":"models","category":"website","elo":1275,"win_rate":54,"rank":28}],"artificial_analysis":{"intelligence_index":33,"coding_index":70.1,"agentic_index":27.3}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"google/gemini-3.5-flash:batch","canonical_slug":"google/gemini-3.5-flash-20260519","hugging_face_id":null,"name":"Google: Gemini 3.5 Flash (batch)","created":1779193800,"description":"Gemini 3.5 Flash is Google's high-efficiency multimodal model, bringing near-Pro level coding and reasoning at Flash-tier cost and speed. It is highly optimized for coding proficiency and parallel agentic execution...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.0000045","image":"0.00000075","audio":"0.0000015","input_audio_cache":"0.00000015","web_search":"0.014","internal_reasoning":"0.0000045","input_cache_read":"0.000000075"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-01","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.5-flash-20260519/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1180,"win_rate":55.4,"rank":12},{"arena":"agents","category":"agentichtmlslides","elo":1162,"win_rate":45.8,"rank":6},{"arena":"agents","category":"agenticslides","elo":1244,"win_rate":57.5,"rank":3},{"arena":"agents","category":"agenticslides(html)","elo":1162,"win_rate":45.7,"rank":6},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1242,"win_rate":57.8,"rank":2},{"arena":"agents","category":"androidnative","elo":1198,"win_rate":57.8,"rank":14},{"arena":"agents","category":"fullstack","elo":1208,"win_rate":57,"rank":18},{"arena":"agents","category":"godotgamedev","elo":1137,"win_rate":43.1,"rank":20},{"arena":"agents","category":"htmlslides","elo":1145,"win_rate":46.5,"rank":19},{"arena":"agents","category":"mobileapps","elo":1201,"win_rate":51.9,"rank":18},{"arena":"agents","category":"pptxslides","elo":1244,"win_rate":57.7,"rank":2},{"arena":"agents","category":"python-pptxslides","elo":1247,"win_rate":57.4,"rank":9},{"arena":"agents","category":"webapps","elo":1219,"win_rate":53.1,"rank":18},{"arena":"models","category":"3d","elo":1270,"win_rate":56.8,"rank":26},{"arena":"models","category":"asciiart","elo":1273,"win_rate":59.5,"rank":12},{"arena":"models","category":"codecategories","elo":1278,"win_rate":54.7,"rank":26},{"arena":"models","category":"dataviz","elo":1250,"win_rate":53.6,"rank":32},{"arena":"models","category":"gamedev","elo":1288,"win_rate":54.1,"rank":19},{"arena":"models","category":"svg","elo":1281,"win_rate":60.5,"rank":9},{"arena":"models","category":"uicomponent","elo":1287,"win_rate":55.4,"rank":22},{"arena":"models","category":"website","elo":1275,"win_rate":54,"rank":28}],"artificial_analysis":{"intelligence_index":33,"coding_index":70.1,"agentic_index":27.3}},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"perceptron/perceptron-mk1","canonical_slug":"perceptron/perceptron-mk1-20260512","hugging_face_id":null,"name":"Perceptron: Perceptron Mk1","created":1778597029,"description":"Perceptron Mk1 (Mark One) is Perceptron's highest-quality vision-language model for video and embodied reasoning.** It accepts image and video inputs paired with natural language queries, and produces detailed visual understanding...","context_length":32768,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000015"},"top_provider":{"context_length":32768,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","structured_outputs","temperature","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/perceptron/perceptron-mk1-20260512/endpoints"},"reasoning":{"mandatory":false}},{"id":"google/gemini-3.1-flash-lite","canonical_slug":"google/gemini-3.1-flash-lite-20260507","hugging_face_id":null,"name":"Google: Gemini 3.1 Flash Lite","created":1778168828,"description":"Gemini 3.1 Flash Lite is Google’s GA high-efficiency multimodal model optimized for low-latency, high-volume workloads. It supports text, image, video, audio, and PDF inputs, and is designed for lightweight agentic...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.0000015","image":"0.00000025","audio":"0.0000005","input_audio_cache":"0.00000005","web_search":"0.014","internal_reasoning":"0.0000015","input_cache_read":"0.000000025","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-flash-lite-20260507/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"minimal"}},{"id":"google/gemini-3.1-flash-lite:batch","canonical_slug":"google/gemini-3.1-flash-lite-20260507","hugging_face_id":null,"name":"Google: Gemini 3.1 Flash Lite (batch)","created":1778168828,"description":"Gemini 3.1 Flash Lite is Google’s GA high-efficiency multimodal model optimized for low-latency, high-volume workloads. It supports text, image, video, audio, and PDF inputs, and is designed for lightweight agentic...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000000125","completion":"0.00000075","image":"0.000000125","audio":"0.00000025","input_audio_cache":"0.000000025","web_search":"0.014","internal_reasoning":"0.00000075","input_cache_read":"0.0000000125"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-flash-lite-20260507/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"minimal"}},{"id":"openai/gpt-chat-latest","canonical_slug":"openai/gpt-chat-latest-20260505","hugging_face_id":null,"name":"OpenAI: GPT Chat Latest","created":1778000212,"description":"GPT Chat Latest points to OpenAI's stable API alias `chat-latest` that always resolves to the latest Instant chat model used in ChatGPT. As OpenAI rolls out new Instant model updates...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.00003","web_search":"0.01","input_cache_read":"0.0000005"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-chat-latest-20260505/endpoints"}},{"id":"x-ai/grok-4.3","canonical_slug":"x-ai/grok-4.3-20260430","hugging_face_id":null,"name":"SpaceXAI: Grok 4.3","created":1777591821,"description":"Grok 4.3 is a reasoning model from SpaceXAI. It accepts text and image inputs with text output, and is suited for agentic workflows, instruction-following tasks, and applications requiring high factual...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.0000025","web_search":"0.005","input_cache_read":"0.0000002","overrides":[{"min_prompt_tokens":200000,"prompt":"0.0000025","completion":"0.000005","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":900000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-4.3-20260430/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1007,"win_rate":28.7,"rank":24},{"arena":"agents","category":"agentichtmlslides","elo":1066,"win_rate":31.8,"rank":9},{"arena":"agents","category":"agenticslides","elo":1072,"win_rate":31.9,"rank":9},{"arena":"agents","category":"agenticslides(html)","elo":1066,"win_rate":31.7,"rank":9},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1068,"win_rate":32.4,"rank":9},{"arena":"agents","category":"androidnative","elo":973,"win_rate":22.5,"rank":36},{"arena":"agents","category":"fullstack","elo":1017,"win_rate":28.5,"rank":40},{"arena":"agents","category":"godotgamedev","elo":1035,"win_rate":31.2,"rank":28},{"arena":"agents","category":"htmlslides","elo":1011,"win_rate":28.4,"rank":23},{"arena":"agents","category":"mobileapps","elo":1093,"win_rate":35.3,"rank":41},{"arena":"agents","category":"pptxslides","elo":1071,"win_rate":32.5,"rank":8},{"arena":"agents","category":"python-pptxslides","elo":1072,"win_rate":30.8,"rank":26},{"arena":"agents","category":"webapps","elo":1144,"win_rate":43.9,"rank":28},{"arena":"models","category":"3d","elo":1153,"win_rate":41.8,"rank":66},{"arena":"models","category":"asciiart","elo":1156,"win_rate":45.2,"rank":43},{"arena":"models","category":"codecategories","elo":1201,"win_rate":44.9,"rank":52},{"arena":"models","category":"dataviz","elo":1198,"win_rate":46,"rank":54},{"arena":"models","category":"gamedev","elo":1197,"win_rate":45.3,"rank":51},{"arena":"models","category":"svg","elo":1110,"win_rate":39.5,"rank":58},{"arena":"models","category":"uicomponent","elo":1206,"win_rate":46,"rank":50},{"arena":"models","category":"website","elo":1206,"win_rate":45,"rank":55}],"artificial_analysis":{"intelligence_index":25.4,"coding_index":42.2,"agentic_index":17.2}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","none"],"default_effort":"low"}},{"id":"x-ai/grok-4.3:batch","canonical_slug":"x-ai/grok-4.3-20260430","hugging_face_id":null,"name":"SpaceXAI: Grok 4.3 (batch)","created":1777591821,"description":"Grok 4.3 is a reasoning model from SpaceXAI. It accepts text and image inputs with text output, and is suited for agentic workflows, instruction-following tasks, and applications requiring high factual...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000002","web_search":"0.005","input_cache_read":"0.00000016","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000002","completion":"0.000004","input_cache_read":"0.00000032"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":900000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-4.3-20260430/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1007,"win_rate":28.7,"rank":24},{"arena":"agents","category":"agentichtmlslides","elo":1066,"win_rate":31.8,"rank":9},{"arena":"agents","category":"agenticslides","elo":1072,"win_rate":31.9,"rank":9},{"arena":"agents","category":"agenticslides(html)","elo":1066,"win_rate":31.7,"rank":9},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1068,"win_rate":32.4,"rank":9},{"arena":"agents","category":"androidnative","elo":973,"win_rate":22.5,"rank":36},{"arena":"agents","category":"fullstack","elo":1017,"win_rate":28.5,"rank":40},{"arena":"agents","category":"godotgamedev","elo":1035,"win_rate":31.2,"rank":28},{"arena":"agents","category":"htmlslides","elo":1011,"win_rate":28.4,"rank":23},{"arena":"agents","category":"mobileapps","elo":1093,"win_rate":35.3,"rank":41},{"arena":"agents","category":"pptxslides","elo":1071,"win_rate":32.5,"rank":8},{"arena":"agents","category":"python-pptxslides","elo":1072,"win_rate":30.8,"rank":26},{"arena":"agents","category":"webapps","elo":1144,"win_rate":43.9,"rank":28},{"arena":"models","category":"3d","elo":1153,"win_rate":41.8,"rank":66},{"arena":"models","category":"asciiart","elo":1156,"win_rate":45.2,"rank":43},{"arena":"models","category":"codecategories","elo":1201,"win_rate":44.9,"rank":52},{"arena":"models","category":"dataviz","elo":1198,"win_rate":46,"rank":54},{"arena":"models","category":"gamedev","elo":1197,"win_rate":45.3,"rank":51},{"arena":"models","category":"svg","elo":1110,"win_rate":39.5,"rank":58},{"arena":"models","category":"uicomponent","elo":1206,"win_rate":46,"rank":50},{"arena":"models","category":"website","elo":1206,"win_rate":45,"rank":55}],"artificial_analysis":{"intelligence_index":25.4,"coding_index":42.2,"agentic_index":17.2}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","none"],"default_effort":"low"}},{"id":"mistralai/mistral-medium-3-5","canonical_slug":"mistralai/mistral-medium-3.5-20260430","hugging_face_id":null,"name":"Mistral: Mistral Medium 3.5","created":1777570439,"description":"Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...","context_length":262144,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000015","completion":"0.0000075"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-medium-3.5-20260430/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":14.9,"coding_index":46.9,"agentic_index":9.4}},"reasoning":{"mandatory":false,"supported_efforts":["high","none"],"default_effort":"high"}},{"id":"mistralai/mistral-medium-3-5:batch","canonical_slug":"mistralai/mistral-medium-3.5-20260430","hugging_face_id":null,"name":"Mistral: Mistral Medium 3.5 (batch)","created":1777570439,"description":"Mistral Medium 3.5 is a dense 128B instruction-following model from Mistral AI. It supports text and image inputs with text output, and is designed for agentic workflows, coding, and complex...","context_length":262144,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.00000375"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-medium-3.5-20260430/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":14.9,"coding_index":46.9,"agentic_index":9.4}},"reasoning":{"mandatory":false,"supported_efforts":["high","none"],"default_effort":"high"}},{"id":"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning:free","canonical_slug":"nvidia/nemotron-3-nano-omni-30b-a3b-reasoning-20260428","hugging_face_id":"nvidia/Nemotron-3-Nano-Omni-30B-A3B-Reasoning-BF16","name":"NVIDIA: Nemotron 3 Nano Omni (free)","created":1777393095,"description":"NVIDIA Nemotron™ 3 Nano Omni is a 30B-A3B open multimodal model designed to function as a perception and context sub-agent in enterprise agent systems. It accepts text, image, video, and...","context_length":256000,"architecture":{"modality":"text+image+audio+video->text","input_modalities":["text","audio","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":256000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","seed","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.6,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3-nano-omni-30b-a3b-reasoning-20260428/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":13.8,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true}},{"id":"~anthropic/claude-haiku-latest","canonical_slug":"~anthropic/claude-haiku-latest","alias_target":{"name":"Anthropic: Claude Haiku 4.5","slug":"anthropic/claude-haiku-4.5"},"hugging_face_id":null,"name":"Anthropic: Claude Haiku Latest","created":1777318492,"description":"This model always redirects to the latest model in the Claude Haiku family.","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000001","input_cache_write":"0.00000125","input_cache_write_1h":"0.000002"},"top_provider":{"context_length":200000,"max_completion_tokens":64000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~anthropic/claude-haiku-latest/endpoints"},"reasoning":{"mandatory":false}},{"id":"~openai/gpt-mini-latest","canonical_slug":"~openai/gpt-mini-latest","alias_target":{"name":"OpenAI: GPT-5.4 Mini","slug":"openai/gpt-5.4-mini"},"hugging_face_id":null,"name":"OpenAI: GPT Mini Latest","created":1777318471,"description":"This model always redirects to the latest model in the GPT Mini family.","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.0000045","web_search":"0.01","input_cache_read":"0.000000075"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-08-31","expiration_date":null,"links":{"details":"/api/v1/models/~openai/gpt-mini-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"~google/gemini-pro-latest","canonical_slug":"~google/gemini-pro-latest","alias_target":{"name":"Google: Gemini 3.1 Pro Preview","slug":"google/gemini-3.1-pro-preview"},"hugging_face_id":null,"name":"Google: Gemini Pro Latest","created":1777318451,"description":"This model always redirects to the latest model in the Gemini Pro family.","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["audio","file","image","text","video"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","image":"0.000002","audio":"0.000002","input_audio_cache":"0.0000002","web_search":"0.014","internal_reasoning":"0.000012","input_cache_read":"0.0000002","input_cache_write":"0.000000375","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000004","completion":"0.000018","audio":"0.000004","input_audio_cache":"0.0000004","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~google/gemini-pro-latest/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"~moonshotai/kimi-latest","canonical_slug":"~moonshotai/kimi-latest","alias_target":{"name":"MoonshotAI: Kimi K3","slug":"moonshotai/kimi-k3"},"hugging_face_id":null,"name":"MoonshotAI: Kimi Latest","created":1777318428,"description":"This model always redirects to the latest model in the Kimi family.","context_length":1048576,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.0000021","completion":"0.00001095","input_cache_read":"0.00000023"},"top_provider":{"context_length":1048576,"max_completion_tokens":943718,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~moonshotai/kimi-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","high","low"],"default_effort":"max"}},{"id":"~google/gemini-flash-latest","canonical_slug":"~google/gemini-flash-latest","alias_target":{"name":"Google: Gemini 3.8 Flash","slug":"google/gemini-3.8-flash"},"hugging_face_id":null,"name":"Google: Gemini Flash Latest","created":1777318398,"description":"This model always redirects to the latest model in the Gemini Flash family.","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.00000375","image":"0.00000075","audio":"0.00000075","input_audio_cache":"0.000000075","web_search":"0.014","internal_reasoning":"0.00000375","input_cache_read":"0.000000075","input_cache_write":"0.0000000416666666666667"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~google/gemini-flash-latest/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"~anthropic/claude-sonnet-latest","canonical_slug":"~anthropic/claude-sonnet-latest","alias_target":{"name":"Anthropic: Claude Sonnet 5","slug":"anthropic/claude-sonnet-5"},"hugging_face_id":null,"name":"Anthropic: Claude Sonnet Latest","created":1777318368,"description":"This model always redirects to the latest model in the Claude Sonnet family.","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.00001","web_search":"0.01","input_cache_read":"0.0000002","input_cache_write":"0.0000025","input_cache_write_1h":"0.000004"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~anthropic/claude-sonnet-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"qwen/qwen3.5-plus-20260420","canonical_slug":"qwen/qwen3.5-plus-20260420","hugging_face_id":null,"name":"Qwen: Qwen3.5 Plus 2026-04-20","created":1777261368,"description":"Qwen3.5 Plus (April 2026) is a large-scale multimodal language model from Alibaba. It accepts text, image, and video input and produces text output, with a 1M token context window. This...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000018","input_cache_write":"0.000000375","overrides":[{"min_prompt_tokens":256000,"prompt":"0.000000375","completion":"0.00000225","input_cache_write":"0.00000046875"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-plus-20260420/endpoints"},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.6-flash","canonical_slug":"qwen/qwen3.6-flash","hugging_face_id":null,"name":"Qwen: Qwen3.6 Flash","created":1777261362,"description":"Qwen3.6 Flash is a fast, efficient language model from Alibaba's Qwen 3.6 series. It supports text, image, and video input with a 1M token context window. Tiered pricing kicks in...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000001875","completion":"0.000001125","input_cache_write":"0.000000234375","overrides":[{"min_prompt_tokens":256000,"prompt":"0.00000075","completion":"0.000003","input_cache_write":"0.0000009375"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.6-flash/endpoints"},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.6-35b-a3b","canonical_slug":"qwen/qwen3.6-35b-a3b-20260415","hugging_face_id":"Qwen/Qwen3.6-35B-A3B","name":"Qwen: Qwen3.6 35B A3B","created":1777260255,"description":"Qwen3.6-35B-A3B is an open-weight multimodal model from Alibaba Cloud with 35 billion total parameters and 3 billion active parameters per token. It uses a hybrid sparse mixture-of-experts architecture combining Gated...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000009","input_cache_read":"0.00000005"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":20},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.6-35b-a3b-20260415/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":18.8,"coding_index":41.9,"agentic_index":15}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"qwen/qwen3.6-max-preview","canonical_slug":"qwen/qwen3.6-max-preview-20260420","hugging_face_id":null,"name":"Qwen: Qwen3.6 Max Preview","created":1777260242,"description":"Qwen3.6-Max-Preview is a proprietary frontier model from Alibaba Cloud built on a sparse mixture-of-experts architecture with approximately 1 trillion total parameters. It is optimized for agentic coding, tool use, and...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000001027","completion":"0.000006162","input_cache_write":"0.00000128375","overrides":[{"min_prompt_tokens":128000,"prompt":"0.00000158","completion":"0.00000948","input_cache_write":"0.000001975"}]},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.6-max-preview-20260420/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"qwen/qwen3.6-27b","canonical_slug":"qwen/qwen3.6-27b-20260422","hugging_face_id":"Qwen/Qwen3.6-27B","name":"Qwen: Qwen3.6 27B","created":1777255064,"description":"Qwen3.6 27B is a dense 27-billion-parameter language model from the Qwen Team at Alibaba, released in April 2026. It features hybrid multimodal capabilities — accepting text, image, and video inputs...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.000002","input_cache_read":"0.00000003"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.6-27b-20260422/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":21.9,"coding_index":53.7,"agentic_index":20.1}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"openai/gpt-5.5-pro","canonical_slug":"openai/gpt-5.5-pro-20260423","hugging_face_id":"","name":"OpenAI: GPT-5.5 Pro","created":1777051896,"description":"GPT-5.5 Pro is OpenAI’s high-capability model optimized for deep reasoning and accuracy on complex, high-stakes workloads. It features a 1M+ token context window (922K input, 128K output) with support for...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00003","completion":"0.00018","web_search":"0.01","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00006","completion":"0.00027"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-12-01","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.5-pro-20260423/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium"],"default_effort":"medium"}},{"id":"openai/gpt-5.5-pro:batch","canonical_slug":"openai/gpt-5.5-pro-20260423","hugging_face_id":"","name":"OpenAI: GPT-5.5 Pro (batch)","created":1777051896,"description":"GPT-5.5 Pro is OpenAI’s high-capability model optimized for deep reasoning and accuracy on complex, high-stakes workloads. It features a 1M+ token context window (922K input, 128K output) with support for...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000015","completion":"0.00009","web_search":"0.01","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00003","completion":"0.000135"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-12-01","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.5-pro-20260423/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium"],"default_effort":"medium"}},{"id":"openai/gpt-5.5","canonical_slug":"openai/gpt-5.5-20260423","hugging_face_id":"","name":"OpenAI: GPT-5.5","created":1777051893,"description":"GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.00003","web_search":"0.01","input_cache_read":"0.0000005","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00001","completion":"0.000045","input_cache_read":"0.000001"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-12-01","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.5-20260423/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1179,"win_rate":52.4,"rank":14},{"arena":"agents","category":"agentichtmlslides","elo":1084,"win_rate":34.2,"rank":8},{"arena":"agents","category":"agenticslides","elo":1150,"win_rate":43.5,"rank":6},{"arena":"agents","category":"agenticslides(html)","elo":1077,"win_rate":33.2,"rank":8},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1155,"win_rate":45.2,"rank":6},{"arena":"agents","category":"androidnative","elo":1176,"win_rate":50.9,"rank":20},{"arena":"agents","category":"fullstack","elo":1099,"win_rate":43,"rank":29},{"arena":"agents","category":"godotgamedev","elo":1210,"win_rate":52.2,"rank":9},{"arena":"agents","category":"htmlslides","elo":1070,"win_rate":35.6,"rank":22},{"arena":"agents","category":"mobileapps","elo":1170,"win_rate":50.4,"rank":28},{"arena":"agents","category":"pptxslides","elo":1157,"win_rate":45.3,"rank":6},{"arena":"agents","category":"python-pptxslides","elo":1152,"win_rate":43.3,"rank":23},{"arena":"agents","category":"webapps","elo":1132,"win_rate":42.6,"rank":31},{"arena":"models","category":"3d","elo":1227,"win_rate":50.9,"rank":40},{"arena":"models","category":"asciiart","elo":1275,"win_rate":60.2,"rank":11},{"arena":"models","category":"codecategories","elo":1274,"win_rate":54.2,"rank":28},{"arena":"models","category":"dataviz","elo":1274,"win_rate":56,"rank":22},{"arena":"models","category":"gamedev","elo":1320,"win_rate":58.8,"rank":12},{"arena":"models","category":"svg","elo":1262,"win_rate":57.5,"rank":12},{"arena":"models","category":"uicomponent","elo":1271,"win_rate":54.5,"rank":30},{"arena":"models","category":"website","elo":1269,"win_rate":53.3,"rank":30}],"artificial_analysis":{"intelligence_index":38.6,"coding_index":74.9,"agentic_index":37.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.5:batch","canonical_slug":"openai/gpt-5.5-20260423","hugging_face_id":"","name":"OpenAI: GPT-5.5 (batch)","created":1777051893,"description":"GPT-5.5 is OpenAI’s frontier model designed for complex professional workloads, building on GPT-5.4 with stronger reasoning, higher reliability, and improved token efficiency on hard tasks. It features a 1M+ token...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.000015","web_search":"0.01","input_cache_read":"0.00000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000005","completion":"0.0000225","input_cache_read":"0.0000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-12-01","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.5-20260423/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1179,"win_rate":52.4,"rank":14},{"arena":"agents","category":"agentichtmlslides","elo":1084,"win_rate":34.2,"rank":8},{"arena":"agents","category":"agenticslides","elo":1150,"win_rate":43.5,"rank":6},{"arena":"agents","category":"agenticslides(html)","elo":1077,"win_rate":33.2,"rank":8},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1155,"win_rate":45.2,"rank":6},{"arena":"agents","category":"androidnative","elo":1176,"win_rate":50.9,"rank":20},{"arena":"agents","category":"fullstack","elo":1099,"win_rate":43,"rank":29},{"arena":"agents","category":"godotgamedev","elo":1210,"win_rate":52.2,"rank":9},{"arena":"agents","category":"htmlslides","elo":1070,"win_rate":35.6,"rank":22},{"arena":"agents","category":"mobileapps","elo":1170,"win_rate":50.4,"rank":28},{"arena":"agents","category":"pptxslides","elo":1157,"win_rate":45.3,"rank":6},{"arena":"agents","category":"python-pptxslides","elo":1152,"win_rate":43.3,"rank":23},{"arena":"agents","category":"webapps","elo":1132,"win_rate":42.6,"rank":31},{"arena":"models","category":"3d","elo":1227,"win_rate":50.9,"rank":40},{"arena":"models","category":"asciiart","elo":1275,"win_rate":60.2,"rank":11},{"arena":"models","category":"codecategories","elo":1274,"win_rate":54.2,"rank":28},{"arena":"models","category":"dataviz","elo":1274,"win_rate":56,"rank":22},{"arena":"models","category":"gamedev","elo":1320,"win_rate":58.8,"rank":12},{"arena":"models","category":"svg","elo":1262,"win_rate":57.5,"rank":12},{"arena":"models","category":"uicomponent","elo":1271,"win_rate":54.5,"rank":30},{"arena":"models","category":"website","elo":1269,"win_rate":53.3,"rank":30}],"artificial_analysis":{"intelligence_index":38.6,"coding_index":74.9,"agentic_index":37.3}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"deepseek/deepseek-v4-pro","canonical_slug":"deepseek/deepseek-v4-pro-20260423","hugging_face_id":"deepseek-ai/DeepSeek-V4-Pro","name":"DeepSeek: DeepSeek V4 Pro 0423","created":1777000679,"description":"DeepSeek V4 Pro is a large-scale Mixture-of-Experts model from DeepSeek with 1.6T total parameters and 49B activated parameters, supporting a 1M-token context window. It is designed for advanced reasoning, coding,...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.0000016","completion":"0.0000032","input_cache_read":"0.000000135"},"top_provider":{"context_length":1048576,"max_completion_tokens":393216,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_completion_tokens","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":1},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-pro-20260423/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":948,"win_rate":22.1,"rank":43},{"arena":"agents","category":"godotgamedev","elo":1059,"win_rate":34,"rank":26},{"arena":"agents","category":"webapps","elo":1000,"win_rate":26.4,"rank":40},{"arena":"models","category":"3d","elo":1282,"win_rate":56.8,"rank":22},{"arena":"models","category":"asciiart","elo":1166,"win_rate":46.4,"rank":36},{"arena":"models","category":"codecategories","elo":1256,"win_rate":51.9,"rank":35},{"arena":"models","category":"dataviz","elo":1219,"win_rate":48.7,"rank":47},{"arena":"models","category":"gamedev","elo":1252,"win_rate":52.3,"rank":33},{"arena":"models","category":"svg","elo":1166,"win_rate":45.4,"rank":43},{"arena":"models","category":"uicomponent","elo":1237,"win_rate":50.6,"rank":41},{"arena":"models","category":"website","elo":1247,"win_rate":50.5,"rank":39}],"artificial_analysis":{"intelligence_index":30.9,"coding_index":59.4,"agentic_index":27.7}},"reasoning":{"mandatory":false,"supported_efforts":["xhigh","high"],"default_effort":"high"}},{"id":"deepseek/deepseek-v4-flash","canonical_slug":"deepseek/deepseek-v4-flash-20260423","hugging_face_id":"deepseek-ai/DeepSeek-V4-Flash","name":"DeepSeek: DeepSeek V4 Flash 0423","created":1777000666,"description":"DeepSeek V4 Flash is an efficiency-optimized Mixture-of-Experts model from DeepSeek with 284B total parameters and 13B activated parameters, supporting a 1M-token context window. It is designed for fast inference and...","context_length":1048576,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.000000088606","completion":"0.000000177212","input_cache_read":"0.0000000177212"},"top_provider":{"context_length":1024000,"max_completion_tokens":384000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_completion_tokens","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v4-flash-20260423/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1216,"win_rate":49.3,"rank":44},{"arena":"models","category":"asciiart","elo":1128,"win_rate":42.8,"rank":53},{"arena":"models","category":"codecategories","elo":1220,"win_rate":48.9,"rank":47},{"arena":"models","category":"dataviz","elo":1143,"win_rate":40.7,"rank":79},{"arena":"models","category":"gamedev","elo":1220,"win_rate":50.2,"rank":43},{"arena":"models","category":"svg","elo":1181,"win_rate":48.4,"rank":36},{"arena":"models","category":"uicomponent","elo":1179,"win_rate":44.7,"rank":62},{"arena":"models","category":"website","elo":1220,"win_rate":49.1,"rank":48}],"artificial_analysis":{"intelligence_index":24.8,"coding_index":52,"agentic_index":27.9}},"reasoning":{"mandatory":false,"supported_efforts":["xhigh","high"],"default_effort":"high"}},{"id":"tencent/hy3-preview","canonical_slug":"tencent/hy3-preview-20260421","hugging_face_id":"tencent/Hy3-preview","name":"Tencent: Hy3 preview","created":1776878150,"description":"Hy3 preview is a high-efficiency Mixture-of-Experts model from Tencent designed for agentic workflows and production use. It supports configurable reasoning levels across disabled, low, and high modes, allowing it to...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000018","completion":"0.0000006","input_cache_read":"0.00000006"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","seed","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.9,"top_p":1,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/tencent/hy3-preview-20260421/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":25.8,"coding_index":58.8,"agentic_index":25.6}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","low","none"],"default_effort":"high"}},{"id":"xiaomi/mimo-v2.5-pro","canonical_slug":"xiaomi/mimo-v2.5-pro-20260422","hugging_face_id":"XiaomiMiMo/MiMo-V2.5-Pro","name":"Xiaomi: MiMo-V2.5-Pro","created":1776874273,"description":"MiMo-V2.5-Pro is Xiaomi’s flagship model, delivering strong performance in general agentic capabilities, complex software engineering, and long-horizon tasks, with top rankings on benchmarks such as ClawEval, GDPVal, and SWE-bench Pro....","context_length":1050000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000435","completion":"0.00000087","input_cache_read":"0.0000000036"},"top_provider":{"context_length":1048576,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/xiaomi/mimo-v2.5-pro-20260422/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1277,"win_rate":55.4,"rank":25},{"arena":"models","category":"asciiart","elo":1169,"win_rate":46.7,"rank":35},{"arena":"models","category":"codecategories","elo":1286,"win_rate":53,"rank":22},{"arena":"models","category":"dataviz","elo":1278,"win_rate":51.4,"rank":19},{"arena":"models","category":"gamedev","elo":1285,"win_rate":54.7,"rank":23},{"arena":"models","category":"svg","elo":1212,"win_rate":50.5,"rank":27},{"arena":"models","category":"uicomponent","elo":1274,"win_rate":54.1,"rank":27},{"arena":"models","category":"website","elo":1285,"win_rate":52,"rank":19}],"artificial_analysis":{"intelligence_index":26.4,"coding_index":60.2,"agentic_index":22.7}},"reasoning":{"mandatory":false}},{"id":"xiaomi/mimo-v2.5","canonical_slug":"xiaomi/mimo-v2.5-20260422","hugging_face_id":"XiaomiMiMo/MiMo-V2.5","name":"Xiaomi: MiMo-V2.5","created":1776874269,"description":"MiMo-V2.5 is a native omnimodal model by Xiaomi. It delivers Pro-level agentic performance at roughly half the inference cost, while surpassing MiMo-V2-Omni in multimodal perception across image and video understanding...","context_length":1050000,"architecture":{"modality":"text+image+audio+video->text","input_modalities":["text","audio","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000014","completion":"0.00000028","input_cache_read":"0.0000000028"},"top_provider":{"context_length":1048576,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/xiaomi/mimo-v2.5-20260422/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1241,"win_rate":51.1,"rank":34},{"arena":"models","category":"asciiart","elo":1162,"win_rate":46.3,"rank":39},{"arena":"models","category":"codecategories","elo":1275,"win_rate":54.1,"rank":27},{"arena":"models","category":"dataviz","elo":1267,"win_rate":54.5,"rank":23},{"arena":"models","category":"gamedev","elo":1268,"win_rate":53.9,"rank":29},{"arena":"models","category":"svg","elo":1201,"win_rate":51.2,"rank":31},{"arena":"models","category":"uicomponent","elo":1273,"win_rate":54.6,"rank":28},{"arena":"models","category":"website","elo":1279,"win_rate":54.2,"rank":26}],"artificial_analysis":{"intelligence_index":22.3,"coding_index":56.8,"agentic_index":17.4}},"reasoning":{"mandatory":false}},{"id":"openai/gpt-5.4-image-2","canonical_slug":"openai/gpt-5.4-image-2-20260421","hugging_face_id":"","name":"OpenAI: GPT-5.4 Image 2","created":1776797528,"description":"[GPT-5.4](https://openrouter.ai/openai/gpt-5.4) Image 2 combines OpenAI's GPT-5.4 model with state-of-the-art image generation capabilities from GPT Image 2. It enables rich multimodal workflows, allowing users to seamlessly move between reasoning, coding, and...","context_length":272000,"architecture":{"modality":"text+image+file->text+image","input_modalities":["image","text","file"],"output_modalities":["image","text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000008","completion":"0.000015","image_output":"0.00003","web_search":"0.01","input_cache_read":"0.000002"},"top_provider":{"context_length":272000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","presence_penalty","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","top_logprobs"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-image-2-20260421/endpoints"},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"~anthropic/claude-opus-latest","canonical_slug":"~anthropic/claude-opus-latest","alias_target":{"name":"Anthropic: Claude Opus 5","slug":"anthropic/claude-opus-5"},"hugging_face_id":"","name":"Anthropic: Claude Opus Latest","created":1776795361,"description":"This model always redirects to the latest model in the Claude Opus family.","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/~anthropic/claude-opus-latest/endpoints"},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"openrouter/pareto-code","canonical_slug":"openrouter/pareto-code","hugging_face_id":"","name":"Pareto Code Router","created":1776747900,"description":"The Pareto Router maintains a tiered shortlist of strong coding models, ranked by [Artificial Analysis](https://artificialanalysis.ai/) coding percentiles. Set min_coding_score between 0 and 1 on the [pareto-router plugin](https://openrouter.ai/docs/guides/routing/routers/pareto-router#the-min_coding_score-parameter) to control how...","context_length":2000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"-1","completion":"-1"},"top_provider":{"context_length":null,"max_completion_tokens":null,"is_moderated":false},"per_request_limits":null,"supported_parameters":[],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openrouter/pareto-code/endpoints"}},{"id":"moonshotai/kimi-k2.6","canonical_slug":"moonshotai/kimi-k2.6-20260420","hugging_face_id":"moonshotai/Kimi-K2.6","name":"MoonshotAI: Kimi K2.6","created":1776699402,"description":"Kimi K2.6 is Moonshot AI's next-generation multimodal model, designed for long-horizon coding, coding-driven UI/UX generation, and multi-agent orchestration. It handles complex end-to-end coding tasks across Python, Rust, and Go, and...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000095","completion":"0.000004","input_cache_read":"0.00000016"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","parallel_tool_calls","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k2.6-20260420/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1141,"win_rate":47.5,"rank":20},{"arena":"agents","category":"agentichtmlslides","elo":1248,"win_rate":59,"rank":2},{"arena":"agents","category":"agenticslides","elo":1187,"win_rate":45.8,"rank":4},{"arena":"agents","category":"agenticslides(html)","elo":1252,"win_rate":59.2,"rank":2},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1186,"win_rate":45.5,"rank":4},{"arena":"agents","category":"androidnative","elo":1176,"win_rate":49.9,"rank":21},{"arena":"agents","category":"fullstack","elo":1165,"win_rate":50.7,"rank":25},{"arena":"agents","category":"godotgamedev","elo":1159,"win_rate":47,"rank":14},{"arena":"agents","category":"htmlslides","elo":1187,"win_rate":52.3,"rank":10},{"arena":"agents","category":"mobileapps","elo":1181,"win_rate":48.4,"rank":24},{"arena":"agents","category":"pptxslides","elo":1181,"win_rate":44.3,"rank":4},{"arena":"agents","category":"python-pptxslides","elo":1180,"win_rate":42.1,"rank":18},{"arena":"agents","category":"webapps","elo":1268,"win_rate":59.3,"rank":7},{"arena":"models","category":"3d","elo":1296,"win_rate":57.6,"rank":17},{"arena":"models","category":"asciiart","elo":1183,"win_rate":47.5,"rank":30},{"arena":"models","category":"codecategories","elo":1287,"win_rate":54.8,"rank":21},{"arena":"models","category":"dataviz","elo":1266,"win_rate":52,"rank":24},{"arena":"models","category":"gamedev","elo":1274,"win_rate":54.9,"rank":28},{"arena":"models","category":"svg","elo":1207,"win_rate":51.2,"rank":29},{"arena":"models","category":"uicomponent","elo":1282,"win_rate":55.6,"rank":24},{"arena":"models","category":"website","elo":1282,"win_rate":54.2,"rank":23}],"artificial_analysis":{"intelligence_index":27.5,"coding_index":61.8,"agentic_index":22.1}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"anthropic/claude-opus-4.7","canonical_slug":"anthropic/claude-4.7-opus-20260416","hugging_face_id":null,"name":"Anthropic: Claude Opus 4.7","created":1776351100,"description":"Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.7-opus-20260416/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":73.6,"agentic_index":39.5}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-opus-4.7:batch","canonical_slug":"anthropic/claude-4.7-opus-20260416","hugging_face_id":null,"name":"Anthropic: Claude Opus 4.7 (batch)","created":1776351100,"description":"Opus 4.7 is the next generation of Anthropic's Opus family, built for long-running, asynchronous agents. Building on the coding and agentic strengths of Opus 4.6, it delivers stronger performance on...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.0000125","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.000003125","input_cache_write_1h":"0.000005"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.7-opus-20260416/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":73.6,"agentic_index":39.5}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["max","xhigh","high","medium","low"],"default_effort":"high"}},{"id":"z-ai/glm-5.1","canonical_slug":"z-ai/glm-5.1-20260406","hugging_face_id":"zai-org/GLM-5.1","name":"Z.ai: GLM 5.1","created":1775578025,"description":"GLM-5.1 delivers a major leap in coding capability, with particularly significant gains in handling long-horizon tasks. Unlike previous models built around minute-level interactions, GLM-5.1 can work independently and continuously on...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000966","completion":"0.000003036","input_cache_read":"0.0000001794"},"top_provider":{"context_length":200000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5.1-20260406/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1336,"win_rate":62.6,"rank":10},{"arena":"models","category":"asciiart","elo":1146,"win_rate":43.8,"rank":46},{"arena":"models","category":"codecategories","elo":1282,"win_rate":53.8,"rank":23},{"arena":"models","category":"dataviz","elo":1366,"win_rate":67,"rank":1},{"arena":"models","category":"gamedev","elo":1285,"win_rate":56.6,"rank":22},{"arena":"models","category":"svg","elo":1242,"win_rate":56.8,"rank":17},{"arena":"models","category":"uicomponent","elo":1292,"win_rate":54.9,"rank":20},{"arena":"models","category":"website","elo":1290,"win_rate":55.1,"rank":17},{"arena":"agents","category":"agenticgamedev","elo":1165,"win_rate":49.8,"rank":17},{"arena":"agents","category":"agentichtmlslides","elo":1205,"win_rate":52,"rank":5},{"arena":"agents","category":"agenticslides","elo":1245,"win_rate":54.4,"rank":2},{"arena":"agents","category":"agenticslides(html)","elo":1204,"win_rate":51.8,"rank":5},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1240,"win_rate":53.3,"rank":3},{"arena":"agents","category":"androidnative","elo":1192,"win_rate":51.9,"rank":16},{"arena":"agents","category":"fullstack","elo":1176,"win_rate":53.5,"rank":23},{"arena":"agents","category":"godotgamedev","elo":1104,"win_rate":39.5,"rank":24},{"arena":"agents","category":"htmlslides","elo":1173,"win_rate":48.9,"rank":14},{"arena":"agents","category":"mobileapps","elo":1192,"win_rate":51.7,"rank":21},{"arena":"agents","category":"pptxslides","elo":1241,"win_rate":53.5,"rank":3},{"arena":"agents","category":"python-pptxslides","elo":1258,"win_rate":54.2,"rank":7},{"arena":"agents","category":"webapps","elo":1190,"win_rate":51.2,"rank":23}],"artificial_analysis":{"intelligence_index":26.4,"coding_index":55.8,"agentic_index":25.2}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"google/gemma-4-26b-a4b-it","canonical_slug":"google/gemma-4-26b-a4b-it-20260403","hugging_face_id":"google/gemma-4-26B-A4B-it","name":"Google: Gemma 4 26B A4B ","created":1775227989,"description":"Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Gemma","instruct_type":null},"pricing":{"prompt":"0.00000009","completion":"0.0000003","input_cache_read":"0.00000005"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":64},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-4-26b-a4b-it-20260403/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":39.3,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":false}},{"id":"google/gemma-4-26b-a4b-it:free","canonical_slug":"google/gemma-4-26b-a4b-it-20260403","hugging_face_id":"google/gemma-4-26B-A4B-it","name":"Google: Gemma 4 26B A4B (free)","created":1775227989,"description":"Gemma 4 26B A4B IT is an instruction-tuned Mixture-of-Experts (MoE) model from Google DeepMind. Despite 25.2B total parameters, only 3.8B activate per token during inference — delivering near-31B quality at...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Gemma","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":64},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-4-26b-a4b-it-20260403/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":39.3,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":false}},{"id":"google/gemma-4-31b-it","canonical_slug":"google/gemma-4-31b-it-20260402","hugging_face_id":"google/gemma-4-31B-it","name":"Google: Gemma 4 31B","created":1775148486,"description":"Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Gemma","instruct_type":null},"pricing":{"prompt":"0.00000009","completion":"0.00000034","input_cache_read":"0.00000005"},"top_provider":{"context_length":262144,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":64,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-4-31b-it-20260402/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":15.4,"coding_index":43.4,"agentic_index":6.7}},"reasoning":{"mandatory":false,"default_enabled":false}},{"id":"google/gemma-4-31b-it:free","canonical_slug":"google/gemma-4-31b-it-20260402","hugging_face_id":"google/gemma-4-31B-it","name":"Google: Gemma 4 31B (free)","created":1775148486,"description":"Gemma 4 31B Instruct is Google DeepMind's 30.7B dense multimodal model supporting text and image input with text output. Features a 256K token context window, configurable thinking/reasoning mode, native function...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Gemma","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":64,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-4-31b-it-20260402/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":15.4,"coding_index":43.4,"agentic_index":6.7}},"reasoning":{"mandatory":false,"default_enabled":false}},{"id":"qwen/qwen3.6-plus","canonical_slug":"qwen/qwen3.6-plus-04-02","hugging_face_id":"","name":"Qwen: Qwen3.6 Plus","created":1775133557,"description":"Qwen 3.6 Plus builds on a hybrid architecture that combines efficient linear attention with sparse mixture-of-experts routing, enabling strong scalability and high-performance inference. Compared to the 3.5 series, it delivers...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.000000325","completion":"0.00000195","input_cache_write":"0.00000040625","overrides":[{"min_prompt_tokens":256000,"prompt":"0.0000013","completion":"0.0000039","input_cache_write":"0.000001625"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.6-plus-04-02/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1230,"win_rate":51.1,"rank":39},{"arena":"models","category":"asciiart","elo":1136,"win_rate":42.9,"rank":51},{"arena":"models","category":"codecategories","elo":1250,"win_rate":51.1,"rank":38},{"arena":"models","category":"dataviz","elo":1245,"win_rate":51.3,"rank":35},{"arena":"models","category":"gamedev","elo":1233,"win_rate":49.9,"rank":39},{"arena":"models","category":"svg","elo":1187,"win_rate":51.1,"rank":35},{"arena":"models","category":"uicomponent","elo":1249,"win_rate":51.6,"rank":38},{"arena":"models","category":"website","elo":1253,"win_rate":50.9,"rank":37}],"artificial_analysis":{"intelligence_index":null,"coding_index":54.5,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"z-ai/glm-5v-turbo","canonical_slug":"z-ai/glm-5v-turbo-20260401","hugging_face_id":"","name":"Z.ai: GLM 5V Turbo","created":1775061458,"description":"GLM-5V-Turbo is Z.ai’s first native multimodal agent foundation model, built for vision-based coding and agent-driven tasks. It natively handles image, video, and text inputs, excels at long-horizon planning, complex coding,...","context_length":202752,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000012","completion":"0.000004","input_cache_read":"0.00000024"},"top_provider":{"context_length":202752,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":"2098-12-31","links":{"details":"/api/v1/models/z-ai/glm-5v-turbo-20260401/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1102,"win_rate":42.6,"rank":23},{"arena":"agents","category":"agentichtmlslides","elo":1134,"win_rate":41.7,"rank":7},{"arena":"agents","category":"agenticslides","elo":1171,"win_rate":51.6,"rank":5},{"arena":"agents","category":"agenticslides(html)","elo":1137,"win_rate":41.8,"rank":7},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1183,"win_rate":53.9,"rank":5},{"arena":"agents","category":"androidnative","elo":1267,"win_rate":54.8,"rank":4},{"arena":"agents","category":"fullstack","elo":1159,"win_rate":52,"rank":26},{"arena":"agents","category":"godotgamedev","elo":997,"win_rate":26.9,"rank":29},{"arena":"agents","category":"htmlslides","elo":1123,"win_rate":44.6,"rank":21},{"arena":"agents","category":"mobileapps","elo":1163,"win_rate":50.4,"rank":31},{"arena":"agents","category":"pptxslides","elo":1164,"win_rate":52.1,"rank":5},{"arena":"agents","category":"python-pptxslides","elo":1165,"win_rate":51.9,"rank":20},{"arena":"agents","category":"webapps","elo":1139,"win_rate":44.7,"rank":30},{"arena":"models","category":"3d","elo":1237,"win_rate":53.3,"rank":37},{"arena":"models","category":"asciiart","elo":1130,"win_rate":43,"rank":52},{"arena":"models","category":"codecategories","elo":1244,"win_rate":50.6,"rank":41},{"arena":"models","category":"dataviz","elo":1216,"win_rate":48,"rank":48},{"arena":"models","category":"gamedev","elo":1241,"win_rate":51.6,"rank":36},{"arena":"models","category":"svg","elo":1175,"win_rate":49.8,"rank":38},{"arena":"models","category":"uicomponent","elo":1227,"win_rate":49.2,"rank":43},{"arena":"models","category":"website","elo":1243,"win_rate":49.6,"rank":40}]},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"arcee-ai/trinity-large-thinking","canonical_slug":"arcee-ai/trinity-large-thinking","hugging_face_id":"arcee-ai/Trinity-Large-Thinking","name":"Arcee AI: Trinity Large Thinking","created":1775058318,"description":"Trinity Large Thinking is a powerful open source reasoning model from the team at Arcee AI. It shows strong performance in PinchBench, agentic workloads, and reasoning tasks. Launch video: https://youtu.be/Gc82AXLa0Rg?si=4RLn6WBz33qT--B7...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.0000008","input_cache_read":"0.00000006"},"top_provider":{"context_length":262144,"max_completion_tokens":80000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.3,"top_p":0.8,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/arcee-ai/trinity-large-thinking/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1110,"win_rate":41.3,"rank":82},{"arena":"models","category":"asciiart","elo":1059,"win_rate":37.1,"rank":63},{"arena":"models","category":"codecategories","elo":1132,"win_rate":40.1,"rank":84},{"arena":"models","category":"dataviz","elo":1114,"win_rate":39.3,"rank":88},{"arena":"models","category":"gamedev","elo":1102,"win_rate":38.4,"rank":89},{"arena":"models","category":"svg","elo":1041,"win_rate":35.2,"rank":75},{"arena":"models","category":"uicomponent","elo":1060,"win_rate":32.6,"rank":94},{"arena":"models","category":"website","elo":1149,"win_rate":41.3,"rank":81}],"artificial_analysis":{"intelligence_index":10.9,"coding_index":25.8,"agentic_index":1.2}},"reasoning":{"mandatory":true}},{"id":"x-ai/grok-4.20-multi-agent","canonical_slug":"x-ai/grok-4.20-multi-agent-20260309","hugging_face_id":"","name":"SpaceXAI: Grok 4.20 Multi-Agent","created":1774979158,"description":"Grok 4.20 Multi-Agent is a variant of SpaceXAI’s Grok 4.20 designed for collaborative, agent-based workflows. Multiple agents operate in parallel to conduct deep research, coordinate tool use, and synthesize information...","context_length":2000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.0000025","web_search":"0.005","input_cache_read":"0.0000002","overrides":[{"min_prompt_tokens":200000,"prompt":"0.0000025","completion":"0.000005","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":2000000,"max_completion_tokens":1800000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-09-01","expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-4.20-multi-agent-20260309/endpoints"},"reasoning":{"mandatory":true,"default_enabled":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"x-ai/grok-4.20","canonical_slug":"x-ai/grok-4.20-20260309","hugging_face_id":"","name":"SpaceXAI: Grok 4.20","created":1774979019,"description":"Grok 4.20 is a reasoning model from SpaceXAI with industry-leading speed and agentic tool calling capabilities. It combines the lowest hallucination rate on the market with strict prompt adherance, delivering...","context_length":2000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Grok","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.0000025","web_search":"0.005","input_cache_read":"0.0000002","overrides":[{"min_prompt_tokens":200000,"prompt":"0.0000025","completion":"0.000005","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":2000000,"max_completion_tokens":1800000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","logprobs","max_tokens","reasoning","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-09-01","expiration_date":null,"links":{"details":"/api/v1/models/x-ai/grok-4.20-20260309/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1059,"win_rate":35,"rank":34},{"arena":"agents","category":"fullstack","elo":1063,"win_rate":41,"rank":34},{"arena":"agents","category":"godotgamedev","elo":1089,"win_rate":38.5,"rank":25},{"arena":"agents","category":"htmlslides","elo":1180,"win_rate":45.7,"rank":12},{"arena":"agents","category":"mobileapps","elo":1118,"win_rate":45.4,"rank":39},{"arena":"agents","category":"webapps","elo":1159,"win_rate":49.7,"rank":26},{"arena":"models","category":"3d","elo":1221,"win_rate":52.1,"rank":42},{"arena":"models","category":"asciiart","elo":1193,"win_rate":48.2,"rank":25},{"arena":"models","category":"codecategories","elo":1236,"win_rate":51.8,"rank":42},{"arena":"models","category":"dataviz","elo":1234,"win_rate":52.1,"rank":43},{"arena":"models","category":"gamedev","elo":1216,"win_rate":50.1,"rank":45},{"arena":"models","category":"svg","elo":1189,"win_rate":52.4,"rank":34},{"arena":"models","category":"uicomponent","elo":1214,"win_rate":48.9,"rank":47},{"arena":"models","category":"website","elo":1242,"win_rate":51.8,"rank":41}]},"reasoning":{"mandatory":false,"default_enabled":false}},{"id":"google/lyria-3-pro-preview","canonical_slug":"google/lyria-3-pro-preview-20260330","hugging_face_id":null,"name":"Google: Lyria 3 Pro Preview","created":1774907286,"description":"Full-length songs are priced at $0.08 per song. Lyria 3 is Google's family of music generation models, available through the Gemini API. With Lyria 3, you can generate high-quality, 48kHz...","context_length":1048576,"architecture":{"modality":"text+image->text+audio","input_modalities":["text","image"],"output_modalities":["text","audio"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/lyria-3-pro-preview-20260330/endpoints"}},{"id":"google/lyria-3-clip-preview","canonical_slug":"google/lyria-3-clip-preview-20260330","hugging_face_id":null,"name":"Google: Lyria 3 Clip Preview","created":1774907255,"description":"30 second duration clips are priced at $0.04 per clip. Lyria 3 is Google's family of music generation models, available through the Gemini API. With Lyria 3, you can generate...","context_length":1048576,"architecture":{"modality":"text+image->text+audio","input_modalities":["text","image"],"output_modalities":["text","audio"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/lyria-3-clip-preview-20260330/endpoints"}},{"id":"kwaipilot/kat-coder-pro-v2","canonical_slug":"kwaipilot/kat-coder-pro-v2-20260327","hugging_face_id":"","name":"Kwaipilot: KAT-Coder-Pro V2","created":1774649310,"description":"KAT-Coder-Pro V2 is the latest high-performance model in KwaiKAT’s KAT-Coder series, designed for complex enterprise-grade software engineering and SaaS integration. It builds on the agentic coding strengths of earlier versions,...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.00000006"},"top_provider":{"context_length":262144,"max_completion_tokens":144000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/kwaipilot/kat-coder-pro-v2-20260327/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":59.5,"agentic_index":null}}},{"id":"rekaai/reka-edge","canonical_slug":"rekaai/reka-edge-2603","hugging_face_id":"RekaAI/reka-edge-2603","name":"Reka Edge","created":1774026965,"description":"Reka Edge is an extremely efficient 7B multimodal vision-language model that accepts image/video+text inputs and generates text outputs. This model is optimized specifically to deliver industry-leading performance in image understanding,...","context_length":16384,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000001"},"top_provider":{"context_length":16384,"max_completion_tokens":14745,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/rekaai/reka-edge-2603/endpoints"},"reasoning":{"mandatory":false}},{"id":"minimax/minimax-m2.7","canonical_slug":"minimax/minimax-m2.7-20260318","hugging_face_id":"MiniMaxAI/MiniMax-M2.7","name":"MiniMax: MiniMax M2.7","created":1773836697,"description":"MiniMax-M2.7 is a next-generation large language model designed for autonomous, real-world productivity and continuous improvement. Built to actively participate in its own evolution, M2.7 integrates advanced agentic capabilities through multi-agent...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.00000006"},"top_provider":{"context_length":204800,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m2.7-20260318/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1221,"win_rate":50.3,"rank":41},{"arena":"models","category":"asciiart","elo":1158,"win_rate":47.4,"rank":41},{"arena":"models","category":"codecategories","elo":1250,"win_rate":51.7,"rank":37},{"arena":"models","category":"dataviz","elo":1249,"win_rate":52.8,"rank":33},{"arena":"models","category":"gamedev","elo":1230,"win_rate":50.9,"rank":40},{"arena":"models","category":"svg","elo":1162,"win_rate":48.5,"rank":46},{"arena":"models","category":"uicomponent","elo":1225,"win_rate":49.1,"rank":44},{"arena":"models","category":"website","elo":1257,"win_rate":52.2,"rank":36}],"artificial_analysis":{"intelligence_index":23.2,"coding_index":52.6,"agentic_index":16.8}},"reasoning":{"mandatory":true}},{"id":"openai/gpt-5.4-nano","canonical_slug":"openai/gpt-5.4-nano-20260317","hugging_face_id":"","name":"OpenAI: GPT-5.4 Nano","created":1773748187,"description":"GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.00000125","web_search":"0.01","input_cache_read":"0.00000002"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-08-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-nano-20260317/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":21.2,"coding_index":56.1,"agentic_index":17.7}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.4-nano:batch","canonical_slug":"openai/gpt-5.4-nano-20260317","hugging_face_id":"","name":"OpenAI: GPT-5.4 Nano (batch)","created":1773748187,"description":"GPT-5.4 nano is the most lightweight and cost-efficient variant of the GPT-5.4 family, optimized for speed-critical and high-volume tasks. It supports text and image inputs and is designed for low-latency...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.000000625","web_search":"0.01","input_cache_read":"0.00000001"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-08-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-nano-20260317/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":21.2,"coding_index":56.1,"agentic_index":17.7}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.4-mini","canonical_slug":"openai/gpt-5.4-mini-20260317","hugging_face_id":"","name":"OpenAI: GPT-5.4 Mini","created":1773748178,"description":"GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000075","completion":"0.0000045","web_search":"0.01","input_cache_read":"0.000000075"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-08-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-mini-20260317/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":24.6,"coding_index":56.1,"agentic_index":19.7}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.4-mini:batch","canonical_slug":"openai/gpt-5.4-mini-20260317","hugging_face_id":"","name":"OpenAI: GPT-5.4 Mini (batch)","created":1773748178,"description":"GPT-5.4 mini brings the core capabilities of GPT-5.4 to a faster, more efficient model optimized for high-throughput workloads. It supports text and image inputs with strong performance across reasoning, coding,...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000375","completion":"0.00000225","web_search":"0.01","input_cache_read":"0.0000000375"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-08-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-mini-20260317/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":24.6,"coding_index":56.1,"agentic_index":19.7}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"mistralai/mistral-small-2603","canonical_slug":"mistralai/mistral-small-2603","hugging_face_id":"mistralai/Mistral-Small-4-119B-2603","name":"Mistral: Mistral Small 4","created":1773695685,"description":"Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000015"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-small-2603/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":11.5,"coding_index":26.6,"agentic_index":1.4}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["high","none"],"default_effort":"high"}},{"id":"mistralai/mistral-small-2603:batch","canonical_slug":"mistralai/mistral-small-2603","hugging_face_id":"mistralai/Mistral-Small-4-119B-2603","name":"Mistral: Mistral Small 4 (batch)","created":1773695685,"description":"Mistral Small 4 is the next major release in the Mistral Small family, unifying the capabilities of several flagship Mistral models into a single system. It combines strong reasoning from...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.0000003","input_cache_read":"0.0000000075"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-small-2603/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":11.5,"coding_index":26.6,"agentic_index":1.4}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["high","none"],"default_effort":"high"}},{"id":"z-ai/glm-5-turbo","canonical_slug":"z-ai/glm-5-turbo-20260315","hugging_face_id":"","name":"Z.ai: GLM 5 Turbo","created":1773583573,"description":"GLM-5 Turbo is a new model from Z.ai designed for fast inference and strong performance in agent-driven environments such as OpenClaw scenarios. It is deeply optimized for real-world agent workflows...","context_length":202752,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000012","completion":"0.000004","input_cache_read":"0.00000024"},"top_provider":{"context_length":202752,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":"2098-12-31","links":{"details":"/api/v1/models/z-ai/glm-5-turbo-20260315/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1278,"win_rate":56.9,"rank":23},{"arena":"models","category":"asciiart","elo":1170,"win_rate":49.4,"rank":32},{"arena":"models","category":"codecategories","elo":1281,"win_rate":54.7,"rank":24},{"arena":"models","category":"dataviz","elo":1280,"win_rate":55.4,"rank":18},{"arena":"models","category":"gamedev","elo":1275,"win_rate":54.7,"rank":27},{"arena":"models","category":"svg","elo":1232,"win_rate":55.9,"rank":19},{"arena":"models","category":"uicomponent","elo":1280,"win_rate":56.5,"rank":26},{"arena":"models","category":"website","elo":1278,"win_rate":54,"rank":27}]},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"nvidia/nemotron-3-super-120b-a12b","canonical_slug":"nvidia/nemotron-3-super-120b-a12b-20230311","hugging_face_id":"nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8","name":"NVIDIA: Nemotron 3 Super","created":1773245239,"description":"NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000008","completion":"0.00000045"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3-super-120b-a12b-20230311/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":13.6,"coding_index":37.7,"agentic_index":4.1}},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true,"supported_efforts":["medium","low"],"default_effort":"medium"}},{"id":"nvidia/nemotron-3-super-120b-a12b:free","canonical_slug":"nvidia/nemotron-3-super-120b-a12b-20230311","hugging_face_id":"nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-FP8","name":"NVIDIA: Nemotron 3 Super (free)","created":1773245239,"description":"NVIDIA Nemotron 3 Super is a 120B-parameter open hybrid MoE model, activating just 12B parameters for maximum compute efficiency and accuracy in complex multi-agent applications. Built on a hybrid Mamba-Transformer...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3-super-120b-a12b-20230311/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":13.6,"coding_index":37.7,"agentic_index":4.1}},"reasoning":{"mandatory":false,"default_enabled":true,"supports_max_tokens":true,"supported_efforts":["medium","low"],"default_effort":"medium"}},{"id":"bytedance-seed/seed-2.0-lite","canonical_slug":"bytedance-seed/seed-2.0-lite-20260309","hugging_face_id":null,"name":"ByteDance Seed: Seed-2.0-Lite","created":1773157231,"description":"Seed-2.0-Lite is a versatile, cost‑efficient enterprise workhorse that delivers strong multimodal and agent capabilities while offering noticeably lower latency, making it a practical default choice for most production workloads across...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.000002","overrides":[{"min_prompt_tokens":128000,"prompt":"0.0000005","completion":"0.000004"}]},"top_provider":{"context_length":262144,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/bytedance-seed/seed-2.0-lite-20260309/endpoints"},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"qwen/qwen3.5-9b","canonical_slug":"qwen/qwen3.5-9b-20260310","hugging_face_id":"Qwen/Qwen3.5-9B","name":"Qwen: Qwen3.5-9B","created":1773152396,"description":"Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.00000015"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-9b-20260310/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":28.7,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.5-9b:batch","canonical_slug":"qwen/qwen3.5-9b-20260310","hugging_face_id":"Qwen/Qwen3.5-9B","name":"Qwen: Qwen3.5-9B (batch)","created":1773152396,"description":"Qwen3.5-9B is a multimodal foundation model from the Qwen3.5 family, designed to deliver strong reasoning, coding, and visual understanding in an efficient 9B-parameter architecture. It uses a unified vision-language design...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000017","completion":"0.00000025"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-9b-20260310/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":28.7,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"openai/gpt-5.4-pro","canonical_slug":"openai/gpt-5.4-pro-20260305","hugging_face_id":"","name":"OpenAI: GPT-5.4 Pro","created":1772734366,"description":"GPT-5.4 Pro is OpenAI's most advanced model, building on GPT-5.4's unified architecture with enhanced reasoning capabilities for complex, high-stakes tasks. It features a 1M+ token context window (922K input, 128K...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00003","completion":"0.00018","web_search":"0.01","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00006","completion":"0.00027"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-pro-20260305/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium"],"default_effort":"medium"}},{"id":"openai/gpt-5.4-pro:batch","canonical_slug":"openai/gpt-5.4-pro-20260305","hugging_face_id":"","name":"OpenAI: GPT-5.4 Pro (batch)","created":1772734366,"description":"GPT-5.4 Pro is OpenAI's most advanced model, building on GPT-5.4's unified architecture with enhanced reasoning capabilities for complex, high-stakes tasks. It features a 1M+ token context window (922K input, 128K...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000015","completion":"0.00009","web_search":"0.01","overrides":[{"min_prompt_tokens":272000,"prompt":"0.00003","completion":"0.000135"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-pro-20260305/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium"],"default_effort":"medium"}},{"id":"openai/gpt-5.4","canonical_slug":"openai/gpt-5.4-20260305","hugging_face_id":"","name":"OpenAI: GPT-5.4","created":1772734352,"description":"GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.000015","web_search":"0.01","input_cache_read":"0.00000025","overrides":[{"min_prompt_tokens":272000,"prompt":"0.000005","completion":"0.0000225","input_cache_read":"0.0000005"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-20260305/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1130,"win_rate":42.3,"rank":74},{"arena":"models","category":"asciiart","elo":1216,"win_rate":55.4,"rank":23},{"arena":"models","category":"codecategories","elo":1227,"win_rate":52.5,"rank":44},{"arena":"models","category":"dataviz","elo":1250,"win_rate":56.6,"rank":31},{"arena":"models","category":"gamedev","elo":1261,"win_rate":57.6,"rank":30},{"arena":"models","category":"svg","elo":1219,"win_rate":57.8,"rank":22},{"arena":"models","category":"uicomponent","elo":1255,"win_rate":57.4,"rank":33},{"arena":"models","category":"website","elo":1232,"win_rate":52.5,"rank":46},{"arena":"agents","category":"androidnative","elo":1047,"win_rate":47.4,"rank":35},{"arena":"agents","category":"fullstack","elo":1026,"win_rate":40.8,"rank":39},{"arena":"agents","category":"godotgamedev","elo":1135,"win_rate":46.9,"rank":21},{"arena":"agents","category":"mobileapps","elo":1116,"win_rate":46.1,"rank":40},{"arena":"agents","category":"webapps","elo":1074,"win_rate":40.3,"rank":36}],"artificial_analysis":{"intelligence_index":null,"coding_index":71.1,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.4:batch","canonical_slug":"openai/gpt-5.4-20260305","hugging_face_id":"","name":"OpenAI: GPT-5.4 (batch)","created":1772734352,"description":"GPT-5.4 is OpenAI’s latest frontier model, unifying the Codex and GPT lines into a single system. It features a 1M+ token context window (922K input, 128K output) with support for...","context_length":1050000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.0000075","web_search":"0.01","input_cache_read":"0.000000125","overrides":[{"min_prompt_tokens":272000,"prompt":"0.0000025","completion":"0.00001125","input_cache_read":"0.00000025"}]},"top_provider":{"context_length":1050000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.4-20260305/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1130,"win_rate":42.3,"rank":74},{"arena":"models","category":"asciiart","elo":1216,"win_rate":55.4,"rank":23},{"arena":"models","category":"codecategories","elo":1227,"win_rate":52.5,"rank":44},{"arena":"models","category":"dataviz","elo":1250,"win_rate":56.6,"rank":31},{"arena":"models","category":"gamedev","elo":1261,"win_rate":57.6,"rank":30},{"arena":"models","category":"svg","elo":1219,"win_rate":57.8,"rank":22},{"arena":"models","category":"uicomponent","elo":1255,"win_rate":57.4,"rank":33},{"arena":"models","category":"website","elo":1232,"win_rate":52.5,"rank":46},{"arena":"agents","category":"androidnative","elo":1047,"win_rate":47.4,"rank":35},{"arena":"agents","category":"fullstack","elo":1026,"win_rate":40.8,"rank":39},{"arena":"agents","category":"godotgamedev","elo":1135,"win_rate":46.9,"rank":21},{"arena":"agents","category":"mobileapps","elo":1116,"win_rate":46.1,"rank":40},{"arena":"agents","category":"webapps","elo":1074,"win_rate":40.3,"rank":36}],"artificial_analysis":{"intelligence_index":null,"coding_index":71.1,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"inception/mercury-2","canonical_slug":"inception/mercury-2-20260304","hugging_face_id":null,"name":"Inception: Mercury 2","created":1772636275,"description":"Mercury 2 is an extremely fast reasoning LLM, and the first reasoning diffusion LLM (dLLM). Instead of generating tokens sequentially, Mercury 2 produces and refines multiple tokens in parallel, achieving...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.00000075","input_cache_read":"0.000000025"},"top_provider":{"context_length":128000,"max_completion_tokens":50000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools"],"default_parameters":{"temperature":0.75,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/inception/mercury-2-20260304/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1002,"win_rate":22.3,"rank":104},{"arena":"models","category":"asciiart","elo":1020,"win_rate":26.5,"rank":64},{"arena":"models","category":"codecategories","elo":1027,"win_rate":21.3,"rank":108},{"arena":"models","category":"dataviz","elo":1013,"win_rate":21.8,"rank":104},{"arena":"models","category":"gamedev","elo":1004,"win_rate":19.7,"rank":109},{"arena":"models","category":"svg","elo":1013,"win_rate":24.5,"rank":81},{"arena":"models","category":"uicomponent","elo":998,"win_rate":19.3,"rank":105},{"arena":"models","category":"website","elo":1037,"win_rate":21.6,"rank":112}],"artificial_analysis":{"intelligence_index":11.5,"coding_index":31.1,"agentic_index":4}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","none"],"default_effort":"medium"}},{"id":"google/gemini-3.1-flash-lite-preview","canonical_slug":"google/gemini-3.1-flash-lite-preview-20260303","hugging_face_id":"","name":"Google: Gemini 3.1 Flash Lite Preview","created":1772512673,"description":"Gemini 3.1 Flash Lite Preview is Google's high-efficiency model optimized for high-volume use cases. It outperforms Gemini 2.5 Flash Lite on overall quality and approaches Gemini 2.5 Flash performance across...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","video","file","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.0000015","image":"0.00000025","audio":"0.0000005","input_audio_cache":"0.00000005","web_search":"0.014","internal_reasoning":"0.0000015","input_cache_read":"0.000000025","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-flash-lite-preview-20260303/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1076,"win_rate":38.7,"rank":93},{"arena":"models","category":"asciiart","elo":1184,"win_rate":50.7,"rank":27},{"arena":"models","category":"codecategories","elo":1087,"win_rate":36.4,"rank":96},{"arena":"models","category":"dataviz","elo":1060,"win_rate":33.3,"rank":99},{"arena":"models","category":"gamedev","elo":1052,"win_rate":33.7,"rank":101},{"arena":"models","category":"svg","elo":1077,"win_rate":42.5,"rank":66},{"arena":"models","category":"uicomponent","elo":1087,"win_rate":37.7,"rank":91},{"arena":"models","category":"website","elo":1095,"win_rate":36.6,"rank":98}],"artificial_analysis":{"intelligence_index":16,"coding_index":34.7,"agentic_index":3.2}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"minimal"}},{"id":"bytedance-seed/seed-2.0-mini","canonical_slug":"bytedance-seed/seed-2.0-mini-20260224","hugging_face_id":"","name":"ByteDance Seed: Seed-2.0-Mini","created":1772131107,"description":"Seed-2.0-mini targets latency-sensitive, high-concurrency, and cost-sensitive scenarios, emphasizing fast response and flexible inference deployment. It delivers performance comparable to ByteDance-Seed-1.6, supports 256k context, four reasoning effort modes (minimal/low/medium/high), multimodal understanding,...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000004","overrides":[{"min_prompt_tokens":128000,"prompt":"0.0000002","completion":"0.0000008"}]},"top_provider":{"context_length":262144,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/bytedance-seed/seed-2.0-mini-20260224/endpoints"},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"google/gemini-3.1-flash-image-preview","canonical_slug":"google/gemini-3.1-flash-image-preview-20260226","hugging_face_id":"","name":"Google: Nano Banana 2 (Gemini 3.1 Flash Image Preview)","created":1772119558,"description":"Gemini 3.1 Flash Image Preview, a.k.a. \"Nano Banana 2,\" is Google’s latest state of the art image generation and editing model, delivering Pro-level visual quality at Flash speed. It combines...","context_length":65536,"architecture":{"modality":"text+image->text+image","input_modalities":["image","text"],"output_modalities":["image","text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.000003","image_output":"0.00006","web_search":"0.014"},"top_provider":{"context_length":65536,"max_completion_tokens":58982,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-flash-image-preview-20260226/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"graphicdesign","elo":1260,"win_rate":66.3,"rank":4},{"arena":"models","category":"image","elo":1279,"win_rate":65.1,"rank":4},{"arena":"models","category":"logo","elo":1263,"win_rate":62.9,"rank":4}]},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","minimal"],"default_effort":"minimal"}},{"id":"qwen/qwen3.5-35b-a3b","canonical_slug":"qwen/qwen3.5-35b-a3b-20260224","hugging_face_id":"Qwen/Qwen3.5-35B-A3B","name":"Qwen: Qwen3.5-35B-A3B","created":1772053822,"description":"The Qwen3.5 Series 35B-A3B is a native vision-language model designed with a hybrid architecture that integrates linear attention mechanisms and a sparse mixture-of-experts model, achieving higher inference efficiency. Its overall...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000001625","completion":"0.0000013"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-35b-a3b-20260224/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":37,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.5-27b","canonical_slug":"qwen/qwen3.5-27b-20260224","hugging_face_id":"Qwen/Qwen3.5-27B","name":"Qwen: Qwen3.5-27B","created":1772053810,"description":"The Qwen3.5 27B native vision-language Dense model incorporates a linear attention mechanism, delivering fast response times while balancing inference speed and performance. Its overall capabilities are comparable to those of...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.000000195","completion":"0.00000156"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.6,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-27b-20260224/endpoints"},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.5-122b-a10b","canonical_slug":"qwen/qwen3.5-122b-a10b-20260224","hugging_face_id":"Qwen/Qwen3.5-122B-A10B","name":"Qwen: Qwen3.5-122B-A10B","created":1772053789,"description":"The Qwen3.5 122B-A10B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. In terms of...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000026","completion":"0.00000208"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.6,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-122b-a10b-20260224/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":16.2,"coding_index":45.7,"agentic_index":9.6}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.5-flash-02-23","canonical_slug":"qwen/qwen3.5-flash-20260224","hugging_face_id":null,"name":"Qwen: Qwen3.5-Flash","created":1772053776,"description":"The Qwen3.5 native vision-language Flash models are built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. Compared to the...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.000000065","completion":"0.00000026"},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-flash-20260224/endpoints"},"reasoning":{"mandatory":false}},{"id":"google/gemini-3.1-pro-preview-customtools","canonical_slug":"google/gemini-3.1-pro-preview-customtools-20260219","hugging_face_id":null,"name":"Google: Gemini 3.1 Pro Preview Custom Tools","created":1772045923,"description":"Gemini 3.1 Pro Preview Custom Tools is a variant of Gemini 3.1 Pro that improves tool selection behavior by preventing overuse of a general bash tool when more efficient third-party...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","audio","image","video","file"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","image":"0.000002","audio":"0.000002","input_audio_cache":"0.0000002","web_search":"0.014","internal_reasoning":"0.000012","input_cache_read":"0.0000002","input_cache_write":"0.000000375","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000004","completion":"0.000018","audio":"0.000004","input_audio_cache":"0.0000004","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-pro-preview-customtools-20260219/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-5.3-codex","canonical_slug":"openai/gpt-5.3-codex-20260224","hugging_face_id":"","name":"OpenAI: GPT-5.3-Codex","created":1771959164,"description":"GPT-5.3-Codex is OpenAI’s most advanced agentic coding model, combining the frontier software engineering performance of GPT-5.2-Codex with the broader reasoning and professional knowledge capabilities of GPT-5.2. It achieves state-of-the-art results...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000175","completion":"0.000014","web_search":"0.01","input_cache_read":"0.000000175"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.3-codex-20260224/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1084,"win_rate":35.2,"rank":30},{"arena":"agents","category":"fullstack","elo":992,"win_rate":36.5,"rank":42},{"arena":"agents","category":"godotgamedev","elo":1124,"win_rate":45.1,"rank":23},{"arena":"agents","category":"mobileapps","elo":1083,"win_rate":41.4,"rank":42},{"arena":"agents","category":"webapps","elo":1048,"win_rate":36.9,"rank":38},{"arena":"models","category":"3d","elo":1036,"win_rate":35.3,"rank":98},{"arena":"models","category":"asciiart","elo":1169,"win_rate":51.2,"rank":34},{"arena":"models","category":"codecategories","elo":1160,"win_rate":47.2,"rank":72},{"arena":"models","category":"dataviz","elo":1180,"win_rate":50.6,"rank":64},{"arena":"models","category":"gamedev","elo":1184,"win_rate":51.3,"rank":58},{"arena":"models","category":"svg","elo":1154,"win_rate":54,"rank":48},{"arena":"models","category":"uicomponent","elo":1152,"win_rate":47.3,"rank":71},{"arena":"models","category":"website","elo":1175,"win_rate":48.6,"rank":71}]},"reasoning":{"mandatory":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"aion-labs/aion-2.0","canonical_slug":"aion-labs/aion-2.0-20260223","hugging_face_id":null,"name":"AionLabs: Aion-2.0","created":1771881306,"description":"Aion-2.0 is a variant of DeepSeek V3.2 optimized for immersive roleplaying and storytelling. It is particularly strong at introducing tension, crises, and conflict into stories, making narratives feel more engaging....","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000008","completion":"0.0000016","input_cache_read":"0.0000002"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/aion-labs/aion-2.0-20260223/endpoints"},"reasoning":{"mandatory":true}},{"id":"google/gemini-3.1-pro-preview","canonical_slug":"google/gemini-3.1-pro-preview-20260219","hugging_face_id":"","name":"Google: Gemini 3.1 Pro Preview","created":1771509627,"description":"Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["audio","file","image","text","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","image":"0.000002","audio":"0.000002","input_audio_cache":"0.0000002","web_search":"0.014","internal_reasoning":"0.000012","input_cache_read":"0.0000002","input_cache_write":"0.000000375","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000004","completion":"0.000018","audio":"0.000004","input_audio_cache":"0.0000004","input_cache_read":"0.0000004"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-pro-preview-20260219/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1114,"win_rate":44.2,"rank":22},{"arena":"agents","category":"agentichtmlslides","elo":1226,"win_rate":55.8,"rank":4},{"arena":"agents","category":"agenticslides","elo":1112,"win_rate":33.8,"rank":7},{"arena":"agents","category":"agenticslides(html)","elo":1219,"win_rate":54.4,"rank":4},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1107,"win_rate":33.9,"rank":7},{"arena":"agents","category":"androidnative","elo":1065,"win_rate":41.3,"rank":33},{"arena":"agents","category":"fullstack","elo":1078,"win_rate":41.3,"rank":31},{"arena":"agents","category":"godotgamedev","elo":1236,"win_rate":60,"rank":5},{"arena":"agents","category":"htmlslides","elo":1155,"win_rate":48.6,"rank":16},{"arena":"agents","category":"mobileapps","elo":1121,"win_rate":43.6,"rank":35},{"arena":"agents","category":"pptxslides","elo":1110,"win_rate":34.1,"rank":7},{"arena":"agents","category":"python-pptxslides","elo":1109,"win_rate":31.9,"rank":25},{"arena":"agents","category":"webapps","elo":1141,"win_rate":44.9,"rank":29},{"arena":"models","category":"3d","elo":1263,"win_rate":58.5,"rank":30},{"arena":"models","category":"asciiart","elo":1288,"win_rate":63.5,"rank":9},{"arena":"models","category":"codecategories","elo":1259,"win_rate":64.4,"rank":33},{"arena":"models","category":"dataviz","elo":1254,"win_rate":61.8,"rank":29},{"arena":"models","category":"gamedev","elo":1226,"win_rate":52.2,"rank":42},{"arena":"models","category":"svg","elo":1311,"win_rate":67.8,"rank":8},{"arena":"models","category":"uicomponent","elo":1295,"win_rate":69.4,"rank":18},{"arena":"models","category":"website","elo":1265,"win_rate":64.4,"rank":32}],"artificial_analysis":{"intelligence_index":30.4,"coding_index":68.8,"agentic_index":10.3}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"google/gemini-3.1-pro-preview:batch","canonical_slug":"google/gemini-3.1-pro-preview-20260219","hugging_face_id":"","name":"Google: Gemini 3.1 Pro Preview (batch)","created":1771509627,"description":"Gemini 3.1 Pro Preview is Google’s frontier reasoning model, delivering enhanced software engineering performance, improved agentic reliability, and more efficient token usage across complex workflows. Building on the multimodal foundation...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["audio","file","image","text","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000006","image":"0.000001","audio":"0.000001","web_search":"0.014","internal_reasoning":"0.000006","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000002","completion":"0.000009","audio":"0.000002"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3.1-pro-preview-20260219/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1114,"win_rate":44.2,"rank":22},{"arena":"agents","category":"agentichtmlslides","elo":1226,"win_rate":55.8,"rank":4},{"arena":"agents","category":"agenticslides","elo":1112,"win_rate":33.8,"rank":7},{"arena":"agents","category":"agenticslides(html)","elo":1219,"win_rate":54.4,"rank":4},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1107,"win_rate":33.9,"rank":7},{"arena":"agents","category":"androidnative","elo":1065,"win_rate":41.3,"rank":33},{"arena":"agents","category":"fullstack","elo":1078,"win_rate":41.3,"rank":31},{"arena":"agents","category":"godotgamedev","elo":1236,"win_rate":60,"rank":5},{"arena":"agents","category":"htmlslides","elo":1155,"win_rate":48.6,"rank":16},{"arena":"agents","category":"mobileapps","elo":1121,"win_rate":43.6,"rank":35},{"arena":"agents","category":"pptxslides","elo":1110,"win_rate":34.1,"rank":7},{"arena":"agents","category":"python-pptxslides","elo":1109,"win_rate":31.9,"rank":25},{"arena":"agents","category":"webapps","elo":1141,"win_rate":44.9,"rank":29},{"arena":"models","category":"3d","elo":1263,"win_rate":58.5,"rank":30},{"arena":"models","category":"asciiart","elo":1288,"win_rate":63.5,"rank":9},{"arena":"models","category":"codecategories","elo":1259,"win_rate":64.4,"rank":33},{"arena":"models","category":"dataviz","elo":1254,"win_rate":61.8,"rank":29},{"arena":"models","category":"gamedev","elo":1226,"win_rate":52.2,"rank":42},{"arena":"models","category":"svg","elo":1311,"win_rate":67.8,"rank":8},{"arena":"models","category":"uicomponent","elo":1295,"win_rate":69.4,"rank":18},{"arena":"models","category":"website","elo":1265,"win_rate":64.4,"rank":32}],"artificial_analysis":{"intelligence_index":30.4,"coding_index":68.8,"agentic_index":10.3}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"anthropic/claude-sonnet-4.6","canonical_slug":"anthropic/claude-4.6-sonnet-20260217","hugging_face_id":"","name":"Anthropic: Claude Sonnet 4.6","created":1771342990,"description":"Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000015","web_search":"0.01","input_cache_read":"0.0000003","input_cache_write":"0.00000375","input_cache_write_1h":"0.000006"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.6-sonnet-20260217/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1177,"win_rate":52.2,"rank":16},{"arena":"agents","category":"androidnative","elo":1211,"win_rate":62,"rank":13},{"arena":"agents","category":"fullstack","elo":1223,"win_rate":64.3,"rank":15},{"arena":"agents","category":"godotgamedev","elo":1226,"win_rate":60.6,"rank":6},{"arena":"agents","category":"mobileapps","elo":1241,"win_rate":63.6,"rank":11},{"arena":"agents","category":"webapps","elo":1205,"win_rate":56.9,"rank":22},{"arena":"models","category":"3d","elo":1263,"win_rate":57.6,"rank":29},{"arena":"models","category":"asciiart","elo":1251,"win_rate":60.1,"rank":16},{"arena":"models","category":"codecategories","elo":1291,"win_rate":59.4,"rank":19},{"arena":"models","category":"dataviz","elo":1294,"win_rate":58.2,"rank":14},{"arena":"models","category":"gamedev","elo":1282,"win_rate":58.8,"rank":25},{"arena":"models","category":"svg","elo":1222,"win_rate":58.7,"rank":20},{"arena":"models","category":"uicomponent","elo":1287,"win_rate":58.3,"rank":21},{"arena":"models","category":"website","elo":1297,"win_rate":60,"rank":14}],"artificial_analysis":{"intelligence_index":30.5,"coding_index":63,"agentic_index":33.1}},"reasoning":{"mandatory":false,"supported_efforts":["max","high","medium","low"],"default_effort":"medium"}},{"id":"anthropic/claude-sonnet-4.6:batch","canonical_slug":"anthropic/claude-4.6-sonnet-20260217","hugging_face_id":"","name":"Anthropic: Claude Sonnet 4.6 (batch)","created":1771342990,"description":"Sonnet 4.6 is Anthropic's most capable Sonnet-class model yet, with frontier performance across coding, agents, and professional work. It excels at iterative development, complex codebase navigation, end-to-end project management with...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000015","completion":"0.0000075","web_search":"0.01","input_cache_read":"0.00000015","input_cache_write":"0.000001875","input_cache_write_1h":"0.000003"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.6-sonnet-20260217/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1177,"win_rate":52.2,"rank":16},{"arena":"agents","category":"androidnative","elo":1211,"win_rate":62,"rank":13},{"arena":"agents","category":"fullstack","elo":1223,"win_rate":64.3,"rank":15},{"arena":"agents","category":"godotgamedev","elo":1226,"win_rate":60.6,"rank":6},{"arena":"agents","category":"mobileapps","elo":1241,"win_rate":63.6,"rank":11},{"arena":"agents","category":"webapps","elo":1205,"win_rate":56.9,"rank":22},{"arena":"models","category":"3d","elo":1263,"win_rate":57.6,"rank":29},{"arena":"models","category":"asciiart","elo":1251,"win_rate":60.1,"rank":16},{"arena":"models","category":"codecategories","elo":1291,"win_rate":59.4,"rank":19},{"arena":"models","category":"dataviz","elo":1294,"win_rate":58.2,"rank":14},{"arena":"models","category":"gamedev","elo":1282,"win_rate":58.8,"rank":25},{"arena":"models","category":"svg","elo":1222,"win_rate":58.7,"rank":20},{"arena":"models","category":"uicomponent","elo":1287,"win_rate":58.3,"rank":21},{"arena":"models","category":"website","elo":1297,"win_rate":60,"rank":14}],"artificial_analysis":{"intelligence_index":30.5,"coding_index":63,"agentic_index":33.1}},"reasoning":{"mandatory":false,"supported_efforts":["max","high","medium","low"],"default_effort":"medium"}},{"id":"qwen/qwen3.5-plus-02-15","canonical_slug":"qwen/qwen3.5-plus-20260216","hugging_face_id":"","name":"Qwen: Qwen3.5 Plus 2026-02-15","created":1771229416,"description":"The Qwen3.5 native vision-language series Plus models are built on a hybrid architecture that integrates linear attention mechanisms with sparse mixture-of-experts models, achieving higher inference efficiency. In a variety of...","context_length":1000000,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000026","completion":"0.00000156","overrides":[{"min_prompt_tokens":256000,"prompt":"0.000000325","completion":"0.00000195"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-plus-20260216/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1147,"win_rate":47.7,"rank":69},{"arena":"models","category":"asciiart","elo":1111,"win_rate":43.2,"rank":57},{"arena":"models","category":"codecategories","elo":1182,"win_rate":48.5,"rank":65},{"arena":"models","category":"dataviz","elo":1151,"win_rate":44.9,"rank":76},{"arena":"models","category":"gamedev","elo":1128,"win_rate":42.6,"rank":79},{"arena":"models","category":"svg","elo":1132,"win_rate":48.9,"rank":54},{"arena":"models","category":"uicomponent","elo":1193,"win_rate":52.2,"rank":56},{"arena":"models","category":"website","elo":1200,"win_rate":50,"rank":58}]},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3.5-397b-a17b","canonical_slug":"qwen/qwen3.5-397b-a17b-20260216","hugging_face_id":"Qwen/Qwen3.5-397B-A17B","name":"Qwen: Qwen3.5 397B A17B","created":1771223018,"description":"The Qwen3.5 series 397B-A17B native vision-language model is built on a hybrid architecture that integrates a linear attention mechanism with a sparse mixture-of-experts model, achieving higher inference efficiency. It delivers...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["text","image","video"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000055","completion":"0.0000035","input_cache_read":"0.000000225"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.6,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3.5-397b-a17b-20260216/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1189,"win_rate":56.7,"rank":52},{"arena":"models","category":"codecategories","elo":1195,"win_rate":52.6,"rank":56},{"arena":"models","category":"dataviz","elo":1191,"win_rate":53.2,"rank":58},{"arena":"models","category":"gamedev","elo":1163,"win_rate":50.1,"rank":64},{"arena":"models","category":"svg","elo":1164,"win_rate":56.1,"rank":45},{"arena":"models","category":"uicomponent","elo":1178,"win_rate":51.4,"rank":64},{"arena":"models","category":"website","elo":1203,"win_rate":52.5,"rank":56}],"artificial_analysis":{"intelligence_index":19.1,"coding_index":48.2,"agentic_index":10.6}},"reasoning":{"mandatory":false}},{"id":"minimax/minimax-m2.5","canonical_slug":"minimax/minimax-m2.5-20260211","hugging_face_id":"MiniMaxAI/MiniMax-M2.5","name":"MiniMax: MiniMax M2.5","created":1770908502,"description":"MiniMax-M2.5 is a SOTA large language model designed for real-world productivity. Trained in a diverse range of complex real-world digital working environments, M2.5 builds upon the coding expertise of M2.1...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000027","completion":"0.00000108","input_cache_read":"0.000000027"},"top_provider":{"context_length":200000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m2.5-20260211/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1198,"win_rate":57.6,"rank":48},{"arena":"models","category":"codecategories","elo":1222,"win_rate":56.8,"rank":46},{"arena":"models","category":"dataviz","elo":1187,"win_rate":51.2,"rank":59},{"arena":"models","category":"gamedev","elo":1199,"win_rate":55.5,"rank":50},{"arena":"models","category":"svg","elo":1174,"win_rate":54.5,"rank":40},{"arena":"models","category":"uicomponent","elo":1188,"win_rate":53.4,"rank":58},{"arena":"models","category":"website","elo":1235,"win_rate":57.5,"rank":44}]},"reasoning":{"mandatory":true}},{"id":"z-ai/glm-5","canonical_slug":"z-ai/glm-5-20260211","hugging_face_id":"zai-org/GLM-5","name":"Z.ai: GLM 5","created":1770829182,"description":"GLM-5 is Z.ai’s flagship open-source foundation model engineered for complex systems design and long-horizon agent workflows. Built for expert developers, it delivers production-grade performance on large-scale programming tasks, rivaling leading...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.00000192","input_cache_read":"0.00000012"},"top_provider":{"context_length":198000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-5-20260211/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1160,"win_rate":55.2,"rank":24},{"arena":"agents","category":"fullstack","elo":1132,"win_rate":51.7,"rank":27},{"arena":"agents","category":"godotgamedev","elo":1144,"win_rate":46.7,"rank":15},{"arena":"agents","category":"htmlslides","elo":1150,"win_rate":45.1,"rank":18},{"arena":"agents","category":"mobileapps","elo":1164,"win_rate":51.6,"rank":30},{"arena":"models","category":"3d","elo":1257,"win_rate":56.3,"rank":31},{"arena":"models","category":"asciiart","elo":1165,"win_rate":47.9,"rank":37},{"arena":"models","category":"codecategories","elo":1262,"win_rate":55.5,"rank":32},{"arena":"models","category":"dataviz","elo":1244,"win_rate":53,"rank":36},{"arena":"models","category":"gamedev","elo":1257,"win_rate":57.4,"rank":31},{"arena":"models","category":"svg","elo":1193,"win_rate":54.4,"rank":33},{"arena":"models","category":"uicomponent","elo":1246,"win_rate":53.6,"rank":39},{"arena":"models","category":"website","elo":1260,"win_rate":55,"rank":34}]},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"qwen/qwen3-max-thinking","canonical_slug":"qwen/qwen3-max-thinking-20260123","hugging_face_id":null,"name":"Qwen: Qwen3 Max Thinking","created":1770671901,"description":"Qwen3-Max-Thinking is the flagship reasoning model in the Qwen3 series, designed for high-stakes cognitive tasks that require deep, multi-step reasoning. By significantly scaling model capacity and reinforcement learning compute, it...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.00000078","completion":"0.0000039","overrides":[{"min_prompt_tokens":32000,"prompt":"0.00000156","completion":"0.0000078"},{"min_prompt_tokens":128000,"prompt":"0.00000195","completion":"0.00000975"}]},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-max-thinking-20260123/endpoints"},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-opus-4.6","canonical_slug":"anthropic/claude-4.6-opus-20260205","hugging_face_id":"","name":"Anthropic: Claude Opus 4.6","created":1770219050,"description":"Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.6-opus-20260205/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1215,"win_rate":50.3,"rank":8},{"arena":"agents","category":"androidnative","elo":1219,"win_rate":64.6,"rank":11},{"arena":"agents","category":"fullstack","elo":1228,"win_rate":60.9,"rank":13},{"arena":"agents","category":"mobileapps","elo":1238,"win_rate":60.1,"rank":12},{"arena":"agents","category":"webapps","elo":1219,"win_rate":57.2,"rank":17},{"arena":"models","category":"3d","elo":1303,"win_rate":62.5,"rank":15},{"arena":"models","category":"asciiart","elo":1272,"win_rate":61.9,"rank":13},{"arena":"models","category":"codecategories","elo":1304,"win_rate":61.2,"rank":14},{"arena":"models","category":"dataviz","elo":1296,"win_rate":58.7,"rank":12},{"arena":"models","category":"gamedev","elo":1301,"win_rate":61,"rank":15},{"arena":"models","category":"svg","elo":1253,"win_rate":60.9,"rank":13},{"arena":"models","category":"uicomponent","elo":1300,"win_rate":59.8,"rank":16},{"arena":"models","category":"website","elo":1304,"win_rate":61.2,"rank":13}]},"reasoning":{"mandatory":false,"default_enabled":false,"supports_max_tokens":true,"supported_efforts":["max","high","medium","low"],"default_effort":"high"}},{"id":"anthropic/claude-opus-4.6:batch","canonical_slug":"anthropic/claude-4.6-opus-20260205","hugging_face_id":"","name":"Anthropic: Claude Opus 4.6 (batch)","created":1770219050,"description":"Opus 4.6 is Anthropic’s strongest model for coding and long-running professional tasks. It is built for agents that operate across entire workflows rather than single prompts, making it especially effective...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.0000125","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.000003125","input_cache_write_1h":"0.000005"},"top_provider":{"context_length":1000000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.6-opus-20260205/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticgamedev","elo":1215,"win_rate":50.3,"rank":8},{"arena":"agents","category":"androidnative","elo":1219,"win_rate":64.6,"rank":11},{"arena":"agents","category":"fullstack","elo":1228,"win_rate":60.9,"rank":13},{"arena":"agents","category":"mobileapps","elo":1238,"win_rate":60.1,"rank":12},{"arena":"agents","category":"webapps","elo":1219,"win_rate":57.2,"rank":17},{"arena":"models","category":"3d","elo":1303,"win_rate":62.5,"rank":15},{"arena":"models","category":"asciiart","elo":1272,"win_rate":61.9,"rank":13},{"arena":"models","category":"codecategories","elo":1304,"win_rate":61.2,"rank":14},{"arena":"models","category":"dataviz","elo":1296,"win_rate":58.7,"rank":12},{"arena":"models","category":"gamedev","elo":1301,"win_rate":61,"rank":15},{"arena":"models","category":"svg","elo":1253,"win_rate":60.9,"rank":13},{"arena":"models","category":"uicomponent","elo":1300,"win_rate":59.8,"rank":16},{"arena":"models","category":"website","elo":1304,"win_rate":61.2,"rank":13}]},"reasoning":{"mandatory":false,"default_enabled":false,"supports_max_tokens":true,"supported_efforts":["max","high","medium","low"],"default_effort":"high"}},{"id":"qwen/qwen3-coder-next","canonical_slug":"qwen/qwen3-coder-next-2025-02-03","hugging_face_id":"Qwen/Qwen3-Coder-Next","name":"Qwen: Qwen3 Coder Next","created":1770164101,"description":"Qwen3-Coder-Next is an open-weight causal language model optimized for coding agents and local development workflows. It uses a sparse MoE design with 80B total parameters and only 3B activated per...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.00000012","completion":"0.0000008","input_cache_read":"0.00000007"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-coder-next-2025-02-03/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":10.1,"coding_index":36.2,"agentic_index":3.6}}},{"id":"openrouter/free","canonical_slug":"openrouter/free","hugging_face_id":"","name":"Free Models Router","created":1769917427,"description":"The simplest way to get free inference. openrouter/free is a router that selects free models at random from the models available on OpenRouter. The router smartly filters for models that...","context_length":200000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"0","completion":"0"},"top_provider":{"context_length":null,"max_completion_tokens":null,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_completion_tokens","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openrouter/free/endpoints"}},{"id":"stepfun/step-3.5-flash","canonical_slug":"stepfun/step-3.5-flash","hugging_face_id":"stepfun-ai/Step-3.5-Flash","name":"StepFun: Step 3.5 Flash","created":1769728337,"description":"Step 3.5 Flash is StepFun's most capable open-source foundation model. Built on a sparse Mixture of Experts (MoE) architecture, it selectively activates only 11B of its 196B parameters per token....","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000003"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/stepfun/step-3.5-flash/endpoints"},"reasoning":{"mandatory":true}},{"id":"moonshotai/kimi-k2.5","canonical_slug":"moonshotai/kimi-k2.5-0127","hugging_face_id":"moonshotai/Kimi-K2.5","name":"MoonshotAI: Kimi K2.5","created":1769487076,"description":"Kimi K2.5 is Moonshot AI's native multimodal model, delivering state-of-the-art visual coding capability and a self-directed agent swarm paradigm. Built on Kimi K2 with continued pretraining over approximately 15T mixed...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000045","completion":"0.00000225","input_cache_read":"0.00000007"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k2.5-0127/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1134,"win_rate":57.8,"rank":26},{"arena":"agents","category":"fullstack","elo":1123,"win_rate":54.5,"rank":28},{"arena":"agents","category":"godotgamedev","elo":1219,"win_rate":59.8,"rank":8},{"arena":"agents","category":"mobileapps","elo":1176,"win_rate":54.6,"rank":26},{"arena":"agents","category":"webapps","elo":1151,"win_rate":51.6,"rank":27},{"arena":"models","category":"3d","elo":1233,"win_rate":53.1,"rank":38},{"arena":"models","category":"asciiart","elo":1184,"win_rate":46.4,"rank":28},{"arena":"models","category":"codecategories","elo":1252,"win_rate":54.1,"rank":36},{"arena":"models","category":"dataviz","elo":1235,"win_rate":51.3,"rank":41},{"arena":"models","category":"gamedev","elo":1229,"win_rate":53.4,"rank":41},{"arena":"models","category":"svg","elo":1175,"win_rate":48.4,"rank":39},{"arena":"models","category":"uicomponent","elo":1252,"win_rate":53.6,"rank":37},{"arena":"models","category":"website","elo":1261,"win_rate":55.2,"rank":33}],"artificial_analysis":{"intelligence_index":null,"coding_index":46.8,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"upstage/solar-pro-3","canonical_slug":"upstage/solar-pro-3","hugging_face_id":"","name":"Upstage: Solar Pro 3","created":1769481200,"description":"Solar Pro 3 is Upstage's powerful Mixture-of-Experts (MoE) language model. With 102B total parameters and 12B active parameters per forward pass, it delivers exceptional performance while maintaining computational efficiency. Optimized...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000015"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","parallel_tool_calls","presence_penalty","reasoning","response_format","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/upstage/solar-pro-3/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":7.8,"coding_index":16.2,"agentic_index":1.4}},"reasoning":{"mandatory":false}},{"id":"minimax/minimax-m2-her","canonical_slug":"minimax/minimax-m2-her-20260123","hugging_face_id":"","name":"MiniMax: MiniMax M2-her","created":1769177239,"description":"MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in tone and personality, it supports rich message...","context_length":65536,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.00000003"},"top_provider":{"context_length":65536,"max_completion_tokens":2048,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","temperature","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m2-her-20260123/endpoints"}},{"id":"writer/palmyra-x5","canonical_slug":"writer/palmyra-x5-20250428","hugging_face_id":"","name":"Writer: Palmyra X5","created":1769003823,"description":"Palmyra X5 is Writer's most advanced model, purpose-built for building and scaling AI agents across the enterprise. It delivers industry-leading speed and efficiency on context windows up to 1 million...","context_length":1040000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.000006"},"top_provider":{"context_length":1040000,"max_completion_tokens":8192,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/writer/palmyra-x5-20250428/endpoints"}},{"id":"openai/gpt-audio","canonical_slug":"openai/gpt-audio","hugging_face_id":"","name":"OpenAI: GPT Audio","created":1768862569,"description":"The gpt-audio model is OpenAI's first generally available audio model. The new snapshot features an upgraded decoder for more natural sounding voices and maintains better voice consistency. Audio is priced...","context_length":128000,"architecture":{"modality":"text+audio->text+audio","input_modalities":["text","audio"],"output_modalities":["text","audio"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.00001","audio":"0.000032","audio_output":"0.000064"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-audio/endpoints"}},{"id":"openai/gpt-audio-mini","canonical_slug":"openai/gpt-audio-mini","hugging_face_id":"","name":"OpenAI: GPT Audio Mini","created":1768859419,"description":"A cost-efficient version of GPT Audio. The new snapshot features an upgraded decoder for more natural sounding voices and maintains better voice consistency. Input is priced at $0.60 per million...","context_length":128000,"architecture":{"modality":"text+audio->text+audio","input_modalities":["text","audio"],"output_modalities":["text","audio"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.0000024","audio":"0.0000006","audio_output":"0.0000024"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-audio-mini/endpoints"}},{"id":"z-ai/glm-4.7-flash","canonical_slug":"z-ai/glm-4.7-flash-20260119","hugging_face_id":"zai-org/GLM-4.7-Flash","name":"Z.ai: GLM 4.7 Flash","created":1768833913,"description":"As a 30B-class SOTA model, GLM-4.7-Flash offers a new option that balances performance and efficiency. It is further optimized for agentic coding use cases, strengthening coding capabilities, long-horizon task planning,...","context_length":200000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000000605","completion":"0.0000004"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-4.7-flash-20260119/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1149,"win_rate":51.2,"rank":68},{"arena":"models","category":"codecategories","elo":1193,"win_rate":53.1,"rank":57},{"arena":"models","category":"dataviz","elo":1139,"win_rate":45.3,"rank":83},{"arena":"models","category":"gamedev","elo":1158,"win_rate":49.7,"rank":68},{"arena":"models","category":"svg","elo":1062,"win_rate":44.2,"rank":69},{"arena":"models","category":"uicomponent","elo":1223,"win_rate":57.6,"rank":45},{"arena":"models","category":"website","elo":1207,"win_rate":54,"rank":53}]},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"openai/gpt-5.2-codex","canonical_slug":"openai/gpt-5.2-codex-20260114","hugging_face_id":"","name":"OpenAI: GPT-5.2-Codex","created":1768409315,"description":"GPT-5.2-Codex is an upgraded version of GPT-5.1-Codex optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....","context_length":400000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000175","completion":"0.000014","web_search":"0.01","input_cache_read":"0.000000175"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.2-codex-20260114/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1176,"win_rate":47.5,"rank":19},{"arena":"agents","category":"fullstack","elo":1002,"win_rate":37.2,"rank":41},{"arena":"agents","category":"godotgamedev","elo":1142,"win_rate":47.8,"rank":18},{"arena":"agents","category":"mobileapps","elo":1119,"win_rate":46.6,"rank":38},{"arena":"agents","category":"webapps","elo":1065,"win_rate":39.7,"rank":37}]},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"bytedance-seed/seed-1.6-flash","canonical_slug":"bytedance-seed/seed-1.6-flash-20250625","hugging_face_id":"","name":"ByteDance Seed: Seed 1.6 Flash","created":1766505011,"description":"Seed 1.6 Flash is an ultra-fast multimodal deep thinking model by ByteDance Seed, supporting both text and visual understanding. It features a 256k context window and can generate outputs of...","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.0000003","overrides":[{"min_prompt_tokens":128000,"prompt":"0.0000001","completion":"0.0000008"}]},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/bytedance-seed/seed-1.6-flash-20250625/endpoints"},"reasoning":{"mandatory":false}},{"id":"bytedance-seed/seed-1.6","canonical_slug":"bytedance-seed/seed-1.6-20250625","hugging_face_id":"","name":"ByteDance Seed: Seed 1.6","created":1766504997,"description":"Seed 1.6 is a general-purpose model released by the ByteDance Seed team. It incorporates multimodal capabilities and adaptive deep thinking with a 256K context window.","context_length":262144,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.000002","overrides":[{"min_prompt_tokens":128000,"prompt":"0.0000005","completion":"0.000004"}]},"top_provider":{"context_length":262144,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/bytedance-seed/seed-1.6-20250625/endpoints"},"reasoning":{"mandatory":false}},{"id":"minimax/minimax-m2.1","canonical_slug":"minimax/minimax-m2.1","hugging_face_id":"MiniMaxAI/MiniMax-M2.1","name":"MiniMax: MiniMax M2.1","created":1766454997,"description":"MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000012","input_cache_read":"0.00000003"},"top_provider":{"context_length":204800,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.9,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m2.1/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1193,"win_rate":57.5,"rank":51},{"arena":"models","category":"codecategories","elo":1206,"win_rate":55.3,"rank":49},{"arena":"models","category":"dataviz","elo":1222,"win_rate":57,"rank":45},{"arena":"models","category":"gamedev","elo":1157,"win_rate":50.4,"rank":69},{"arena":"models","category":"svg","elo":1156,"win_rate":55.4,"rank":47},{"arena":"models","category":"uicomponent","elo":1238,"win_rate":60.9,"rank":40},{"arena":"models","category":"website","elo":1214,"win_rate":55.4,"rank":49}]},"reasoning":{"mandatory":true}},{"id":"z-ai/glm-4.7","canonical_slug":"z-ai/glm-4.7-20251222","hugging_face_id":"zai-org/GLM-4.7","name":"Z.ai: GLM 4.7","created":1766378014,"description":"GLM-4.7 is Z.ai’s latest flagship model, featuring upgrades in two key areas: enhanced programming capabilities and more stable multi-step reasoning/execution. It demonstrates significant improvements in executing complex agent tasks while...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.00000175","input_cache_read":"0.00000008"},"top_provider":{"context_length":202752,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-4.7-20251222/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1129,"win_rate":56.2,"rank":27},{"arena":"agents","category":"fullstack","elo":1059,"win_rate":45.1,"rank":36},{"arena":"agents","category":"godotgamedev","elo":1058,"win_rate":35.8,"rank":27},{"arena":"agents","category":"mobileapps","elo":1134,"win_rate":48.9,"rank":32},{"arena":"models","category":"3d","elo":1219,"win_rate":54.3,"rank":43},{"arena":"models","category":"asciiart","elo":1183,"win_rate":48.1,"rank":29},{"arena":"models","category":"codecategories","elo":1232,"win_rate":54.8,"rank":43},{"arena":"models","category":"dataviz","elo":1211,"win_rate":51.2,"rank":50},{"arena":"models","category":"gamedev","elo":1211,"win_rate":55.2,"rank":46},{"arena":"models","category":"svg","elo":1168,"win_rate":54.3,"rank":42},{"arena":"models","category":"uicomponent","elo":1214,"win_rate":51.1,"rank":46},{"arena":"models","category":"website","elo":1238,"win_rate":55.3,"rank":42}],"artificial_analysis":{"intelligence_index":null,"coding_index":45.3,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"google/gemini-3-flash-preview","canonical_slug":"google/gemini-3-flash-preview-20251217","hugging_face_id":"","name":"Google: Gemini 3 Flash Preview","created":1765987078,"description":"Gemini 3 Flash Preview is a high speed, high value thinking model designed for agentic workflows, multi turn chat, and coding assistance. It delivers near Pro level reasoning and tool...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","file","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.000003","image":"0.0000005","audio":"0.000001","input_audio_cache":"0.0000001","web_search":"0.014","internal_reasoning":"0.000003","input_cache_read":"0.00000005","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3-flash-preview-20251217/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticslides","elo":1073,"win_rate":39.3,"rank":8},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1075,"win_rate":39.3,"rank":8},{"arena":"agents","category":"androidnative","elo":1066,"win_rate":48.1,"rank":32},{"arena":"agents","category":"fullstack","elo":1073,"win_rate":47.4,"rank":33},{"arena":"agents","category":"godotgamedev","elo":1161,"win_rate":50.6,"rank":13},{"arena":"agents","category":"mobileapps","elo":1119,"win_rate":46.6,"rank":37},{"arena":"agents","category":"python-pptxslides","elo":1036,"win_rate":38.3,"rank":27},{"arena":"agents","category":"webapps","elo":1126,"win_rate":48.5,"rank":32},{"arena":"models","category":"3d","elo":1212,"win_rate":62.7,"rank":45},{"arena":"models","category":"codecategories","elo":1204,"win_rate":57.6,"rank":51},{"arena":"models","category":"gamedev","elo":1189,"win_rate":58.3,"rank":57},{"arena":"models","category":"website","elo":1208,"win_rate":57,"rank":51}]},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"google/gemini-3-flash-preview:batch","canonical_slug":"google/gemini-3-flash-preview-20251217","hugging_face_id":"","name":"Google: Gemini 3 Flash Preview (batch)","created":1765987078,"description":"Gemini 3 Flash Preview is a high speed, high value thinking model designed for agentic workflows, multi turn chat, and coding assistance. It delivers near Pro level reasoning and tool...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","file","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.0000015","image":"0.00000025","audio":"0.0000005","web_search":"0.014","internal_reasoning":"0.0000015"},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3-flash-preview-20251217/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"agenticslides","elo":1073,"win_rate":39.3,"rank":8},{"arena":"agents","category":"agenticslides(python-pptx)","elo":1075,"win_rate":39.3,"rank":8},{"arena":"agents","category":"androidnative","elo":1066,"win_rate":48.1,"rank":32},{"arena":"agents","category":"fullstack","elo":1073,"win_rate":47.4,"rank":33},{"arena":"agents","category":"godotgamedev","elo":1161,"win_rate":50.6,"rank":13},{"arena":"agents","category":"mobileapps","elo":1119,"win_rate":46.6,"rank":37},{"arena":"agents","category":"python-pptxslides","elo":1036,"win_rate":38.3,"rank":27},{"arena":"agents","category":"webapps","elo":1126,"win_rate":48.5,"rank":32},{"arena":"models","category":"3d","elo":1212,"win_rate":62.7,"rank":45},{"arena":"models","category":"codecategories","elo":1204,"win_rate":57.6,"rank":51},{"arena":"models","category":"gamedev","elo":1189,"win_rate":58.3,"rank":57},{"arena":"models","category":"website","elo":1208,"win_rate":57,"rank":51}]},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"nvidia/nemotron-3-nano-30b-a3b","canonical_slug":"nvidia/nemotron-3-nano-30b-a3b","hugging_face_id":"nvidia/NVIDIA-Nemotron-3-Nano-30B-A3B-BF16","name":"NVIDIA: Nemotron 3 Nano 30B A3B","created":1765731275,"description":"NVIDIA Nemotron 3 Nano 30B A3B is a small language MoE model with highest compute efficiency and accuracy for developers to build specialized agentic AI systems. The model is fully...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000005","completion":"0.0000002","input_cache_read":"0.00000003"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/nvidia/nemotron-3-nano-30b-a3b/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":8.9,"coding_index":14.4,"agentic_index":1}},"reasoning":{"mandatory":false}},{"id":"openai/gpt-5.2-chat","canonical_slug":"openai/gpt-5.2-chat-20251211","hugging_face_id":"","name":"OpenAI: GPT-5.2 Chat","created":1765389783,"description":"GPT-5.2 Chat (AKA Instant) is the fast, lightweight member of the 5.2 family, optimized for low-latency chat while retaining strong general intelligence. It uses adaptive reasoning to selectively “think” on...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000175","completion":"0.000014","web_search":"0.01","input_cache_read":"0.000000175"},"top_provider":{"context_length":128000,"max_completion_tokens":32000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_completion_tokens","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.2-chat-20251211/endpoints"}},{"id":"openai/gpt-5.2-pro","canonical_slug":"openai/gpt-5.2-pro-20251211","hugging_face_id":"","name":"OpenAI: GPT-5.2 Pro","created":1765389780,"description":"GPT-5.2 Pro is OpenAI’s most advanced model, offering major improvements in agentic coding and long context performance over GPT-5 Pro. It is optimized for complex tasks that require step-by-step reasoning,...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000021","completion":"0.000168","web_search":"0.01"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.2-pro-20251211/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium"],"default_effort":"medium"}},{"id":"openai/gpt-5.2-pro:batch","canonical_slug":"openai/gpt-5.2-pro-20251211","hugging_face_id":"","name":"OpenAI: GPT-5.2 Pro (batch)","created":1765389780,"description":"GPT-5.2 Pro is OpenAI’s most advanced model, offering major improvements in agentic coding and long context performance over GPT-5 Pro. It is optimized for complex tasks that require step-by-step reasoning,...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000105","completion":"0.000084","web_search":"0.01"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.2-pro-20251211/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium"],"default_effort":"medium"}},{"id":"openai/gpt-5.2","canonical_slug":"openai/gpt-5.2-20251211","hugging_face_id":"","name":"OpenAI: GPT-5.2","created":1765389775,"description":"GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000175","completion":"0.000014","web_search":"0.01","input_cache_read":"0.000000175"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.2-20251211/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"website","elo":1207,"win_rate":54.4,"rank":52},{"arena":"agents","category":"androidnative","elo":1074,"win_rate":49.1,"rank":31},{"arena":"agents","category":"fullstack","elo":1052,"win_rate":44.3,"rank":37},{"arena":"agents","category":"godotgamedev","elo":1142,"win_rate":48.1,"rank":17},{"arena":"agents","category":"mobileapps","elo":1121,"win_rate":46.8,"rank":36},{"arena":"agents","category":"webapps","elo":1100,"win_rate":45.5,"rank":34},{"arena":"models","category":"3d","elo":1107,"win_rate":41.5,"rank":85},{"arena":"models","category":"codecategories","elo":1185,"win_rate":49.6,"rank":63},{"arena":"models","category":"dataviz","elo":1216,"win_rate":56.1,"rank":49},{"arena":"models","category":"gamedev","elo":1218,"win_rate":56,"rank":44},{"arena":"models","category":"uicomponent","elo":1205,"win_rate":51.3,"rank":51},{"arena":"models","category":"asciiart","elo":1170,"win_rate":50.7,"rank":33},{"arena":"models","category":"svg","elo":1164,"win_rate":53.7,"rank":44}]},"reasoning":{"mandatory":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"openai/gpt-5.2:batch","canonical_slug":"openai/gpt-5.2-20251211","hugging_face_id":"","name":"OpenAI: GPT-5.2 (batch)","created":1765389775,"description":"GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000875","completion":"0.000007","web_search":"0.01","input_cache_read":"0.0000000875"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.2-20251211/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"website","elo":1207,"win_rate":54.4,"rank":52},{"arena":"agents","category":"androidnative","elo":1074,"win_rate":49.1,"rank":31},{"arena":"agents","category":"fullstack","elo":1052,"win_rate":44.3,"rank":37},{"arena":"agents","category":"godotgamedev","elo":1142,"win_rate":48.1,"rank":17},{"arena":"agents","category":"mobileapps","elo":1121,"win_rate":46.8,"rank":36},{"arena":"agents","category":"webapps","elo":1100,"win_rate":45.5,"rank":34},{"arena":"models","category":"3d","elo":1107,"win_rate":41.5,"rank":85},{"arena":"models","category":"codecategories","elo":1185,"win_rate":49.6,"rank":63},{"arena":"models","category":"dataviz","elo":1216,"win_rate":56.1,"rank":49},{"arena":"models","category":"gamedev","elo":1218,"win_rate":56,"rank":44},{"arena":"models","category":"uicomponent","elo":1205,"win_rate":51.3,"rank":51},{"arena":"models","category":"asciiart","elo":1170,"win_rate":50.7,"rank":33},{"arena":"models","category":"svg","elo":1164,"win_rate":53.7,"rank":44}]},"reasoning":{"mandatory":false,"supported_efforts":["xhigh","high","medium","low","none"],"default_effort":"medium"}},{"id":"mistralai/devstral-2512","canonical_slug":"mistralai/devstral-2512","hugging_face_id":"mistralai/Devstral-2-123B-Instruct-2512","name":"Mistral: Devstral 2 2512","created":1765285419,"description":"Devstral 2 is a state-of-the-art open-source model by Mistral AI specializing in agentic coding. It is a 123B-parameter dense transformer model supporting a 256K context window. Devstral 2 supports exploring...","context_length":262144,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.000002","input_cache_read":"0.00000004"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/devstral-2512/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":9.4,"coding_index":31.3,"agentic_index":4.9}}},{"id":"relace/relace-search","canonical_slug":"relace/relace-search-20251208","hugging_face_id":null,"name":"Relace: Relace Search","created":1765213560,"description":"The relace-search model uses 4-12 `view_file` and `grep` tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search performs agentic...","context_length":256000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000003"},"top_provider":{"context_length":256000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","stop","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/relace/relace-search-20251208/endpoints"}},{"id":"z-ai/glm-4.6v","canonical_slug":"z-ai/glm-4.6-20251208","hugging_face_id":"zai-org/GLM-4.6V","name":"Z.ai: GLM 4.6V","created":1765207462,"description":"GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K tokens, processes complex page layouts...","context_length":131072,"architecture":{"modality":"text+image+video->text","input_modalities":["image","text","video"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000009","input_cache_read":"0.000000055"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.8,"top_p":0.6,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-4.6-20251208/endpoints"},"reasoning":{"mandatory":false}},{"id":"openrouter/bodybuilder","canonical_slug":"openrouter/bodybuilder","hugging_face_id":"","name":"Body Builder (beta)","created":1764903653,"description":"Transform your natural language requests into structured OpenRouter API request objects. Describe what you want to accomplish with AI models, and Body Builder will construct the appropriate API calls. Example:...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"-1","completion":"-1"},"top_provider":{"context_length":null,"max_completion_tokens":null,"is_moderated":false},"per_request_limits":null,"supported_parameters":[],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openrouter/bodybuilder/endpoints"}},{"id":"openai/gpt-5.1-codex-max","canonical_slug":"openai/gpt-5.1-codex-max-20251204","hugging_face_id":"","name":"OpenAI: GPT-5.1-Codex-Max","created":1764878934,"description":"GPT-5.1-Codex-Max is OpenAI’s latest agentic coding model, designed for long-running, high-context software development tasks. It is based on an updated version of the 5.1 reasoning stack and trained on agentic...","context_length":400000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00001","web_search":"0.01","input_cache_read":"0.000000125"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.1-codex-max-20251204/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["xhigh","high","medium","low"],"default_effort":"medium"}},{"id":"amazon/nova-2-lite-v1","canonical_slug":"amazon/nova-2-lite-v1","hugging_face_id":"","name":"Amazon: Nova 2 Lite","created":1764696672,"description":"Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text. Nova 2 Lite demonstrates standout capabilities in processing...","context_length":1000000,"architecture":{"modality":"text+image+file+video->text","input_modalities":["text","image","video","file"],"output_modalities":["text"],"tokenizer":"Nova","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000025"},"top_provider":{"context_length":1000000,"max_completion_tokens":65535,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/amazon/nova-2-lite-v1/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":23,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"mistralai/ministral-14b-2512","canonical_slug":"mistralai/ministral-14b-2512","hugging_face_id":"mistralai/Ministral-3-14B-Instruct-2512","name":"Mistral: Ministral 3 14B 2512","created":1764681735,"description":"The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000002","input_cache_read":"0.00000002"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/ministral-14b-2512/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1018,"win_rate":39.6,"rank":102},{"arena":"models","category":"codecategories","elo":1080,"win_rate":44,"rank":98},{"arena":"models","category":"gamedev","elo":1061,"win_rate":43.6,"rank":98},{"arena":"models","category":"website","elo":1094,"win_rate":44.8,"rank":99}],"artificial_analysis":{"intelligence_index":6,"coding_index":14.4,"agentic_index":1.1}}},{"id":"mistralai/ministral-8b-2512","canonical_slug":"mistralai/ministral-8b-2512","hugging_face_id":"mistralai/Ministral-3-8B-Instruct-2512","name":"Mistral: Ministral 3 8B 2512","created":1764681654,"description":"A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.00000015","input_cache_read":"0.000000015"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/ministral-8b-2512/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1060,"win_rate":46.2,"rank":96},{"arena":"models","category":"codecategories","elo":1068,"win_rate":42.9,"rank":99},{"arena":"models","category":"gamedev","elo":1013,"win_rate":38.7,"rank":108},{"arena":"models","category":"website","elo":1077,"win_rate":42.9,"rank":101}],"artificial_analysis":{"intelligence_index":5.5,"coding_index":9.7,"agentic_index":0.6}}},{"id":"mistralai/ministral-8b-2512:batch","canonical_slug":"mistralai/ministral-8b-2512","hugging_face_id":"mistralai/Ministral-3-8B-Instruct-2512","name":"Mistral: Ministral 3 8B 2512 (batch)","created":1764681654,"description":"A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.000000075","input_cache_read":"0.0000000075"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/ministral-8b-2512/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1060,"win_rate":46.2,"rank":96},{"arena":"models","category":"codecategories","elo":1068,"win_rate":42.9,"rank":99},{"arena":"models","category":"gamedev","elo":1013,"win_rate":38.7,"rank":108},{"arena":"models","category":"website","elo":1077,"win_rate":42.9,"rank":101}],"artificial_analysis":{"intelligence_index":5.5,"coding_index":9.7,"agentic_index":0.6}}},{"id":"mistralai/ministral-3b-2512","canonical_slug":"mistralai/ministral-3b-2512","hugging_face_id":"mistralai/Ministral-3-3B-Instruct-2512","name":"Mistral: Ministral 3 3B 2512","created":1764681560,"description":"The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000001","input_cache_read":"0.00000001"},"top_provider":{"context_length":131072,"max_completion_tokens":104857,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/ministral-3b-2512/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":991,"win_rate":35.9,"rank":107},{"arena":"models","category":"codecategories","elo":1025,"win_rate":37.3,"rank":109},{"arena":"models","category":"gamedev","elo":971,"win_rate":33,"rank":115},{"arena":"models","category":"website","elo":1040,"win_rate":38.2,"rank":110}],"artificial_analysis":{"intelligence_index":4.8,"coding_index":4.8,"agentic_index":0.8}}},{"id":"mistralai/mistral-large-2512","canonical_slug":"mistralai/mistral-large-2512","hugging_face_id":"","name":"Mistral: Mistral Large 3 2512","created":1764624472,"description":"Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.","context_length":262144,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.0000015","input_cache_read":"0.00000005"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.0645,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-large-2512/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1127,"win_rate":46.9,"rank":77},{"arena":"models","category":"asciiart","elo":1086,"win_rate":40.3,"rank":61},{"arena":"models","category":"codecategories","elo":1157,"win_rate":47.6,"rank":75},{"arena":"models","category":"dataviz","elo":1152,"win_rate":45.7,"rank":75},{"arena":"models","category":"gamedev","elo":1103,"win_rate":41.5,"rank":87},{"arena":"models","category":"svg","elo":1017,"win_rate":38,"rank":79},{"arena":"models","category":"uicomponent","elo":1117,"win_rate":43,"rank":81},{"arena":"models","category":"website","elo":1175,"win_rate":49.5,"rank":72}],"artificial_analysis":{"intelligence_index":9.7,"coding_index":20.1,"agentic_index":2.4}}},{"id":"mistralai/mistral-large-2512:batch","canonical_slug":"mistralai/mistral-large-2512","hugging_face_id":"","name":"Mistral: Mistral Large 3 2512 (batch)","created":1764624472,"description":"Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.","context_length":262144,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.00000075","input_cache_read":"0.000000025"},"top_provider":{"context_length":262144,"max_completion_tokens":209715,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.0645,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-large-2512/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1127,"win_rate":46.9,"rank":77},{"arena":"models","category":"asciiart","elo":1086,"win_rate":40.3,"rank":61},{"arena":"models","category":"codecategories","elo":1157,"win_rate":47.6,"rank":75},{"arena":"models","category":"dataviz","elo":1152,"win_rate":45.7,"rank":75},{"arena":"models","category":"gamedev","elo":1103,"win_rate":41.5,"rank":87},{"arena":"models","category":"svg","elo":1017,"win_rate":38,"rank":79},{"arena":"models","category":"uicomponent","elo":1117,"win_rate":43,"rank":81},{"arena":"models","category":"website","elo":1175,"win_rate":49.5,"rank":72}],"artificial_analysis":{"intelligence_index":9.7,"coding_index":20.1,"agentic_index":2.4}}},{"id":"deepseek/deepseek-v3.2","canonical_slug":"deepseek/deepseek-v3.2-20251201","hugging_face_id":"deepseek-ai/DeepSeek-V3.2","name":"DeepSeek: DeepSeek V3.2","created":1764594642,"description":"DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.000000269","completion":"0.0000004","input_cache_read":"0.0000001345"},"top_provider":{"context_length":163840,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v3.2-20251201/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1161,"win_rate":49.4,"rank":63},{"arena":"models","category":"asciiart","elo":1100,"win_rate":40.5,"rank":59},{"arena":"models","category":"codecategories","elo":1179,"win_rate":49.3,"rank":68},{"arena":"models","category":"dataviz","elo":1174,"win_rate":48.2,"rank":67},{"arena":"models","category":"gamedev","elo":1154,"win_rate":46.5,"rank":71},{"arena":"models","category":"svg","elo":1057,"win_rate":40.8,"rank":70},{"arena":"models","category":"uicomponent","elo":1164,"win_rate":46.8,"rank":67},{"arena":"models","category":"website","elo":1187,"win_rate":50.2,"rank":66}],"artificial_analysis":{"intelligence_index":null,"coding_index":44.2,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":false}},{"id":"anthropic/claude-opus-4.5","canonical_slug":"anthropic/claude-4.5-opus-20251124","hugging_face_id":"","name":"Anthropic: Claude Opus 4.5","created":1764010580,"description":"Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000025","web_search":"0.01","input_cache_read":"0.0000005","input_cache_write":"0.00000625","input_cache_write_1h":"0.00001"},"top_provider":{"context_length":200000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.5-opus-20251124/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1242,"win_rate":58.5,"rank":33},{"arena":"models","category":"asciiart","elo":1207,"win_rate":54.8,"rank":24},{"arena":"models","category":"codecategories","elo":1257,"win_rate":59.5,"rank":34},{"arena":"models","category":"dataviz","elo":1258,"win_rate":58.5,"rank":27},{"arena":"models","category":"gamedev","elo":1252,"win_rate":59.3,"rank":32},{"arena":"models","category":"svg","elo":1205,"win_rate":58.7,"rank":30},{"arena":"models","category":"uicomponent","elo":1253,"win_rate":58.1,"rank":34},{"arena":"models","category":"website","elo":1259,"win_rate":59.7,"rank":35},{"arena":"agents","category":"androidnative","elo":1192,"win_rate":65.5,"rank":15},{"arena":"agents","category":"fullstack","elo":1166,"win_rate":60.2,"rank":24},{"arena":"agents","category":"mobileapps","elo":1218,"win_rate":60.7,"rank":15},{"arena":"agents","category":"webapps","elo":1174,"win_rate":55.1,"rank":25}]},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-opus-4.5:batch","canonical_slug":"anthropic/claude-4.5-opus-20251124","hugging_face_id":"","name":"Anthropic: Claude Opus 4.5 (batch)","created":1764010580,"description":"Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["file","image","text"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.0000125","web_search":"0.01","input_cache_read":"0.00000025","input_cache_write":"0.000003125","input_cache_write_1h":"0.000005"},"top_provider":{"context_length":200000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","verbosity"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.5-opus-20251124/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1242,"win_rate":58.5,"rank":33},{"arena":"models","category":"asciiart","elo":1207,"win_rate":54.8,"rank":24},{"arena":"models","category":"codecategories","elo":1257,"win_rate":59.5,"rank":34},{"arena":"models","category":"dataviz","elo":1258,"win_rate":58.5,"rank":27},{"arena":"models","category":"gamedev","elo":1252,"win_rate":59.3,"rank":32},{"arena":"models","category":"svg","elo":1205,"win_rate":58.7,"rank":30},{"arena":"models","category":"uicomponent","elo":1253,"win_rate":58.1,"rank":34},{"arena":"models","category":"website","elo":1259,"win_rate":59.7,"rank":35},{"arena":"agents","category":"androidnative","elo":1192,"win_rate":65.5,"rank":15},{"arena":"agents","category":"fullstack","elo":1166,"win_rate":60.2,"rank":24},{"arena":"agents","category":"mobileapps","elo":1218,"win_rate":60.7,"rank":15},{"arena":"agents","category":"webapps","elo":1174,"win_rate":55.1,"rank":25}]},"reasoning":{"mandatory":false}},{"id":"google/gemini-3-pro-image-preview","canonical_slug":"google/gemini-3-pro-image-preview-20251120","hugging_face_id":"","name":"Google: Nano Banana Pro (Gemini 3 Pro Image Preview)","created":1763653797,"description":"Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and...","context_length":65536,"architecture":{"modality":"text+image->text+image","input_modalities":["image","text"],"output_modalities":["image","text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000012","image":"0.000002","image_output":"0.00012","audio":"0.000002","input_audio_cache":"0.0000002","web_search":"0.014","internal_reasoning":"0.000012","input_cache_read":"0.0000002","input_cache_write":"0.000000375"},"top_provider":{"context_length":65536,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-3-pro-image-preview-20251120/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"graphicdesign","elo":1250,"win_rate":65.8,"rank":5},{"arena":"models","category":"image","elo":1246,"win_rate":62.1,"rank":5},{"arena":"models","category":"logo","elo":1241,"win_rate":61,"rank":6},{"arena":"models","category":"imageediting","elo":1254,"win_rate":66,"rank":3}]},"reasoning":{"mandatory":true}},{"id":"openai/gpt-5.1","canonical_slug":"openai/gpt-5.1-20251113","hugging_face_id":"","name":"OpenAI: GPT-5.1","created":1763060305,"description":"GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00001","web_search":"0.01","input_cache_read":"0.000000125"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.1-20251113/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1089,"win_rate":43.7,"rank":90},{"arena":"models","category":"asciiart","elo":1136,"win_rate":48.6,"rank":50},{"arena":"models","category":"codecategories","elo":1186,"win_rate":53,"rank":62},{"arena":"models","category":"dataviz","elo":1220,"win_rate":58,"rank":46},{"arena":"models","category":"gamedev","elo":1201,"win_rate":55.9,"rank":49},{"arena":"models","category":"svg","elo":1174,"win_rate":57.4,"rank":41},{"arena":"models","category":"uicomponent","elo":1181,"win_rate":52.9,"rank":60},{"arena":"models","category":"website","elo":1199,"win_rate":54,"rank":60}],"artificial_analysis":{"intelligence_index":null,"coding_index":49.4,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","none"],"default_effort":"none"}},{"id":"openai/gpt-5.1:batch","canonical_slug":"openai/gpt-5.1-20251113","hugging_face_id":"","name":"OpenAI: GPT-5.1 (batch)","created":1763060305,"description":"GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000625","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000000625"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.1-20251113/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1089,"win_rate":43.7,"rank":90},{"arena":"models","category":"asciiart","elo":1136,"win_rate":48.6,"rank":50},{"arena":"models","category":"codecategories","elo":1186,"win_rate":53,"rank":62},{"arena":"models","category":"dataviz","elo":1220,"win_rate":58,"rank":46},{"arena":"models","category":"gamedev","elo":1201,"win_rate":55.9,"rank":49},{"arena":"models","category":"svg","elo":1174,"win_rate":57.4,"rank":41},{"arena":"models","category":"uicomponent","elo":1181,"win_rate":52.9,"rank":60},{"arena":"models","category":"website","elo":1199,"win_rate":54,"rank":60}],"artificial_analysis":{"intelligence_index":null,"coding_index":49.4,"agentic_index":null}},"reasoning":{"mandatory":false,"default_enabled":true,"supported_efforts":["high","medium","low","none"],"default_effort":"none"}},{"id":"openai/gpt-5.1-codex","canonical_slug":"openai/gpt-5.1-codex-20251113","hugging_face_id":"","name":"OpenAI: GPT-5.1-Codex","created":1763060298,"description":"GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....","context_length":400000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00001","web_search":"0.01","input_cache_read":"0.00000013"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.1-codex-20251113/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"fullstack","elo":1086,"win_rate":44.5,"rank":30},{"arena":"agents","category":"mobileapps","elo":1191,"win_rate":53.4,"rank":22},{"arena":"agents","category":"webapps","elo":1033,"win_rate":44,"rank":39},{"arena":"models","category":"codecategories","elo":1168,"win_rate":54.6,"rank":70},{"arena":"models","category":"dataviz","elo":1229,"win_rate":55.9,"rank":44},{"arena":"models","category":"gamedev","elo":1161,"win_rate":50.5,"rank":66},{"arena":"models","category":"uicomponent","elo":1252,"win_rate":60,"rank":36},{"arena":"models","category":"website","elo":1174,"win_rate":56.1,"rank":73}]},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-5.1-codex-mini","canonical_slug":"openai/gpt-5.1-codex-mini-20251113","hugging_face_id":"","name":"OpenAI: GPT-5.1-Codex-Mini","created":1763057820,"description":"GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex","context_length":400000,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.000002","web_search":"0.01","input_cache_read":"0.00000003"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5.1-codex-mini-20251113/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1016,"win_rate":32.8,"rank":103},{"arena":"models","category":"asciiart","elo":1122,"win_rate":43,"rank":54},{"arena":"models","category":"codecategories","elo":1109,"win_rate":41.5,"rank":92},{"arena":"models","category":"dataviz","elo":1112,"win_rate":40.6,"rank":90},{"arena":"models","category":"gamedev","elo":1115,"win_rate":43.4,"rank":85},{"arena":"models","category":"svg","elo":1003,"win_rate":35.3,"rank":82},{"arena":"models","category":"uicomponent","elo":1096,"win_rate":41,"rank":88},{"arena":"models","category":"website","elo":1123,"win_rate":42.8,"rank":92}]},"reasoning":{"mandatory":false,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"moonshotai/kimi-k2-thinking","canonical_slug":"moonshotai/kimi-k2-thinking-20251106","hugging_face_id":"moonshotai/Kimi-K2-Thinking","name":"MoonshotAI: Kimi K2 Thinking","created":1762440622,"description":"Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.0000025","input_cache_read":"0.00000015"},"top_provider":{"context_length":262144,"max_completion_tokens":98304,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k2-thinking-20251106/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"website","elo":1125,"win_rate":48.8,"rank":91}],"artificial_analysis":{"intelligence_index":null,"coding_index":21,"agentic_index":null}},"reasoning":{"mandatory":true,"default_enabled":true}},{"id":"amazon/nova-premier-v1","canonical_slug":"amazon/nova-premier-v1","hugging_face_id":"","name":"Amazon: Nova Premier 1.0","created":1761950332,"description":"Amazon Nova Premier is the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models.","context_length":1000000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Nova","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.0000125","input_cache_read":"0.000000625"},"top_provider":{"context_length":1000000,"max_completion_tokens":32000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/amazon/nova-premier-v1/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"website","elo":848,"win_rate":26.2,"rank":127}]}},{"id":"perplexity/sonar-pro-search","canonical_slug":"perplexity/sonar-pro-search","hugging_face_id":"","name":"Perplexity: Sonar Pro Search","created":1761854366,"description":"Exclusively available on the OpenRouter API, Sonar Pro's new Pro Search mode is Perplexity's most advanced agentic search system. It is designed for deeper reasoning and analysis. Pricing is based...","context_length":200000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000015","web_search":"0.018"},"top_provider":{"context_length":200000,"max_completion_tokens":8000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","structured_outputs","temperature","top_k","top_p","web_search_options"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/perplexity/sonar-pro-search/endpoints"},"reasoning":{"mandatory":true}},{"id":"mistralai/voxtral-small-24b-2507","canonical_slug":"mistralai/voxtral-small-24b-2507","hugging_face_id":"mistralai/Voxtral-Small-24B-2507","name":"Mistral: Voxtral Small 24B 2507","created":1761835144,"description":"Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...","context_length":32768,"architecture":{"modality":"text+file+audio->text","input_modalities":["text","audio","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000003","audio":"0.0001","input_cache_read":"0.00000001"},"top_provider":{"context_length":32768,"max_completion_tokens":26214,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.2,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/mistralai/voxtral-small-24b-2507/endpoints"}},{"id":"openai/gpt-oss-safeguard-20b","canonical_slug":"openai/gpt-oss-safeguard-20b","hugging_face_id":"openai/gpt-oss-safeguard-20b","name":"OpenAI: gpt-oss-safeguard-20b","created":1761752836,"description":"gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.0000003","input_cache_read":"0.0000000375"},"top_provider":{"context_length":131072,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-oss-safeguard-20b/endpoints"},"reasoning":{"mandatory":true}},{"id":"minimax/minimax-m2","canonical_slug":"minimax/minimax-m2","hugging_face_id":"MiniMaxAI/MiniMax-M2","name":"MiniMax: MiniMax M2","created":1761252093,"description":"MiniMax-M2 is a compact, high-efficiency large language model optimized for end-to-end coding and agentic workflows. With 10 billion activated parameters (230 billion total), it delivers near-frontier intelligence across general reasoning,...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000255","completion":"0.00000102"},"top_provider":{"context_length":204800,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m2/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1126,"win_rate":48.3,"rank":78},{"arena":"models","category":"codecategories","elo":1150,"win_rate":48.1,"rank":81},{"arena":"models","category":"dataviz","elo":1156,"win_rate":50,"rank":74},{"arena":"models","category":"gamedev","elo":1141,"win_rate":48.1,"rank":75},{"arena":"models","category":"svg","elo":1125,"win_rate":55.3,"rank":55},{"arena":"models","category":"uicomponent","elo":1152,"win_rate":49.2,"rank":72},{"arena":"models","category":"website","elo":1155,"win_rate":48,"rank":80}]},"reasoning":{"mandatory":true}},{"id":"qwen/qwen3-vl-32b-instruct","canonical_slug":"qwen/qwen3-vl-32b-instruct","hugging_face_id":"Qwen/Qwen3-VL-32B-Instruct","name":"Qwen: Qwen3 VL 32B Instruct","created":1761231332,"description":"Qwen3-VL-32B-Instruct is a large-scale multimodal vision-language model designed for high-precision understanding and reasoning across text, images, and video. With 32 billion parameters, it combines deep visual perception with advanced text...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.000000104","completion":"0.000000416"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.7,"top_p":0.8,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":1},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-32b-instruct/endpoints"}},{"id":"ibm-granite/granite-4.0-h-micro","canonical_slug":"ibm-granite/granite-4.0-h-micro","hugging_face_id":"ibm-granite/granite-4.0-h-micro","name":"IBM: Granite 4.0 Micro","created":1760927695,"description":"Granite-4.0-H-Micro is a 3B parameter from the Granite 4 family of models. These models are the latest in a series of models released by IBM. They are fine-tuned for long...","context_length":131000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000000017","completion":"0.000000112"},"top_provider":{"context_length":131000,"max_completion_tokens":117900,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","temperature","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/ibm-granite/granite-4.0-h-micro/endpoints"}},{"id":"openai/gpt-5-image-mini","canonical_slug":"openai/gpt-5-image-mini","hugging_face_id":"","name":"OpenAI: GPT-5 Image Mini","created":1760624583,"description":"GPT-5 Image Mini combines OpenAI's advanced language capabilities, powered by [GPT-5 Mini](https://openrouter.ai/openai/gpt-5-mini), with GPT Image 1 Mini for efficient image generation. This natively multimodal model features superior instruction following, text...","context_length":400000,"architecture":{"modality":"text+image+file->text+image","input_modalities":["file","image","text"],"output_modalities":["image","text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.000002","image_output":"0.000008","web_search":"0.01","input_cache_read":"0.00000025"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-image-mini/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"graphicdesign","elo":1164,"win_rate":47.7,"rank":11},{"arena":"models","category":"image","elo":1186,"win_rate":51,"rank":11},{"arena":"models","category":"logo","elo":1209,"win_rate":51.4,"rank":8}]},"reasoning":{"mandatory":true}},{"id":"anthropic/claude-haiku-4.5","canonical_slug":"anthropic/claude-4.5-haiku-20251001","hugging_face_id":"","name":"Anthropic: Claude Haiku 4.5","created":1760547638,"description":"Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000001","input_cache_write":"0.00000125","input_cache_write_1h":"0.000002"},"top_provider":{"context_length":200000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.5-haiku-20251001/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1100,"win_rate":41.1,"rank":88},{"arena":"models","category":"asciiart","elo":1158,"win_rate":49.3,"rank":40},{"arena":"models","category":"codecategories","elo":1130,"win_rate":44.9,"rank":85},{"arena":"models","category":"dataviz","elo":1140,"win_rate":45.7,"rank":82},{"arena":"models","category":"gamedev","elo":1120,"win_rate":44.7,"rank":81},{"arena":"models","category":"svg","elo":1050,"win_rate":39.1,"rank":72},{"arena":"models","category":"uicomponent","elo":1115,"win_rate":42.6,"rank":82},{"arena":"models","category":"website","elo":1135,"win_rate":45.1,"rank":86}],"artificial_analysis":{"intelligence_index":17.6,"coding_index":43.9,"agentic_index":10.3}},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-haiku-4.5:batch","canonical_slug":"anthropic/claude-4.5-haiku-20251001","hugging_face_id":"","name":"Anthropic: Claude Haiku 4.5 (batch)","created":1760547638,"description":"Claude Haiku 4.5 is Anthropic’s fastest and most efficient model, delivering near-frontier intelligence at a fraction of the cost and latency of larger Claude models. Matching Claude Sonnet 4’s performance...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.0000025","web_search":"0.01","input_cache_read":"0.00000005","input_cache_write":"0.000000625","input_cache_write_1h":"0.000001"},"top_provider":{"context_length":200000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.5-haiku-20251001/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1100,"win_rate":41.1,"rank":88},{"arena":"models","category":"asciiart","elo":1158,"win_rate":49.3,"rank":40},{"arena":"models","category":"codecategories","elo":1130,"win_rate":44.9,"rank":85},{"arena":"models","category":"dataviz","elo":1140,"win_rate":45.7,"rank":82},{"arena":"models","category":"gamedev","elo":1120,"win_rate":44.7,"rank":81},{"arena":"models","category":"svg","elo":1050,"win_rate":39.1,"rank":72},{"arena":"models","category":"uicomponent","elo":1115,"win_rate":42.6,"rank":82},{"arena":"models","category":"website","elo":1135,"win_rate":45.1,"rank":86}],"artificial_analysis":{"intelligence_index":17.6,"coding_index":43.9,"agentic_index":10.3}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-vl-8b-thinking","canonical_slug":"qwen/qwen3-vl-8b-thinking","hugging_face_id":"Qwen/Qwen3-VL-8B-Thinking","name":"Qwen: Qwen3 VL 8B Thinking","created":1760463746,"description":"Qwen3-VL-8B-Thinking is the reasoning-optimized variant of the Qwen3-VL-8B multimodal model, designed for advanced visual and textual reasoning across complex scenes, documents, and temporal sequences. It integrates enhanced multimodal alignment and...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000018","completion":"0.0000021"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":0.95},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-8b-thinking/endpoints"},"reasoning":{"mandatory":true}},{"id":"qwen/qwen3-vl-8b-instruct","canonical_slug":"qwen/qwen3-vl-8b-instruct","hugging_face_id":"Qwen/Qwen3-VL-8B-Instruct","name":"Qwen: Qwen3 VL 8B Instruct","created":1760463308,"description":"Qwen3-VL-8B-Instruct is a multimodal vision-language model from the Qwen3-VL series, built for high-fidelity understanding and reasoning across text, images, and video. It features improved multimodal fusion with Interleaved-MRoPE for long-horizon...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.000000117","completion":"0.000000455"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.7,"top_p":0.8,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-8b-instruct/endpoints"}},{"id":"openai/gpt-5-image","canonical_slug":"openai/gpt-5-image","hugging_face_id":"","name":"OpenAI: GPT-5 Image","created":1760447986,"description":"[GPT-5](https://openrouter.ai/openai/gpt-5) Image combines OpenAI's GPT-5 model with state-of-the-art image generation capabilities. It offers major improvements in reasoning, code quality, and user experience while incorporating GPT Image 1's superior instruction following,...","context_length":400000,"architecture":{"modality":"text+image+file->text+image","input_modalities":["image","text","file"],"output_modalities":["image","text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00001","image_output":"0.00004","web_search":"0.01","input_cache_read":"0.00000125"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-image/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"graphicdesign","elo":1172,"win_rate":48.9,"rank":9},{"arena":"models","category":"image","elo":1194,"win_rate":53.9,"rank":9},{"arena":"models","category":"logo","elo":1202,"win_rate":52.7,"rank":9}]},"reasoning":{"mandatory":true}},{"id":"google/gemini-2.5-flash-image","canonical_slug":"google/gemini-2.5-flash-image","hugging_face_id":"","name":"Google: Nano Banana (Gemini 2.5 Flash Image)","created":1759870431,"description":"Gemini 2.5 Flash Image, a.k.a. \"Nano Banana,\" is now generally available. It is a state of the art image generation model with contextual understanding. It is capable of image generation,...","context_length":32768,"architecture":{"modality":"text+image->text+image","input_modalities":["image","text"],"output_modalities":["image","text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000025","image":"0.0000003","image_output":"0.00003","audio":"0.000001","input_audio_cache":"0.0000001","web_search":"0.014","internal_reasoning":"0.0000025","input_cache_read":"0.00000003","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":32768,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","stop","structured_outputs","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-flash-image/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"graphicdesign","elo":1167,"win_rate":56.9,"rank":10},{"arena":"models","category":"image","elo":1189,"win_rate":55.6,"rank":10},{"arena":"models","category":"logo","elo":1169,"win_rate":51.4,"rank":11}]}},{"id":"qwen/qwen3-vl-30b-a3b-thinking","canonical_slug":"qwen/qwen3-vl-30b-a3b-thinking","hugging_face_id":"Qwen/Qwen3-VL-30B-A3B-Thinking","name":"Qwen: Qwen3 VL 30B A3B Thinking","created":1759794479,"description":"Qwen3-VL-30B-A3B-Thinking is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Thinking variant enhances reasoning in STEM, math, and complex tasks. It excels...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000024"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.8,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":1},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-30b-a3b-thinking/endpoints"},"reasoning":{"mandatory":true}},{"id":"qwen/qwen3-vl-30b-a3b-instruct","canonical_slug":"qwen/qwen3-vl-30b-a3b-instruct","hugging_face_id":"Qwen/Qwen3-VL-30B-A3B-Instruct","name":"Qwen: Qwen3 VL 30B A3B Instruct","created":1759794476,"description":"Qwen3-VL-30B-A3B-Instruct is a multimodal model that unifies strong text generation with visual understanding for images and videos. Its Instruct variant optimizes instruction-following for general multimodal tasks. It excels in perception...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006"},"top_provider":{"context_length":262144,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.7,"top_p":0.8,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":1},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-30b-a3b-instruct/endpoints"}},{"id":"openai/gpt-5-pro","canonical_slug":"openai/gpt-5-pro-2025-10-06","hugging_face_id":"","name":"OpenAI: GPT-5 Pro","created":1759776663,"description":"GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000015","completion":"0.00012","web_search":"0.01"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-pro-2025-10-06/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["high"],"default_effort":"high"}},{"id":"openai/gpt-5-pro:batch","canonical_slug":"openai/gpt-5-pro-2025-10-06","hugging_face_id":"","name":"OpenAI: GPT-5 Pro (batch)","created":1759776663,"description":"GPT-5 Pro is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000075","completion":"0.00006","web_search":"0.01"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-pro-2025-10-06/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["high"],"default_effort":"high"}},{"id":"z-ai/glm-4.6","canonical_slug":"z-ai/glm-4.6","hugging_face_id":"zai-org/GLM-4.6","name":"Z.ai: GLM 4.6","created":1759235576,"description":"Compared with GLM-4.5, this generation brings several key improvements: Longer context window: The context window has been expanded from 128K to 200K tokens, enabling the model to handle more complex...","context_length":204800,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000043","completion":"0.00000175","input_cache_read":"0.00000008"},"top_provider":{"context_length":198000,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.6,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-4.6/endpoints"},"benchmarks":{"design_arena":[{"arena":"agents","category":"androidnative","elo":1100,"win_rate":52.6,"rank":29},{"arena":"agents","category":"fullstack","elo":1039,"win_rate":42.3,"rank":38},{"arena":"agents","category":"godotgamedev","elo":1180,"win_rate":53.2,"rank":12},{"arena":"agents","category":"mobileapps","elo":1125,"win_rate":47.2,"rank":34},{"arena":"models","category":"3d","elo":1157,"win_rate":54.2,"rank":64},{"arena":"models","category":"codecategories","elo":1181,"win_rate":54.3,"rank":66},{"arena":"models","category":"dataviz","elo":1177,"win_rate":52.6,"rank":66},{"arena":"models","category":"gamedev","elo":1174,"win_rate":54.8,"rank":61},{"arena":"models","category":"svg","elo":1133,"win_rate":52.1,"rank":53},{"arena":"models","category":"uicomponent","elo":1163,"win_rate":52.5,"rank":68},{"arena":"models","category":"website","elo":1187,"win_rate":54.3,"rank":67}],"artificial_analysis":{"intelligence_index":null,"coding_index":45.8,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-sonnet-4.5","canonical_slug":"anthropic/claude-4.5-sonnet-20250929","hugging_face_id":"","name":"Anthropic: Claude Sonnet 4.5","created":1759161676,"description":"Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000015","web_search":"0.01","input_cache_read":"0.0000003","input_cache_write":"0.00000375","input_cache_write_1h":"0.000006","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000006","completion":"0.0000225","input_cache_read":"0.0000006","input_cache_write":"0.0000075","input_cache_write_1h":"0.000012"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":1,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.5-sonnet-20250929/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1186,"win_rate":51,"rank":53},{"arena":"models","category":"asciiart","elo":1222,"win_rate":56.1,"rank":21},{"arena":"models","category":"codecategories","elo":1198,"win_rate":51.5,"rank":53},{"arena":"models","category":"dataviz","elo":1183,"win_rate":47.4,"rank":61},{"arena":"models","category":"gamedev","elo":1189,"win_rate":51.1,"rank":56},{"arena":"models","category":"svg","elo":1140,"win_rate":52.2,"rank":51},{"arena":"models","category":"uicomponent","elo":1188,"win_rate":49.4,"rank":57},{"arena":"models","category":"website","elo":1202,"win_rate":51.8,"rank":57},{"arena":"agents","category":"fullstack","elo":1060,"win_rate":43.7,"rank":35},{"arena":"agents","category":"mobileapps","elo":1166,"win_rate":52.9,"rank":29},{"arena":"agents","category":"webapps","elo":1075,"win_rate":43.2,"rank":35}],"artificial_analysis":{"intelligence_index":21.2,"coding_index":52.1,"agentic_index":17.5}},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-sonnet-4.5:batch","canonical_slug":"anthropic/claude-4.5-sonnet-20250929","hugging_face_id":"","name":"Anthropic: Claude Sonnet 4.5 (batch)","created":1759161676,"description":"Claude Sonnet 4.5 is Anthropic’s most advanced Sonnet model to date, optimized for real-world agents and coding workflows. It delivers state-of-the-art performance on coding benchmarks such as SWE-bench Verified, with...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000015","completion":"0.0000075","web_search":"0.01","input_cache_read":"0.00000015","input_cache_write":"0.000001875","input_cache_write_1h":"0.000003","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000003","completion":"0.00001125","input_cache_read":"0.0000003","input_cache_write":"0.00000375","input_cache_write_1h":"0.000006"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":1,"top_p":1,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.5-sonnet-20250929/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1186,"win_rate":51,"rank":53},{"arena":"models","category":"asciiart","elo":1222,"win_rate":56.1,"rank":21},{"arena":"models","category":"codecategories","elo":1198,"win_rate":51.5,"rank":53},{"arena":"models","category":"dataviz","elo":1183,"win_rate":47.4,"rank":61},{"arena":"models","category":"gamedev","elo":1189,"win_rate":51.1,"rank":56},{"arena":"models","category":"svg","elo":1140,"win_rate":52.2,"rank":51},{"arena":"models","category":"uicomponent","elo":1188,"win_rate":49.4,"rank":57},{"arena":"models","category":"website","elo":1202,"win_rate":51.8,"rank":57},{"arena":"agents","category":"fullstack","elo":1060,"win_rate":43.7,"rank":35},{"arena":"agents","category":"mobileapps","elo":1166,"win_rate":52.9,"rank":29},{"arena":"agents","category":"webapps","elo":1075,"win_rate":43.2,"rank":35}],"artificial_analysis":{"intelligence_index":21.2,"coding_index":52.1,"agentic_index":17.5}},"reasoning":{"mandatory":false}},{"id":"deepseek/deepseek-v3.2-exp","canonical_slug":"deepseek/deepseek-v3.2-exp","hugging_face_id":"deepseek-ai/DeepSeek-V3.2-Exp","name":"DeepSeek: DeepSeek V3.2 Exp","created":1759150481,"description":"DeepSeek-V3.2-Exp is an experimental large language model released by DeepSeek as an intermediate step between V3.1 and future architectures. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":"deepseek-v3.1"},"pricing":{"prompt":"0.00000027","completion":"0.00000041"},"top_provider":{"context_length":163840,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.6,"top_p":0.95,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-07-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v3.2-exp/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1176,"win_rate":56.4,"rank":56},{"arena":"models","category":"codecategories","elo":1186,"win_rate":54.2,"rank":61},{"arena":"models","category":"dataviz","elo":1168,"win_rate":50.6,"rank":70},{"arena":"models","category":"gamedev","elo":1167,"win_rate":53,"rank":63},{"arena":"models","category":"svg","elo":1057,"win_rate":42,"rank":71},{"arena":"models","category":"uicomponent","elo":1182,"win_rate":53.3,"rank":59},{"arena":"models","category":"website","elo":1191,"win_rate":54.3,"rank":63}]},"reasoning":{"mandatory":false}},{"id":"thedrummer/cydonia-24b-v4.1","canonical_slug":"thedrummer/cydonia-24b-v4.1","hugging_face_id":"thedrummer/cydonia-24b-v4.1","name":"TheDrummer: Cydonia 24B V4.1","created":1758931878,"description":"Uncensored and creative writing model based on Mistral Small 3.2 24B with good recall, prompt adherence, and intelligence.","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000005","input_cache_read":"0.00000015"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-04-30","expiration_date":null,"links":{"details":"/api/v1/models/thedrummer/cydonia-24b-v4.1/endpoints"}},{"id":"relace/relace-apply-3","canonical_slug":"relace/relace-apply-3","hugging_face_id":"","name":"Relace: Relace Apply 3","created":1758891572,"description":"Relace Apply 3 is a specialized code-patching LLM that merges AI-suggested edits straight into your source files. It can apply updates from GPT-4o, Claude, and others into your files at...","context_length":256000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000085","completion":"0.00000125"},"top_provider":{"context_length":256000,"max_completion_tokens":128000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","seed","stop"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/relace/relace-apply-3/endpoints"}},{"id":"qwen/qwen3-vl-235b-a22b-thinking","canonical_slug":"qwen/qwen3-vl-235b-a22b-thinking","hugging_face_id":"Qwen/Qwen3-VL-235B-A22B-Thinking","name":"Qwen: Qwen3 VL 235B A22B Thinking","created":1758668690,"description":"Qwen3-VL-235B-A22B Thinking is a multimodal model that unifies strong text generation with visual understanding across images and video. The Thinking model is optimized for multimodal reasoning in STEM and math....","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.000004"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.8,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":1},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-235b-a22b-thinking/endpoints"},"reasoning":{"mandatory":true}},{"id":"qwen/qwen3-vl-235b-a22b-instruct","canonical_slug":"qwen/qwen3-vl-235b-a22b-instruct","hugging_face_id":"Qwen/Qwen3-VL-235B-A22B-Instruct","name":"Qwen: Qwen3 VL 235B A22B Instruct","created":1758668687,"description":"Qwen3-VL-235B-A22B Instruct is an open-weight multimodal model that unifies strong text generation with visual understanding across images and video. The Instruct model targets general vision-language use (VQA, document parsing, chart/table...","context_length":262144,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000021","completion":"0.0000019","input_cache_read":"0.0000001"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.7,"top_p":0.8,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-vl-235b-a22b-instruct/endpoints"}},{"id":"qwen/qwen3-max","canonical_slug":"qwen/qwen3-max","hugging_face_id":"","name":"Qwen: Qwen3 Max","created":1758662808,"description":"Qwen3-Max is an updated release built on the Qwen3 series, offering major improvements in reasoning, instruction following, multilingual support, and long-tail knowledge coverage compared to the January 2025 version. It...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000078","completion":"0.0000039","input_cache_read":"0.000000156","input_cache_write":"0.000000975","overrides":[{"min_prompt_tokens":32000,"prompt":"0.00000156","completion":"0.0000078","input_cache_read":"0.000000312","input_cache_write":"0.00000195"},{"min_prompt_tokens":128000,"prompt":"0.00000195","completion":"0.00000975","input_cache_read":"0.00000039","input_cache_write":"0.0000024375"}]},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":1,"top_p":1,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-max/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1102,"win_rate":43.5,"rank":87},{"arena":"models","category":"asciiart","elo":1145,"win_rate":47.2,"rank":48},{"arena":"models","category":"codecategories","elo":1125,"win_rate":44,"rank":88},{"arena":"models","category":"dataviz","elo":1115,"win_rate":41.2,"rank":87},{"arena":"models","category":"gamedev","elo":1117,"win_rate":43.8,"rank":84},{"arena":"models","category":"svg","elo":1036,"win_rate":37.3,"rank":77},{"arena":"models","category":"uicomponent","elo":1093,"win_rate":40.2,"rank":89},{"arena":"models","category":"website","elo":1131,"win_rate":44.5,"rank":89}]},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-coder-plus","canonical_slug":"qwen/qwen3-coder-plus","hugging_face_id":"","name":"Qwen: Qwen3 Coder Plus","created":1758662707,"description":"Qwen3 Coder Plus is Alibaba's proprietary version of the Open Source Qwen3 Coder 480B A35B. It is a powerful coding agent model specializing in autonomous programming via tool calling and...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000065","completion":"0.00000325","input_cache_read":"0.00000013","input_cache_write":"0.0000008125","overrides":[{"min_prompt_tokens":32000,"prompt":"0.00000117","completion":"0.00000585","input_cache_read":"0.000000234","input_cache_write":"0.0000014625"},{"min_prompt_tokens":128000,"prompt":"0.00000195","completion":"0.00000975","input_cache_read":"0.00000039","input_cache_write":"0.0000024375"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-coder-plus/endpoints"}},{"id":"deepseek/deepseek-v3.1-terminus","canonical_slug":"deepseek/deepseek-v3.1-terminus","hugging_face_id":"deepseek-ai/DeepSeek-V3.1-Terminus","name":"DeepSeek: DeepSeek V3.1 Terminus","created":1758548275,"description":"DeepSeek-V3.1 Terminus is an update to [DeepSeek V3.1](/deepseek/deepseek-chat-v3.1) that maintains the model's original capabilities while addressing issues reported by users, including language consistency and agent capabilities, further optimizing the model's...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":"deepseek-v3.1"},"pricing":{"prompt":"0.00000027","completion":"0.000001","input_cache_read":"0.000000135"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-v3.1-terminus/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1169,"win_rate":56,"rank":58},{"arena":"models","category":"codecategories","elo":1190,"win_rate":56,"rank":59},{"arena":"models","category":"dataviz","elo":1183,"win_rate":53.4,"rank":62},{"arena":"models","category":"gamedev","elo":1153,"win_rate":52.5,"rank":72},{"arena":"models","category":"svg","elo":1090,"win_rate":50.1,"rank":63},{"arena":"models","category":"uicomponent","elo":1197,"win_rate":59.5,"rank":52},{"arena":"models","category":"website","elo":1199,"win_rate":56.4,"rank":59}],"artificial_analysis":{"intelligence_index":15.4,"coding_index":43.5,"agentic_index":8.9}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-coder-flash","canonical_slug":"qwen/qwen3-coder-flash","hugging_face_id":"","name":"Qwen: Qwen3 Coder Flash","created":1758115536,"description":"Qwen3 Coder Flash is Alibaba's fast and cost efficient version of their proprietary Qwen3 Coder Plus. It is a powerful coding agent model specializing in autonomous programming via tool calling...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.000000195","completion":"0.000000975","input_cache_read":"0.000000039","input_cache_write":"0.00000024375","overrides":[{"min_prompt_tokens":32000,"prompt":"0.000000325","completion":"0.000001625","input_cache_read":"0.000000065","input_cache_write":"0.00000040625"},{"min_prompt_tokens":128000,"prompt":"0.00000052","completion":"0.0000026","input_cache_read":"0.000000104","input_cache_write":"0.00000065"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-coder-flash/endpoints"}},{"id":"qwen/qwen3-next-80b-a3b-thinking","canonical_slug":"qwen/qwen3-next-80b-a3b-thinking-2509","hugging_face_id":"Qwen/Qwen3-Next-80B-A3B-Thinking","name":"Qwen: Qwen3 Next 80B A3B Thinking","created":1757612284,"description":"Qwen3-Next-80B-A3B-Thinking is a reasoning-first chat model in the Qwen3-Next line that outputs structured “thinking” traces by default. It’s designed for hard multi-step problems; math proofs, code synthesis/debugging, logic, and agentic...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000012"},"top_provider":{"context_length":131072,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-09-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-next-80b-a3b-thinking-2509/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":17.4,"agentic_index":null}},"reasoning":{"mandatory":true}},{"id":"qwen/qwen3-next-80b-a3b-instruct","canonical_slug":"qwen/qwen3-next-80b-a3b-instruct-2509","hugging_face_id":"Qwen/Qwen3-Next-80B-A3B-Instruct","name":"Qwen: Qwen3 Next 80B A3B Instruct","created":1757612213,"description":"Qwen3-Next-80B-A3B-Instruct is an instruction-tuned chat model in the Qwen3-Next series optimized for fast, stable responses without “thinking” traces. It targets complex tasks across reasoning, code generation, knowledge QA, and multilingual...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000009","completion":"0.0000011"},"top_provider":{"context_length":262144,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-09-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-next-80b-a3b-instruct-2509/endpoints"}},{"id":"qwen/qwen-plus-2025-07-28","canonical_slug":"qwen/qwen-plus-2025-07-28","hugging_face_id":"","name":"Qwen: Qwen Plus 0728","created":1757347599,"description":"Qwen Plus 0728, based on the Qwen3 foundation model, is a 1 million context hybrid reasoning model with a balanced performance, speed, and cost combination.","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000026","completion":"0.00000078","overrides":[{"min_prompt_tokens":256000,"prompt":"0.00000078","completion":"0.00000234"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen-plus-2025-07-28/endpoints"},"reasoning":{"mandatory":false}},{"id":"moonshotai/kimi-k2-0905","canonical_slug":"moonshotai/kimi-k2-0905","hugging_face_id":"moonshotai/Kimi-K2-Instruct-0905","name":"MoonshotAI: Kimi K2 0905","created":1757021147,"description":"Kimi K2 0905 is the September update of [Kimi K2 0711](moonshotai/kimi-k2). It is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.0000025"},"top_provider":{"context_length":262144,"max_completion_tokens":98304,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-12-31","expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k2-0905/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1113,"win_rate":48.5,"rank":91},{"arena":"models","category":"website","elo":1120,"win_rate":48.3,"rank":93}]}},{"id":"qwen/qwen3-30b-a3b-thinking-2507","canonical_slug":"qwen/qwen3-30b-a3b-thinking-2507","hugging_face_id":"Qwen/Qwen3-30B-A3B-Thinking-2507","name":"Qwen: Qwen3 30B A3B Thinking 2507","created":1756399192,"description":"Qwen3-30B-A3B-Thinking-2507 is a 30B parameter Mixture-of-Experts reasoning model optimized for complex tasks requiring extended multi-step thinking. The model is designed specifically for “thinking mode,” where internal reasoning traces are separated...","context_length":81920,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000024"},"top_provider":{"context_length":81920,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-30b-a3b-thinking-2507/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"dataviz","elo":940,"win_rate":33.3,"rank":114},{"arena":"models","category":"website","elo":943,"win_rate":35.5,"rank":122}],"artificial_analysis":{"intelligence_index":9.8,"coding_index":12.1,"agentic_index":0.9}},"reasoning":{"mandatory":true}},{"id":"nousresearch/hermes-4-405b","canonical_slug":"nousresearch/hermes-4-405b","hugging_face_id":"NousResearch/Hermes-4-405B","name":"Nous: Hermes 4 405B","created":1756235463,"description":"Hermes 4 is a large-scale reasoning model built on Meta-Llama-3.1-405B and released by Nous Research. It introduces a hybrid reasoning mode, where the model can choose to deliberate internally with...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000003"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/nousresearch/hermes-4-405b/endpoints"},"reasoning":{"mandatory":false}},{"id":"deepseek/deepseek-chat-v3.1","canonical_slug":"deepseek/deepseek-chat-v3.1","hugging_face_id":"deepseek-ai/DeepSeek-V3.1","name":"DeepSeek: DeepSeek V3.1","created":1755779628,"description":"DeepSeek-V3.1 is a large hybrid reasoning model (671B parameters, 37B active) that supports both thinking and non-thinking modes via prompt templates. It extends the DeepSeek-V3 base with a two-phase long-context...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":"deepseek-v3.1"},"pricing":{"prompt":"0.00000025","completion":"0.00000095","input_cache_read":"0.00000013"},"top_provider":{"context_length":163840,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-chat-v3.1/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1105,"win_rate":47.9,"rank":86},{"arena":"models","category":"codecategories","elo":1127,"win_rate":47.8,"rank":87},{"arena":"models","category":"dataviz","elo":1111,"win_rate":46.2,"rank":91},{"arena":"models","category":"gamedev","elo":1107,"win_rate":47.1,"rank":86},{"arena":"models","category":"svg","elo":992,"win_rate":38.2,"rank":84},{"arena":"models","category":"uicomponent","elo":1100,"win_rate":47.1,"rank":87},{"arena":"models","category":"website","elo":1135,"win_rate":48,"rank":87}]},"reasoning":{"mandatory":false}},{"id":"mistralai/mistral-medium-3.1","canonical_slug":"mistralai/mistral-medium-3.1","hugging_face_id":"","name":"Mistral: Mistral Medium 3.1","created":1755095639,"description":"Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances...","context_length":131072,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.000002","input_cache_read":"0.00000004"},"top_provider":{"context_length":131072,"max_completion_tokens":104857,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-medium-3.1/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1109,"win_rate":44.6,"rank":83},{"arena":"models","category":"asciiart","elo":1015,"win_rate":30.8,"rank":65},{"arena":"models","category":"codecategories","elo":1136,"win_rate":45.1,"rank":83},{"arena":"models","category":"dataviz","elo":1160,"win_rate":47.2,"rank":72},{"arena":"models","category":"gamedev","elo":1097,"win_rate":40.7,"rank":90},{"arena":"models","category":"svg","elo":1017,"win_rate":38.2,"rank":80},{"arena":"models","category":"uicomponent","elo":1114,"win_rate":43.6,"rank":83},{"arena":"models","category":"website","elo":1145,"win_rate":46,"rank":83}],"artificial_analysis":{"intelligence_index":9.5,"coding_index":20.5,"agentic_index":3.1}}},{"id":"mistralai/mistral-medium-3.1:batch","canonical_slug":"mistralai/mistral-medium-3.1","hugging_face_id":"","name":"Mistral: Mistral Medium 3.1 (batch)","created":1755095639,"description":"Mistral Medium 3.1 is an updated version of Mistral Medium 3, which is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances...","context_length":131072,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.000001","input_cache_read":"0.00000002"},"top_provider":{"context_length":131072,"max_completion_tokens":104857,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-medium-3.1/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1109,"win_rate":44.6,"rank":83},{"arena":"models","category":"asciiart","elo":1015,"win_rate":30.8,"rank":65},{"arena":"models","category":"codecategories","elo":1136,"win_rate":45.1,"rank":83},{"arena":"models","category":"dataviz","elo":1160,"win_rate":47.2,"rank":72},{"arena":"models","category":"gamedev","elo":1097,"win_rate":40.7,"rank":90},{"arena":"models","category":"svg","elo":1017,"win_rate":38.2,"rank":80},{"arena":"models","category":"uicomponent","elo":1114,"win_rate":43.6,"rank":83},{"arena":"models","category":"website","elo":1145,"win_rate":46,"rank":83}],"artificial_analysis":{"intelligence_index":9.5,"coding_index":20.5,"agentic_index":3.1}}},{"id":"z-ai/glm-4.5v","canonical_slug":"z-ai/glm-4.5v","hugging_face_id":"zai-org/GLM-4.5V","name":"Z.ai: GLM 4.5V","created":1754922288,"description":"GLM-4.5V is a vision-language foundation model for multimodal agent applications. Built on a Mixture-of-Experts (MoE) architecture with 106B parameters and 12B activated parameters, it achieves state-of-the-art results in video understanding,...","context_length":65536,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.0000018","input_cache_read":"0.00000011"},"top_provider":{"context_length":65536,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.75,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-12-31","expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-4.5v/endpoints"},"reasoning":{"mandatory":false}},{"id":"openai/gpt-5","canonical_slug":"openai/gpt-5-2025-08-07","hugging_face_id":"","name":"OpenAI: GPT-5","created":1754587413,"description":"GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00001","web_search":"0.01","input_cache_read":"0.000000125"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-2025-08-07/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1084,"win_rate":41.3,"rank":91},{"arena":"models","category":"asciiart","elo":1158,"win_rate":49,"rank":42},{"arena":"models","category":"codecategories","elo":1183,"win_rate":54.5,"rank":64},{"arena":"models","category":"dataviz","elo":1237,"win_rate":60.5,"rank":40},{"arena":"models","category":"gamedev","elo":1208,"win_rate":59.3,"rank":47},{"arena":"models","category":"svg","elo":1213,"win_rate":64.1,"rank":26},{"arena":"models","category":"uicomponent","elo":1195,"win_rate":57.4,"rank":55},{"arena":"models","category":"website","elo":1197,"win_rate":53.7,"rank":61}],"artificial_analysis":{"intelligence_index":null,"coding_index":37.8,"agentic_index":null}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"openai/gpt-5:batch","canonical_slug":"openai/gpt-5-2025-08-07","hugging_face_id":"","name":"OpenAI: GPT-5 (batch)","created":1754587413,"description":"GPT-5 is OpenAI’s most advanced model, offering major improvements in reasoning, code quality, and user experience. It is optimized for complex tasks that require step-by-step reasoning, instruction following, and accuracy...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000625","completion":"0.000005","web_search":"0.01","input_cache_read":"0.0000000625"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-2025-08-07/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1084,"win_rate":41.3,"rank":91},{"arena":"models","category":"asciiart","elo":1158,"win_rate":49,"rank":42},{"arena":"models","category":"codecategories","elo":1183,"win_rate":54.5,"rank":64},{"arena":"models","category":"dataviz","elo":1237,"win_rate":60.5,"rank":40},{"arena":"models","category":"gamedev","elo":1208,"win_rate":59.3,"rank":47},{"arena":"models","category":"svg","elo":1213,"win_rate":64.1,"rank":26},{"arena":"models","category":"uicomponent","elo":1195,"win_rate":57.4,"rank":55},{"arena":"models","category":"website","elo":1197,"win_rate":53.7,"rank":61}],"artificial_analysis":{"intelligence_index":null,"coding_index":37.8,"agentic_index":null}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"openai/gpt-5-mini","canonical_slug":"openai/gpt-5-mini-2025-08-07","hugging_face_id":"","name":"OpenAI: GPT-5 Mini","created":1754587407,"description":"GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost....","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.000002","web_search":"0.01","input_cache_read":"0.000000025"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-05-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-mini-2025-08-07/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1065,"win_rate":36.9,"rank":94},{"arena":"models","category":"asciiart","elo":1140,"win_rate":44.5,"rank":49},{"arena":"models","category":"codecategories","elo":1130,"win_rate":43.5,"rank":86},{"arena":"models","category":"dataviz","elo":1146,"win_rate":43.7,"rank":78},{"arena":"models","category":"gamedev","elo":1152,"win_rate":46.5,"rank":73},{"arena":"models","category":"svg","elo":1116,"win_rate":45.8,"rank":57},{"arena":"models","category":"uicomponent","elo":1121,"win_rate":41.9,"rank":79},{"arena":"models","category":"website","elo":1138,"win_rate":44.3,"rank":85}],"artificial_analysis":{"intelligence_index":17.4,"coding_index":15.6,"agentic_index":8.9}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"openai/gpt-5-mini:batch","canonical_slug":"openai/gpt-5-mini-2025-08-07","hugging_face_id":"","name":"OpenAI: GPT-5 Mini (batch)","created":1754587407,"description":"GPT-5 Mini is a compact version of GPT-5, designed to handle lighter-weight reasoning tasks. It provides the same instruction-following and safety-tuning benefits as GPT-5, but with reduced latency and cost....","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000125","completion":"0.000001","web_search":"0.01","input_cache_read":"0.0000000125"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-05-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-mini-2025-08-07/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1065,"win_rate":36.9,"rank":94},{"arena":"models","category":"asciiart","elo":1140,"win_rate":44.5,"rank":49},{"arena":"models","category":"codecategories","elo":1130,"win_rate":43.5,"rank":86},{"arena":"models","category":"dataviz","elo":1146,"win_rate":43.7,"rank":78},{"arena":"models","category":"gamedev","elo":1152,"win_rate":46.5,"rank":73},{"arena":"models","category":"svg","elo":1116,"win_rate":45.8,"rank":57},{"arena":"models","category":"uicomponent","elo":1121,"win_rate":41.9,"rank":79},{"arena":"models","category":"website","elo":1138,"win_rate":44.3,"rank":85}],"artificial_analysis":{"intelligence_index":17.4,"coding_index":15.6,"agentic_index":8.9}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"openai/gpt-5-nano","canonical_slug":"openai/gpt-5-nano-2025-08-07","hugging_face_id":"","name":"OpenAI: GPT-5 Nano","created":1754587402,"description":"GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000005","completion":"0.0000004","web_search":"0.01","input_cache_read":"0.000000005"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_completion_tokens","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-05-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-nano-2025-08-07/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":994,"win_rate":36.3,"rank":105},{"arena":"models","category":"codecategories","elo":1099,"win_rate":48,"rank":94},{"arena":"models","category":"dataviz","elo":1071,"win_rate":46.2,"rank":98},{"arena":"models","category":"gamedev","elo":1067,"win_rate":46.5,"rank":97},{"arena":"models","category":"uicomponent","elo":1082,"win_rate":51.9,"rank":92},{"arena":"models","category":"website","elo":1114,"win_rate":48.9,"rank":94}]},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"openai/gpt-5-nano:batch","canonical_slug":"openai/gpt-5-nano-2025-08-07","hugging_face_id":"","name":"OpenAI: GPT-5 Nano (batch)","created":1754587402,"description":"GPT-5-Nano is the smallest and fastest variant in the GPT-5 system, optimized for developer tools, rapid interactions, and ultra-low latency environments. While limited in reasoning depth compared to its larger...","context_length":400000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000025","completion":"0.0000002","web_search":"0.01","input_cache_read":"0.0000000025"},"top_provider":{"context_length":400000,"max_completion_tokens":128000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-05-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-5-nano-2025-08-07/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":994,"win_rate":36.3,"rank":105},{"arena":"models","category":"codecategories","elo":1099,"win_rate":48,"rank":94},{"arena":"models","category":"dataviz","elo":1071,"win_rate":46.2,"rank":98},{"arena":"models","category":"gamedev","elo":1067,"win_rate":46.5,"rank":97},{"arena":"models","category":"uicomponent","elo":1082,"win_rate":51.9,"rank":92},{"arena":"models","category":"website","elo":1114,"win_rate":48.9,"rank":94}]},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low","minimal"],"default_effort":"medium"}},{"id":"openai/gpt-oss-120b","canonical_slug":"openai/gpt-oss-120b","hugging_face_id":"openai/gpt-oss-120b","name":"OpenAI: gpt-oss-120b","created":1754414231,"description":"gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000037","completion":"0.00000017"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-oss-120b/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":929,"win_rate":29.4,"rank":109},{"arena":"models","category":"codecategories","elo":978,"win_rate":33.4,"rank":117},{"arena":"models","category":"dataviz","elo":1002,"win_rate":43.6,"rank":107},{"arena":"models","category":"gamedev","elo":1015,"win_rate":40.5,"rank":106},{"arena":"models","category":"uicomponent","elo":942,"win_rate":35.7,"rank":112},{"arena":"models","category":"website","elo":980,"win_rate":32.5,"rank":118}],"artificial_analysis":{"intelligence_index":12.3,"coding_index":30.4,"agentic_index":6.2}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-oss-120b:batch","canonical_slug":"openai/gpt-oss-120b","hugging_face_id":"openai/gpt-oss-120b","name":"OpenAI: gpt-oss-120b (batch)","created":1754414231,"description":"gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-oss-120b/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":929,"win_rate":29.4,"rank":109},{"arena":"models","category":"codecategories","elo":978,"win_rate":33.4,"rank":117},{"arena":"models","category":"dataviz","elo":1002,"win_rate":43.6,"rank":107},{"arena":"models","category":"gamedev","elo":1015,"win_rate":40.5,"rank":106},{"arena":"models","category":"uicomponent","elo":942,"win_rate":35.7,"rank":112},{"arena":"models","category":"website","elo":980,"win_rate":32.5,"rank":118}],"artificial_analysis":{"intelligence_index":12.3,"coding_index":30.4,"agentic_index":6.2}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"openai/gpt-oss-20b","canonical_slug":"openai/gpt-oss-20b","hugging_face_id":"openai/gpt-oss-20b","name":"OpenAI: gpt-oss-20b","created":1754414229,"description":"gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000003","completion":"0.00000013","input_cache_read":"0.00000003"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-oss-20b/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"dataviz","elo":947,"win_rate":39.7,"rank":112},{"arena":"models","category":"website","elo":865,"win_rate":27.9,"rank":126}],"artificial_analysis":{"intelligence_index":9,"coding_index":20.7,"agentic_index":1.4}},"reasoning":{"mandatory":true,"supported_efforts":["high","medium","low"],"default_effort":"medium"}},{"id":"anthropic/claude-opus-4.1","canonical_slug":"anthropic/claude-4.1-opus-20250805","hugging_face_id":"","name":"Anthropic: Claude Opus 4.1","created":1754411591,"description":"Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000015","completion":"0.000075","web_search":"0.01","input_cache_read":"0.0000015","input_cache_write":"0.00001875","input_cache_write_1h":"0.00003"},"top_provider":{"context_length":200000,"max_completion_tokens":32000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.1-opus-20250805/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1181,"win_rate":51.9,"rank":55},{"arena":"models","category":"asciiart","elo":1184,"win_rate":51.5,"rank":26},{"arena":"models","category":"codecategories","elo":1186,"win_rate":55.8,"rank":60},{"arena":"models","category":"dataviz","elo":1181,"win_rate":56.4,"rank":63},{"arena":"models","category":"gamedev","elo":1194,"win_rate":58.4,"rank":53},{"arena":"models","category":"svg","elo":1177,"win_rate":60.8,"rank":37},{"arena":"models","category":"uicomponent","elo":1179,"win_rate":57.8,"rank":61},{"arena":"models","category":"website","elo":1189,"win_rate":55.3,"rank":64}]},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-opus-4.1:batch","canonical_slug":"anthropic/claude-4.1-opus-20250805","hugging_face_id":"","name":"Anthropic: Claude Opus 4.1 (batch)","created":1754411591,"description":"Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.0000075","completion":"0.0000375","web_search":"0.01","input_cache_read":"0.00000075","input_cache_write":"0.000009375","input_cache_write_1h":"0.000015"},"top_provider":{"context_length":200000,"max_completion_tokens":32000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","stop","structured_outputs","temperature","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4.1-opus-20250805/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1181,"win_rate":51.9,"rank":55},{"arena":"models","category":"asciiart","elo":1184,"win_rate":51.5,"rank":26},{"arena":"models","category":"codecategories","elo":1186,"win_rate":55.8,"rank":60},{"arena":"models","category":"dataviz","elo":1181,"win_rate":56.4,"rank":63},{"arena":"models","category":"gamedev","elo":1194,"win_rate":58.4,"rank":53},{"arena":"models","category":"svg","elo":1177,"win_rate":60.8,"rank":37},{"arena":"models","category":"uicomponent","elo":1179,"win_rate":57.8,"rank":61},{"arena":"models","category":"website","elo":1189,"win_rate":55.3,"rank":64}]},"reasoning":{"mandatory":false}},{"id":"mistralai/codestral-2508","canonical_slug":"mistralai/codestral-2508","hugging_face_id":"","name":"Mistral: Codestral 2508","created":1754079630,"description":"Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.\n\n[Blog Post](https://mistral.ai/news/codestral-25-08)","context_length":256000,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000009","input_cache_read":"0.00000003"},"top_provider":{"context_length":256000,"max_completion_tokens":204800,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/codestral-2508/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1022,"win_rate":38.5,"rank":111},{"arena":"models","category":"dataviz","elo":1032,"win_rate":41.1,"rank":102},{"arena":"models","category":"gamedev","elo":990,"win_rate":36.3,"rank":112},{"arena":"models","category":"uicomponent","elo":1032,"win_rate":46.7,"rank":101},{"arena":"models","category":"website","elo":1025,"win_rate":37.8,"rank":113},{"arena":"models","category":"3d","elo":1048,"win_rate":45.4,"rank":97}]}},{"id":"mistralai/codestral-2508:batch","canonical_slug":"mistralai/codestral-2508","hugging_face_id":"","name":"Mistral: Codestral 2508 (batch)","created":1754079630,"description":"Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation.\n\n[Blog Post](https://mistral.ai/news/codestral-25-08)","context_length":256000,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.00000045","input_cache_read":"0.000000015"},"top_provider":{"context_length":256000,"max_completion_tokens":204800,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/codestral-2508/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1022,"win_rate":38.5,"rank":111},{"arena":"models","category":"dataviz","elo":1032,"win_rate":41.1,"rank":102},{"arena":"models","category":"gamedev","elo":990,"win_rate":36.3,"rank":112},{"arena":"models","category":"uicomponent","elo":1032,"win_rate":46.7,"rank":101},{"arena":"models","category":"website","elo":1025,"win_rate":37.8,"rank":113},{"arena":"models","category":"3d","elo":1048,"win_rate":45.4,"rank":97}]}},{"id":"qwen/qwen3-coder-30b-a3b-instruct","canonical_slug":"qwen/qwen3-coder-30b-a3b-instruct","hugging_face_id":"Qwen/Qwen3-Coder-30B-A3B-Instruct","name":"Qwen: Qwen3 Coder 30B A3B Instruct","created":1753972379,"description":"Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000007","completion":"0.00000028"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-coder-30b-a3b-instruct/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"dataviz","elo":1098,"win_rate":54.7,"rank":93},{"arena":"models","category":"uicomponent","elo":1061,"win_rate":54.1,"rank":93},{"arena":"models","category":"website","elo":1100,"win_rate":57.1,"rank":97}]}},{"id":"qwen/qwen3-30b-a3b-instruct-2507","canonical_slug":"qwen/qwen3-30b-a3b-instruct-2507","hugging_face_id":"Qwen/Qwen3-30B-A3B-Instruct-2507","name":"Qwen: Qwen3 30B A3B Instruct 2507","created":1753806965,"description":"Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.00000004815","completion":"0.00000019305"},"top_provider":{"context_length":128000,"max_completion_tokens":32000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-30b-a3b-instruct-2507/endpoints"}},{"id":"z-ai/glm-4.5","canonical_slug":"z-ai/glm-4.5","hugging_face_id":"zai-org/GLM-4.5","name":"Z.ai: GLM 4.5","created":1753471347,"description":"GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000006","completion":"0.0000022","input_cache_read":"0.00000011"},"top_provider":{"context_length":131072,"max_completion_tokens":98304,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.75,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-12-31","expiration_date":"2026-12-31","links":{"details":"/api/v1/models/z-ai/glm-4.5/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1198,"win_rate":59.7,"rank":49},{"arena":"models","category":"codecategories","elo":1180,"win_rate":54.4,"rank":67},{"arena":"models","category":"dataviz","elo":1174,"win_rate":53.3,"rank":68},{"arena":"models","category":"gamedev","elo":1168,"win_rate":54.4,"rank":62},{"arena":"models","category":"svg","elo":1121,"win_rate":50.8,"rank":56},{"arena":"models","category":"uicomponent","elo":1159,"win_rate":55,"rank":69},{"arena":"models","category":"website","elo":1182,"win_rate":53.7,"rank":68}]},"reasoning":{"mandatory":false}},{"id":"z-ai/glm-4.5-air","canonical_slug":"z-ai/glm-4.5-air","hugging_face_id":"zai-org/GLM-4.5-Air","name":"Z.ai: GLM 4.5 Air","created":1753471258,"description":"GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000013","completion":"0.00000085","input_cache_read":"0.000000025"},"top_provider":{"context_length":131072,"max_completion_tokens":98304,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.75,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-12-31","expiration_date":null,"links":{"details":"/api/v1/models/z-ai/glm-4.5-air/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1150,"win_rate":54.1,"rank":67},{"arena":"models","category":"codecategories","elo":1152,"win_rate":51.4,"rank":79},{"arena":"models","category":"dataviz","elo":1202,"win_rate":58.5,"rank":51},{"arena":"models","category":"gamedev","elo":1117,"win_rate":48.4,"rank":83},{"arena":"models","category":"svg","elo":1096,"win_rate":50.8,"rank":62},{"arena":"models","category":"uicomponent","elo":1138,"win_rate":54.4,"rank":75},{"arena":"models","category":"website","elo":1159,"win_rate":51.3,"rank":76}]},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-235b-a22b-thinking-2507","canonical_slug":"qwen/qwen3-235b-a22b-thinking-2507","hugging_face_id":"Qwen/Qwen3-235B-A22B-Thinking-2507","name":"Qwen: Qwen3 235B A22B Thinking 2507","created":1753449557,"description":"Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":"qwen3"},"pricing":{"prompt":"0.00000023","completion":"0.0000023"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-235b-a22b-thinking-2507/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1026,"win_rate":40.4,"rank":100},{"arena":"models","category":"codecategories","elo":1048,"win_rate":40.8,"rank":105},{"arena":"models","category":"dataviz","elo":964,"win_rate":32.6,"rank":110},{"arena":"models","category":"gamedev","elo":980,"win_rate":34.2,"rank":113},{"arena":"models","category":"uicomponent","elo":960,"win_rate":34,"rank":109},{"arena":"models","category":"website","elo":1065,"win_rate":42,"rank":103}],"artificial_analysis":{"intelligence_index":12.7,"coding_index":22.1,"agentic_index":1.3}},"reasoning":{"mandatory":true}},{"id":"qwen/qwen3-coder","canonical_slug":"qwen/qwen3-coder-480b-a35b-07-25","hugging_face_id":"Qwen/Qwen3-Coder-480B-A35B-Instruct","name":"Qwen: Qwen3 Coder 480B A35B","created":1753230546,"description":"Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.000001","input_cache_read":"0.0000001"},"top_provider":{"context_length":262144,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-coder-480b-a35b-07-25/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1158,"win_rate":61.2,"rank":73},{"arena":"models","category":"dataviz","elo":1097,"win_rate":54.9,"rank":94},{"arena":"models","category":"gamedev","elo":1122,"win_rate":58.7,"rank":80},{"arena":"models","category":"uicomponent","elo":1128,"win_rate":61.4,"rank":77},{"arena":"models","category":"website","elo":1171,"win_rate":61.7,"rank":74}]}},{"id":"bytedance/ui-tars-1.5-7b","canonical_slug":"bytedance/ui-tars-1.5-7b","hugging_face_id":"ByteDance-Seed/UI-TARS-1.5-7B","name":"ByteDance: UI-TARS 7B ","created":1753205056,"description":"UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, it builds upon the UI-TARS framework with reinforcement...","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000002","input_cache_read":"0.0000001"},"top_provider":{"context_length":128000,"max_completion_tokens":2048,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/bytedance/ui-tars-1.5-7b/endpoints"}},{"id":"google/gemini-2.5-flash-lite","canonical_slug":"google/gemini-2.5-flash-lite","hugging_face_id":"","name":"Google: Gemini 2.5 Flash Lite","created":1753200276,"description":"Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","file","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000004","image":"0.0000001","audio":"0.0000003","input_audio_cache":"0.00000003","web_search":"0.014","internal_reasoning":"0.0000004","input_cache_read":"0.00000001","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65535,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-flash-lite/endpoints"},"reasoning":{"mandatory":false}},{"id":"google/gemini-2.5-flash-lite:batch","canonical_slug":"google/gemini-2.5-flash-lite","hugging_face_id":"","name":"Google: Gemini 2.5 Flash Lite (batch)","created":1753200276,"description":"Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","file","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000005","completion":"0.0000002","image":"0.00000005","audio":"0.00000015","input_audio_cache":"0.00000003","web_search":"0.014","internal_reasoning":"0.0000002","input_cache_read":"0.00000001"},"top_provider":{"context_length":1048576,"max_completion_tokens":65535,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-flash-lite/endpoints"},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-235b-a22b-2507","canonical_slug":"qwen/qwen3-235b-a22b-07-25","hugging_face_id":"Qwen/Qwen3-235B-A22B-Instruct-2507","name":"Qwen: Qwen3 235B A22B Instruct 2507","created":1753119555,"description":"Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":null},"pricing":{"prompt":"0.0000000875","completion":"0.00000035","input_cache_read":"0.0000000175"},"top_provider":{"context_length":262144,"max_completion_tokens":235929,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-235b-a22b-07-25/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1022,"win_rate":41.1,"rank":101},{"arena":"models","category":"codecategories","elo":1054,"win_rate":42.6,"rank":102},{"arena":"models","category":"dataviz","elo":1085,"win_rate":49,"rank":95},{"arena":"models","category":"gamedev","elo":974,"win_rate":35,"rank":114},{"arena":"models","category":"uicomponent","elo":980,"win_rate":38.4,"rank":106},{"arena":"models","category":"website","elo":1070,"win_rate":43.6,"rank":102}]}},{"id":"moonshotai/kimi-k2","canonical_slug":"moonshotai/kimi-k2","hugging_face_id":"moonshotai/Kimi-K2-Instruct","name":"MoonshotAI: Kimi K2 0711","created":1752263252,"description":"Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000057","completion":"0.0000023"},"top_provider":{"context_length":131072,"max_completion_tokens":98304,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-12-31","expiration_date":null,"links":{"details":"/api/v1/models/moonshotai/kimi-k2/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1049,"win_rate":51.7,"rank":103},{"arena":"models","category":"dataviz","elo":1032,"win_rate":49.4,"rank":103},{"arena":"models","category":"gamedev","elo":997,"win_rate":46.4,"rank":110},{"arena":"models","category":"uicomponent","elo":1045,"win_rate":55.1,"rank":95},{"arena":"models","category":"website","elo":1063,"win_rate":53.1,"rank":104}]}},{"id":"cognitivecomputations/dolphin-mistral-24b-venice-edition","canonical_slug":"venice/uncensored","hugging_face_id":"cognitivecomputations/Dolphin-Mistral-24B-Venice-Edition","name":"Venice: Uncensored","created":1752094966,"description":"Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000009"},"top_provider":{"context_length":128000,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-04-30","expiration_date":null,"links":{"details":"/api/v1/models/venice/uncensored/endpoints"}},{"id":"tencent/hunyuan-a13b-instruct","canonical_slug":"tencent/hunyuan-a13b-instruct","hugging_face_id":"tencent/Hunyuan-A13B-Instruct","name":"Tencent: Hunyuan A13B Instruct","created":1751987664,"description":"Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000014","completion":"0.00000057"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","reasoning","response_format","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/tencent/hunyuan-a13b-instruct/endpoints"},"reasoning":{"mandatory":false}},{"id":"morph/morph-v3-large","canonical_slug":"morph/morph-v3-large","hugging_face_id":"","name":"Morph: Morph V3 Large","created":1751910858,"description":"Morph's high-accuracy apply model for complex code edits. ~4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following format: {instruction} {initial_code}...","context_length":262144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000009","completion":"0.0000019"},"top_provider":{"context_length":262144,"max_completion_tokens":131072,"is_moderated":false},"per_request_limits":null,"supported_parameters":["logprobs","max_tokens","response_format","stop","structured_outputs","temperature","top_logprobs"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/morph/morph-v3-large/endpoints"}},{"id":"morph/morph-v3-fast","canonical_slug":"morph/morph-v3-fast","hugging_face_id":"","name":"Morph: Morph V3 Fast","created":1751910002,"description":"Morph's fastest apply model for code edits. ~10,500 tokens/sec with 96% accuracy for rapid code transformations. The model requires the prompt to be in the following format: {instruction} {initial_code} {edit_snippet}...","context_length":81920,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000008","completion":"0.0000012"},"top_provider":{"context_length":81920,"max_completion_tokens":38000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/morph/morph-v3-fast/endpoints"}},{"id":"baidu/ernie-4.5-vl-424b-a47b","canonical_slug":"baidu/ernie-4.5-vl-424b-a47b","hugging_face_id":"baidu/ERNIE-4.5-VL-424B-A47B-PT","name":"Baidu: ERNIE 4.5 VL 424B A47B ","created":1751300903,"description":"ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data...","context_length":123000,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000042","completion":"0.00000125"},"top_provider":{"context_length":123000,"max_completion_tokens":16000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/baidu/ernie-4.5-vl-424b-a47b/endpoints"},"reasoning":{"mandatory":false}},{"id":"mistralai/mistral-small-3.2-24b-instruct","canonical_slug":"mistralai/mistral-small-3.2-24b-instruct-2506","hugging_face_id":"mistralai/Mistral-Small-3.2-24B-Instruct-2506","name":"Mistral: Mistral Small 3.2 24B","created":1750443016,"description":"Mistral-Small-3.2-24B-Instruct-2506 is an updated 24B parameter model from Mistral optimized for instruction following, repetition reduction, and improved function calling. Compared to the 3.1 release, version 3.2 significantly improves accuracy on...","context_length":256000,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000009375","completion":"0.00000025"},"top_provider":{"context_length":256000,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-small-3.2-24b-instruct-2506/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":922,"win_rate":39.8,"rank":120},{"arena":"models","category":"dataviz","elo":943,"win_rate":43.3,"rank":113},{"arena":"models","category":"gamedev","elo":911,"win_rate":39.4,"rank":121},{"arena":"models","category":"uicomponent","elo":922,"win_rate":40.5,"rank":114},{"arena":"models","category":"website","elo":908,"win_rate":38.3,"rank":123}]}},{"id":"minimax/minimax-m1","canonical_slug":"minimax/minimax-m1","hugging_face_id":"","name":"MiniMax: MiniMax M1","created":1750200414,"description":"MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom \"lightning attention\" mechanism, allowing it...","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.0000022"},"top_provider":{"context_length":1000000,"max_completion_tokens":40000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-m1/endpoints"},"reasoning":{"mandatory":false}},{"id":"google/gemini-2.5-flash","canonical_slug":"google/gemini-2.5-flash","hugging_face_id":"","name":"Google: Gemini 2.5 Flash","created":1750172488,"description":"Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in \"thinking\" capabilities, enabling it to provide responses with greater...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["file","image","text","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.0000003","completion":"0.0000025","image":"0.0000003","audio":"0.000001","input_audio_cache":"0.0000001","web_search":"0.014","internal_reasoning":"0.0000025","input_cache_read":"0.00000003","input_cache_write":"0.0000000833333333333333"},"top_provider":{"context_length":1048576,"max_completion_tokens":65535,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-flash/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1099,"win_rate":47.4,"rank":89},{"arena":"models","category":"codecategories","elo":1119,"win_rate":46.9,"rank":90},{"arena":"models","category":"dataviz","elo":1149,"win_rate":49.1,"rank":77},{"arena":"models","category":"gamedev","elo":1088,"win_rate":44.3,"rank":93},{"arena":"models","category":"uicomponent","elo":1108,"win_rate":48.9,"rank":85},{"arena":"models","category":"website","elo":1127,"win_rate":47.1,"rank":90},{"arena":"models","category":"svg","elo":1045,"win_rate":43.1,"rank":74}]},"reasoning":{"mandatory":false}},{"id":"google/gemini-2.5-flash:batch","canonical_slug":"google/gemini-2.5-flash","hugging_face_id":"","name":"Google: Gemini 2.5 Flash (batch)","created":1750172488,"description":"Gemini 2.5 Flash is Google's state-of-the-art workhorse model, specifically designed for advanced reasoning, coding, mathematics, and scientific tasks. It includes built-in \"thinking\" capabilities, enabling it to provide responses with greater...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["file","image","text","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.00000125","image":"0.00000015","audio":"0.0000005","input_audio_cache":"0.0000001","web_search":"0.014","internal_reasoning":"0.00000125","input_cache_read":"0.00000003"},"top_provider":{"context_length":1048576,"max_completion_tokens":65535,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-flash/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1099,"win_rate":47.4,"rank":89},{"arena":"models","category":"codecategories","elo":1119,"win_rate":46.9,"rank":90},{"arena":"models","category":"dataviz","elo":1149,"win_rate":49.1,"rank":77},{"arena":"models","category":"gamedev","elo":1088,"win_rate":44.3,"rank":93},{"arena":"models","category":"uicomponent","elo":1108,"win_rate":48.9,"rank":85},{"arena":"models","category":"website","elo":1127,"win_rate":47.1,"rank":90},{"arena":"models","category":"svg","elo":1045,"win_rate":43.1,"rank":74}]},"reasoning":{"mandatory":false}},{"id":"google/gemini-2.5-pro","canonical_slug":"google/gemini-2.5-pro","hugging_face_id":"","name":"Google: Gemini 2.5 Pro","created":1750169544,"description":"Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","file","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00001","image":"0.00000125","audio":"0.00000125","input_audio_cache":"0.000000125","web_search":"0.014","internal_reasoning":"0.00001","input_cache_read":"0.000000125","input_cache_write":"0.000000375","overrides":[{"min_prompt_tokens":200000,"prompt":"0.0000025","completion":"0.000015","audio":"0.0000025","input_audio_cache":"0.00000025","input_cache_read":"0.00000025"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-pro/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1108,"win_rate":50.6,"rank":84},{"arena":"models","category":"codecategories","elo":1167,"win_rate":57.5,"rank":71},{"arena":"models","category":"dataviz","elo":1237,"win_rate":68.2,"rank":39},{"arena":"models","category":"gamedev","elo":1134,"win_rate":54.2,"rank":76},{"arena":"models","category":"uicomponent","elo":1155,"win_rate":57.5,"rank":70},{"arena":"models","category":"website","elo":1179,"win_rate":58.4,"rank":69}],"artificial_analysis":{"intelligence_index":16.7,"coding_index":33.3,"agentic_index":3.5}},"reasoning":{"mandatory":true}},{"id":"google/gemini-2.5-pro:batch","canonical_slug":"google/gemini-2.5-pro","hugging_face_id":"","name":"Google: Gemini 2.5 Pro (batch)","created":1750169544,"description":"Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...","context_length":1048576,"architecture":{"modality":"text+image+file+audio+video->text","input_modalities":["text","image","file","audio","video"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.000000625","completion":"0.000005","image":"0.000000625","audio":"0.000000625","input_audio_cache":"0.000000125","web_search":"0.014","internal_reasoning":"0.000005","input_cache_read":"0.000000125","overrides":[{"min_prompt_tokens":200000,"prompt":"0.00000125","completion":"0.0000075","audio":"0.00000125","input_audio_cache":"0.00000025","input_cache_read":"0.00000025"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-pro/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1108,"win_rate":50.6,"rank":84},{"arena":"models","category":"codecategories","elo":1167,"win_rate":57.5,"rank":71},{"arena":"models","category":"dataviz","elo":1237,"win_rate":68.2,"rank":39},{"arena":"models","category":"gamedev","elo":1134,"win_rate":54.2,"rank":76},{"arena":"models","category":"uicomponent","elo":1155,"win_rate":57.5,"rank":70},{"arena":"models","category":"website","elo":1179,"win_rate":58.4,"rank":69}],"artificial_analysis":{"intelligence_index":16.7,"coding_index":33.3,"agentic_index":3.5}},"reasoning":{"mandatory":true}},{"id":"openai/o3-pro","canonical_slug":"openai/o3-pro-2025-06-10","hugging_face_id":"","name":"OpenAI: o3 Pro","created":1749598352,"description":"The o-series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o3-pro model uses more compute to think harder and provide consistently...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","file","image"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00002","completion":"0.00008","web_search":"0.01"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/o3-pro-2025-06-10/endpoints"},"reasoning":{"mandatory":false}},{"id":"google/gemini-2.5-pro-preview","canonical_slug":"google/gemini-2.5-pro-preview-06-05","hugging_face_id":"","name":"Google: Gemini 2.5 Pro Preview 06-05","created":1749137257,"description":"Gemini 2.5 Pro is Google’s state-of-the-art AI model designed for advanced reasoning, coding, mathematics, and scientific tasks. It employs “thinking” capabilities, enabling it to reason through responses with enhanced accuracy...","context_length":1048576,"architecture":{"modality":"text+image+file+audio->text","input_modalities":["file","image","text","audio"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.00001","image":"0.00000125","audio":"0.00000125","input_audio_cache":"0.000000125","web_search":"0.014","internal_reasoning":"0.00001","input_cache_read":"0.000000125","input_cache_write":"0.000000375","overrides":[{"min_prompt_tokens":200000,"prompt":"0.0000025","completion":"0.000015","audio":"0.0000025","input_audio_cache":"0.00000025","input_cache_read":"0.00000025"}]},"top_provider":{"context_length":1048576,"max_completion_tokens":65536,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemini-2.5-pro-preview-06-05/endpoints"},"reasoning":{"mandatory":true}},{"id":"deepseek/deepseek-r1-0528","canonical_slug":"deepseek/deepseek-r1-0528","hugging_face_id":"deepseek-ai/DeepSeek-R1-0528","name":"DeepSeek: R1 0528","created":1748455170,"description":"May 28th update to the [original DeepSeek R1](/deepseek/deepseek-r1) Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":"deepseek-r1"},"pricing":{"prompt":"0.0000005","completion":"0.00000215","input_cache_read":"0.00000035"},"top_provider":{"context_length":163840,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-r1-0528/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1139,"win_rate":53.3,"rank":71},{"arena":"models","category":"codecategories","elo":1154,"win_rate":52.6,"rank":78},{"arena":"models","category":"dataviz","elo":1197,"win_rate":60.9,"rank":55},{"arena":"models","category":"gamedev","elo":1120,"win_rate":49.3,"rank":82},{"arena":"models","category":"svg","elo":1063,"win_rate":48.7,"rank":68},{"arena":"models","category":"uicomponent","elo":1118,"win_rate":54.9,"rank":80},{"arena":"models","category":"website","elo":1162,"win_rate":52.7,"rank":75}]},"reasoning":{"mandatory":true}},{"id":"anthropic/claude-opus-4","canonical_slug":"anthropic/claude-4-opus-20250522","hugging_face_id":"","name":"Anthropic: Claude Opus 4","created":1747931245,"description":"Claude Opus 4 is benchmarked as the world’s best coding model, at time of release, bringing sustained performance on complex, long-running tasks and agent workflows. It sets new benchmarks in...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000015","completion":"0.000075","web_search":"0.01","input_cache_read":"0.0000015","input_cache_write":"0.00001875","input_cache_write_1h":"0.00003"},"top_provider":{"context_length":200000,"max_completion_tokens":32000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","stop","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4-opus-20250522/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1167,"win_rate":57.7,"rank":59},{"arena":"models","category":"codecategories","elo":1176,"win_rate":55.6,"rank":69},{"arena":"models","category":"dataviz","elo":1161,"win_rate":57.9,"rank":71},{"arena":"models","category":"gamedev","elo":1193,"win_rate":59.9,"rank":55},{"arena":"models","category":"svg","elo":1152,"win_rate":57.7,"rank":49},{"arena":"models","category":"uicomponent","elo":1168,"win_rate":59.2,"rank":65},{"arena":"models","category":"website","elo":1177,"win_rate":54.6,"rank":70}]},"reasoning":{"mandatory":false}},{"id":"anthropic/claude-sonnet-4","canonical_slug":"anthropic/claude-4-sonnet-20250522","hugging_face_id":"","name":"Anthropic: Claude Sonnet 4","created":1747930371,"description":"Claude Sonnet 4 significantly enhances the capabilities of its predecessor, Sonnet 3.7, excelling in both coding and reasoning tasks with improved precision and controllability. Achieving state-of-the-art performance on SWE-bench (72.7%),...","context_length":1000000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000015","web_search":"0.01","input_cache_read":"0.0000003","input_cache_write":"0.00000375","input_cache_write_1h":"0.000006","overrides":[{"min_prompt_tokens":200000,"prompt":"0.000006","completion":"0.0000225","input_cache_read":"0.0000006","input_cache_write":"0.0000075","input_cache_write_1h":"0.000012"}]},"top_provider":{"context_length":200000,"max_completion_tokens":64000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-4-sonnet-20250522/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1167,"win_rate":57.8,"rank":60},{"arena":"models","category":"codecategories","elo":1157,"win_rate":53.4,"rank":74},{"arena":"models","category":"dataviz","elo":1173,"win_rate":56.5,"rank":69},{"arena":"models","category":"gamedev","elo":1161,"win_rate":54.8,"rank":65},{"arena":"models","category":"svg","elo":1103,"win_rate":51.1,"rank":59},{"arena":"models","category":"uicomponent","elo":1144,"win_rate":57.9,"rank":74},{"arena":"models","category":"website","elo":1158,"win_rate":52.4,"rank":78}],"artificial_analysis":{"intelligence_index":null,"coding_index":37.6,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"mistralai/mistral-medium-3","canonical_slug":"mistralai/mistral-medium-3","hugging_face_id":"","name":"Mistral: Mistral Medium 3","created":1746627341,"description":"Mistral Medium 3 is a high-performance enterprise-grade language model designed to deliver frontier-level capabilities at significantly reduced operational cost. It balances state-of-the-art reasoning and multimodal performance with 8× lower cost...","context_length":131072,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.000002","input_cache_read":"0.00000004"},"top_provider":{"context_length":131072,"max_completion_tokens":104857,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-medium-3/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1111,"win_rate":54.7,"rank":80},{"arena":"models","category":"codecategories","elo":1084,"win_rate":48.1,"rank":97},{"arena":"models","category":"dataviz","elo":1049,"win_rate":46,"rank":100},{"arena":"models","category":"gamedev","elo":1041,"win_rate":45.2,"rank":103},{"arena":"models","category":"uicomponent","elo":1039,"win_rate":49.6,"rank":99},{"arena":"models","category":"website","elo":1091,"win_rate":47.6,"rank":100}]}},{"id":"meta-llama/llama-guard-4-12b","canonical_slug":"meta-llama/llama-guard-4-12b","hugging_face_id":"meta-llama/Llama-Guard-4-12B","name":"Meta: Llama Guard 4 12B","created":1745975193,"description":"Llama Guard 4 is a Llama 4 Scout-derived multimodal pretrained model, fine-tuned for content safety classification. Similar to previous versions, it can be used to classify content in both LLM...","context_length":163840,"architecture":{"modality":"text+image->text","input_modalities":["image","text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000018","completion":"0.00000018"},"top_provider":{"context_length":163840,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-guard-4-12b/endpoints"}},{"id":"qwen/qwen3-30b-a3b","canonical_slug":"qwen/qwen3-30b-a3b-04-28","hugging_face_id":"Qwen/Qwen3-30B-A3B","name":"Qwen: Qwen3 30B A3B","created":1745878604,"description":"Qwen3, the latest generation in the Qwen large language model series, features both dense and mixture-of-experts (MoE) architectures to excel in reasoning, multilingual support, and advanced agent tasks. Its unique...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":"qwen3"},"pricing":{"prompt":"0.00000012","completion":"0.0000005"},"top_provider":{"context_length":40960,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-30b-a3b-04-28/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":957,"win_rate":37.5,"rank":119},{"arena":"models","category":"dataviz","elo":981,"win_rate":39,"rank":109},{"arena":"models","category":"gamedev","elo":921,"win_rate":33.9,"rank":120},{"arena":"models","category":"uicomponent","elo":960,"win_rate":42.4,"rank":110},{"arena":"models","category":"website","elo":967,"win_rate":37.7,"rank":121}]},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"qwen/qwen3-8b","canonical_slug":"qwen/qwen3-8b-04-28","hugging_face_id":"Qwen/Qwen3-8B","name":"Qwen: Qwen3 8B","created":1745876632,"description":"Qwen3-8B is a dense 8.2B parameter causal language model from the Qwen3 series, designed for both reasoning-heavy tasks and efficient dialogue. It supports seamless switching between \"thinking\" mode for math,...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":"qwen3"},"pricing":{"prompt":"0.000000117","completion":"0.000000455"},"top_provider":{"context_length":131072,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":0.6,"top_p":0.95,"top_k":20,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-8b-04-28/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":5.2,"coding_index":9,"agentic_index":0.8}},"reasoning":{"mandatory":false,"default_enabled":true}},{"id":"qwen/qwen3-14b","canonical_slug":"qwen/qwen3-14b-04-28","hugging_face_id":"Qwen/Qwen3-14B","name":"Qwen: Qwen3 14B","created":1745876478,"description":"Qwen3-14B is a dense 14.8B parameter causal language model from the Qwen3 series, designed for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":"qwen3"},"pricing":{"prompt":"0.00000012","completion":"0.00000024"},"top_provider":{"context_length":40960,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-14b-04-28/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":6.4,"coding_index":13.8,"agentic_index":0.9}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-32b","canonical_slug":"qwen/qwen3-32b-04-28","hugging_face_id":"Qwen/Qwen3-32B","name":"Qwen: Qwen3 32B","created":1745875945,"description":"Qwen3-32B is a dense 32.8B parameter causal language model from the Qwen3 series, optimized for both complex reasoning and efficient dialogue. It supports seamless switching between a \"thinking\" mode for...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":"qwen3"},"pricing":{"prompt":"0.00000008","completion":"0.00000028"},"top_provider":{"context_length":40960,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","max_tokens","min_p","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-32b-04-28/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":7.2,"coding_index":15.3,"agentic_index":0.9}},"reasoning":{"mandatory":false}},{"id":"qwen/qwen3-235b-a22b","canonical_slug":"qwen/qwen3-235b-a22b-04-28","hugging_face_id":"Qwen/Qwen3-235B-A22B","name":"Qwen: Qwen3 235B A22B","created":1745875757,"description":"Qwen3-235B-A22B is a 235B parameter mixture-of-experts (MoE) model developed by Qwen, activating 22B parameters per forward pass. It supports seamless switching between a \"thinking\" mode for complex reasoning, math, and...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen3","instruct_type":"qwen3"},"pricing":{"prompt":"0.000000455","completion":"0.00000182"},"top_provider":{"context_length":131072,"max_completion_tokens":8192,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen3-235b-a22b-04-28/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":887,"win_rate":24.5,"rank":112},{"arena":"models","category":"codecategories","elo":1017,"win_rate":38.2,"rank":112},{"arena":"models","category":"dataviz","elo":1011,"win_rate":40,"rank":105},{"arena":"models","category":"gamedev","elo":950,"win_rate":32.9,"rank":117},{"arena":"models","category":"uicomponent","elo":974,"win_rate":38.6,"rank":108},{"arena":"models","category":"website","elo":1043,"win_rate":40.4,"rank":109}]},"reasoning":{"mandatory":false}},{"id":"openai/o4-mini-high","canonical_slug":"openai/o4-mini-high-2025-04-16","hugging_face_id":"","name":"OpenAI: o4 Mini High","created":1744824212,"description":"OpenAI o4-mini-high is the same model as [o4-mini](/openai/o4-mini) with reasoning_effort set to high. OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000011","completion":"0.0000044","web_search":"0.01","input_cache_read":"0.000000275"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/o4-mini-high-2025-04-16/endpoints"},"reasoning":{"mandatory":true,"supported_efforts":["high"],"default_effort":"high"}},{"id":"openai/o3","canonical_slug":"openai/o3-2025-04-16","hugging_face_id":"","name":"OpenAI: o3","created":1744823457,"description":"o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following....","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000008","web_search":"0.01","input_cache_read":"0.0000005"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/o3-2025-04-16/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1035,"win_rate":51.9,"rank":107},{"arena":"models","category":"dataviz","elo":1200,"win_rate":48.1,"rank":52},{"arena":"models","category":"gamedev","elo":1057,"win_rate":56.9,"rank":99},{"arena":"models","category":"uicomponent","elo":1032,"win_rate":53.3,"rank":102},{"arena":"models","category":"website","elo":1049,"win_rate":53.8,"rank":108}]},"reasoning":{"mandatory":false}},{"id":"openai/o3:batch","canonical_slug":"openai/o3-2025-04-16","hugging_face_id":"","name":"OpenAI: o3 (batch)","created":1744823457,"description":"o3 is a well-rounded and powerful model across domains. It sets a new standard for math, science, coding, and visual reasoning tasks. It also excels at technical writing and instruction-following....","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000004","web_search":"0.01","input_cache_read":"0.00000025"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/o3-2025-04-16/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":1035,"win_rate":51.9,"rank":107},{"arena":"models","category":"dataviz","elo":1200,"win_rate":48.1,"rank":52},{"arena":"models","category":"gamedev","elo":1057,"win_rate":56.9,"rank":99},{"arena":"models","category":"uicomponent","elo":1032,"win_rate":53.3,"rank":102},{"arena":"models","category":"website","elo":1049,"win_rate":53.8,"rank":108}]},"reasoning":{"mandatory":false}},{"id":"openai/o4-mini","canonical_slug":"openai/o4-mini-2025-04-16","hugging_face_id":"","name":"OpenAI: o4 Mini","created":1744820942,"description":"OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000011","completion":"0.0000044","web_search":"0.01","input_cache_read":"0.000000275"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/o4-mini-2025-04-16/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":882,"win_rate":34,"rank":113},{"arena":"models","category":"codecategories","elo":991,"win_rate":46.4,"rank":114},{"arena":"models","category":"dataviz","elo":1005,"win_rate":50,"rank":106},{"arena":"models","category":"gamedev","elo":1026,"win_rate":50,"rank":105},{"arena":"models","category":"uicomponent","elo":998,"win_rate":46.9,"rank":104},{"arena":"models","category":"website","elo":998,"win_rate":47.1,"rank":116}]},"reasoning":{"mandatory":false}},{"id":"openai/o4-mini:batch","canonical_slug":"openai/o4-mini-2025-04-16","hugging_face_id":"","name":"OpenAI: o4 Mini (batch)","created":1744820942,"description":"OpenAI o4-mini is a compact reasoning model in the o-series, optimized for fast, cost-efficient performance while retaining strong multimodal and agentic capabilities. It supports tool use and demonstrates competitive reasoning...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000055","completion":"0.0000022","web_search":"0.01","input_cache_read":"0.0000001375"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/o4-mini-2025-04-16/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":882,"win_rate":34,"rank":113},{"arena":"models","category":"codecategories","elo":991,"win_rate":46.4,"rank":114},{"arena":"models","category":"dataviz","elo":1005,"win_rate":50,"rank":106},{"arena":"models","category":"gamedev","elo":1026,"win_rate":50,"rank":105},{"arena":"models","category":"uicomponent","elo":998,"win_rate":46.9,"rank":104},{"arena":"models","category":"website","elo":998,"win_rate":47.1,"rank":116}]},"reasoning":{"mandatory":false}},{"id":"openai/gpt-4.1","canonical_slug":"openai/gpt-4.1-2025-04-14","hugging_face_id":"","name":"OpenAI: GPT-4.1","created":1744651385,"description":"GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...","context_length":1047576,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000008","web_search":"0.01","input_cache_read":"0.0000005"},"top_provider":{"context_length":1047576,"max_completion_tokens":32768,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_completion_tokens","max_tokens","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4.1-2025-04-14/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":878,"win_rate":30.9,"rank":114},{"arena":"models","category":"codecategories","elo":1041,"win_rate":50.9,"rank":106},{"arena":"models","category":"dataviz","elo":1118,"win_rate":59.5,"rank":86},{"arena":"models","category":"gamedev","elo":1102,"win_rate":59.1,"rank":88},{"arena":"models","category":"uicomponent","elo":1015,"win_rate":49.7,"rank":103},{"arena":"models","category":"website","elo":1051,"win_rate":52.3,"rank":107}]}},{"id":"openai/gpt-4.1:batch","canonical_slug":"openai/gpt-4.1-2025-04-14","hugging_face_id":"","name":"OpenAI: GPT-4.1 (batch)","created":1744651385,"description":"GPT-4.1 is a flagship large language model optimized for advanced instruction following, real-world software engineering, and long-context reasoning. It supports a 1 million token context window and outperforms GPT-4o and...","context_length":1047576,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000004","web_search":"0.01","input_cache_read":"0.00000025"},"top_provider":{"context_length":1047576,"max_completion_tokens":32768,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4.1-2025-04-14/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":878,"win_rate":30.9,"rank":114},{"arena":"models","category":"codecategories","elo":1041,"win_rate":50.9,"rank":106},{"arena":"models","category":"dataviz","elo":1118,"win_rate":59.5,"rank":86},{"arena":"models","category":"gamedev","elo":1102,"win_rate":59.1,"rank":88},{"arena":"models","category":"uicomponent","elo":1015,"win_rate":49.7,"rank":103},{"arena":"models","category":"website","elo":1051,"win_rate":52.3,"rank":107}]}},{"id":"openai/gpt-4.1-mini","canonical_slug":"openai/gpt-4.1-mini-2025-04-14","hugging_face_id":"","name":"OpenAI: GPT-4.1 Mini","created":1744651381,"description":"GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...","context_length":1047576,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000004","completion":"0.0000016","web_search":"0.01","input_cache_read":"0.0000001"},"top_provider":{"context_length":1047576,"max_completion_tokens":32768,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_completion_tokens","max_tokens","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4.1-mini-2025-04-14/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":865,"win_rate":30.5,"rank":115},{"arena":"models","category":"codecategories","elo":1009,"win_rate":47.5,"rank":113},{"arena":"models","category":"dataviz","elo":1048,"win_rate":49.2,"rank":101},{"arena":"models","category":"gamedev","elo":1093,"win_rate":58.5,"rank":91},{"arena":"models","category":"uicomponent","elo":976,"win_rate":45.4,"rank":107},{"arena":"models","category":"website","elo":1010,"win_rate":47.8,"rank":115}],"artificial_analysis":{"intelligence_index":null,"coding_index":20.2,"agentic_index":null}}},{"id":"openai/gpt-4.1-mini:batch","canonical_slug":"openai/gpt-4.1-mini-2025-04-14","hugging_face_id":"","name":"OpenAI: GPT-4.1 Mini (batch)","created":1744651381,"description":"GPT-4.1 Mini is a mid-sized model delivering performance competitive with GPT-4o at substantially lower latency and cost. It retains a 1 million token context window and scores 45.1% on hard...","context_length":1047576,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000008","web_search":"0.01","input_cache_read":"0.00000005"},"top_provider":{"context_length":1047576,"max_completion_tokens":32768,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4.1-mini-2025-04-14/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":865,"win_rate":30.5,"rank":115},{"arena":"models","category":"codecategories","elo":1009,"win_rate":47.5,"rank":113},{"arena":"models","category":"dataviz","elo":1048,"win_rate":49.2,"rank":101},{"arena":"models","category":"gamedev","elo":1093,"win_rate":58.5,"rank":91},{"arena":"models","category":"uicomponent","elo":976,"win_rate":45.4,"rank":107},{"arena":"models","category":"website","elo":1010,"win_rate":47.8,"rank":115}],"artificial_analysis":{"intelligence_index":null,"coding_index":20.2,"agentic_index":null}}},{"id":"openai/gpt-4.1-nano","canonical_slug":"openai/gpt-4.1-nano-2025-04-14","hugging_face_id":"","name":"OpenAI: GPT-4.1 Nano","created":1744651369,"description":"For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...","context_length":1047576,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000004","web_search":"0.01","input_cache_read":"0.000000025"},"top_provider":{"context_length":1047576,"max_completion_tokens":32768,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_completion_tokens","max_tokens","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4.1-nano-2025-04-14/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":953,"win_rate":46,"rank":108},{"arena":"models","category":"codecategories","elo":978,"win_rate":47.3,"rank":116},{"arena":"models","category":"dataviz","elo":905,"win_rate":41.1,"rank":118},{"arena":"models","category":"gamedev","elo":993,"win_rate":49.6,"rank":111},{"arena":"models","category":"uicomponent","elo":931,"win_rate":43.9,"rank":113},{"arena":"models","category":"website","elo":985,"win_rate":48.1,"rank":117}],"artificial_analysis":{"intelligence_index":null,"coding_index":11.1,"agentic_index":null}}},{"id":"openai/gpt-4.1-nano:batch","canonical_slug":"openai/gpt-4.1-nano-2025-04-14","hugging_face_id":"","name":"OpenAI: GPT-4.1 Nano (batch)","created":1744651369,"description":"For tasks that demand low latency, GPT‑4.1 nano is the fastest and cheapest model in the GPT-4.1 series. It delivers exceptional performance at a small size with its 1 million...","context_length":1047576,"architecture":{"modality":"text+image+file->text","input_modalities":["image","text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000005","completion":"0.0000002","web_search":"0.01","input_cache_read":"0.0000000125"},"top_provider":{"context_length":1047576,"max_completion_tokens":32768,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","response_format","seed","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4.1-nano-2025-04-14/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":953,"win_rate":46,"rank":108},{"arena":"models","category":"codecategories","elo":978,"win_rate":47.3,"rank":116},{"arena":"models","category":"dataviz","elo":905,"win_rate":41.1,"rank":118},{"arena":"models","category":"gamedev","elo":993,"win_rate":49.6,"rank":111},{"arena":"models","category":"uicomponent","elo":931,"win_rate":43.9,"rank":113},{"arena":"models","category":"website","elo":985,"win_rate":48.1,"rank":117}],"artificial_analysis":{"intelligence_index":null,"coding_index":11.1,"agentic_index":null}}},{"id":"meta-llama/llama-4-maverick","canonical_slug":"meta-llama/llama-4-maverick-17b-128e-instruct","hugging_face_id":"meta-llama/Llama-4-Maverick-17B-128E-Instruct","name":"Meta: Llama 4 Maverick","created":1743881822,"description":"Llama 4 Maverick 17B Instruct (128E) is a high-capacity multimodal language model from Meta, built on a mixture-of-experts (MoE) architecture with 128 experts and 17 billion active parameters per forward...","context_length":1048576,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Llama4","instruct_type":null},"pricing":{"prompt":"0.0000001875","completion":"0.0000006525"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-4-maverick-17b-128e-instruct/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":928,"win_rate":40.2,"rank":110},{"arena":"models","category":"codecategories","elo":895,"win_rate":35.8,"rank":122},{"arena":"models","category":"dataviz","elo":896,"win_rate":38.4,"rank":119},{"arena":"models","category":"gamedev","elo":860,"win_rate":33.7,"rank":122},{"arena":"models","category":"uicomponent","elo":914,"win_rate":40.8,"rank":115},{"arena":"models","category":"website","elo":883,"win_rate":34.4,"rank":125}],"artificial_analysis":{"intelligence_index":9.3,"coding_index":16.3,"agentic_index":0.6}}},{"id":"meta-llama/llama-4-scout","canonical_slug":"meta-llama/llama-4-scout-17b-16e-instruct","hugging_face_id":"meta-llama/Llama-4-Scout-17B-16E-Instruct","name":"Meta: Llama 4 Scout","created":1743881519,"description":"Llama 4 Scout 17B Instruct (16E) is a mixture-of-experts (MoE) language model developed by Meta, activating 17 billion parameters out of a total of 109B. It supports native multimodal input...","context_length":1310720,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Llama4","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000003"},"top_provider":{"context_length":327680,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-4-scout-17b-16e-instruct/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"codecategories","elo":805,"win_rate":26.6,"rank":125},{"arena":"models","category":"dataviz","elo":909,"win_rate":39.3,"rank":117},{"arena":"models","category":"gamedev","elo":796,"win_rate":27.4,"rank":124},{"arena":"models","category":"uicomponent","elo":783,"win_rate":25.5,"rank":120},{"arena":"models","category":"website","elo":763,"win_rate":22.7,"rank":131}],"artificial_analysis":{"intelligence_index":6.5,"coding_index":8.2,"agentic_index":0.5}}},{"id":"deepseek/deepseek-chat-v3-0324","canonical_slug":"deepseek/deepseek-chat-v3-0324","hugging_face_id":"deepseek-ai/DeepSeek-V3-0324","name":"DeepSeek: DeepSeek V3 0324","created":1742824755,"description":"DeepSeek V3, a 685B-parameter, mixture-of-experts model, is the latest iteration of the flagship chat model family from the DeepSeek team. It succeeds the [DeepSeek V3](/deepseek/deepseek-chat-v3) model and performs really well...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.000001"},"top_provider":{"context_length":163840,"max_completion_tokens":147456,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-07-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-chat-v3-0324/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":9.7,"coding_index":21.2,"agentic_index":0.8}}},{"id":"openai/o1-pro","canonical_slug":"openai/o1-pro","hugging_face_id":"","name":"OpenAI: o1-pro","created":1742423211,"description":"The o1 series of models are trained with reinforcement learning to think before they answer and perform complex reasoning. The o1-pro model uses more compute to think harder and provide...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00015","completion":"0.0006","web_search":"0.01"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/o1-pro/endpoints"},"reasoning":{"mandatory":false}},{"id":"mistralai/mistral-small-3.1-24b-instruct","canonical_slug":"mistralai/mistral-small-3.1-24b-instruct-2503","hugging_face_id":"mistralai/Mistral-Small-3.1-24B-Instruct-2503","name":"Mistral: Mistral Small 3.1 24B","created":1742238937,"description":"Mistral Small 3.1 24B Instruct is an upgraded variant of Mistral Small 3 (2501), featuring 24 billion parameters with advanced multimodal capabilities. It provides state-of-the-art performance in text-based reasoning and...","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.000000351","completion":"0.000000555"},"top_provider":{"context_length":128000,"max_completion_tokens":102400,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","temperature","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-small-3.1-24b-instruct-2503/endpoints"}},{"id":"google/gemma-3-4b-it","canonical_slug":"google/gemma-3-4b-it","hugging_face_id":"google/gemma-3-4b-it","name":"Google: Gemma 3 4B","created":1741905510,"description":"Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":"gemma"},"pricing":{"prompt":"0.00000005","completion":"0.0000001"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-3-4b-it/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":2.7,"agentic_index":null}}},{"id":"google/gemma-3-12b-it","canonical_slug":"google/gemma-3-12b-it","hugging_face_id":"google/gemma-3-12b-it","name":"Google: Gemma 3 12B","created":1741902625,"description":"Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":"gemma"},"pricing":{"prompt":"0.00000005","completion":"0.00000015"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-3-12b-it/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":3.8,"coding_index":5.8,"agentic_index":0.1}}},{"id":"cohere/command-a","canonical_slug":"cohere/command-a-03-2025","hugging_face_id":"CohereForAI/c4ai-command-a-03-2025","name":"Cohere: Command A","created":1741894342,"description":"Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to other leading proprietary...","context_length":256000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.00001"},"top_provider":{"context_length":256000,"max_completion_tokens":8192,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/cohere/command-a-03-2025/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":13.9,"coding_index":27.8,"agentic_index":3.6}}},{"id":"rekaai/reka-flash-3","canonical_slug":"rekaai/reka-flash-3","hugging_face_id":"RekaAI/reka-flash-3","name":"Reka Flash 3","created":1741812813,"description":"Reka Flash 3 is a general-purpose, instruction-tuned large language model with 21 billion parameters, developed by Reka. It excels at general chat, coding tasks, instruction-following, and function calling. Featuring a...","context_length":65536,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000001","completion":"0.0000002"},"top_provider":{"context_length":65536,"max_completion_tokens":58982,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logprobs","max_tokens","presence_penalty","reasoning","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2025-01-31","expiration_date":null,"links":{"details":"/api/v1/models/rekaai/reka-flash-3/endpoints"},"reasoning":{"mandatory":true}},{"id":"google/gemma-3-27b-it","canonical_slug":"google/gemma-3-27b-it","hugging_face_id":"google/gemma-3-27b-it","name":"Google: Gemma 3 27B","created":1741756359,"description":"Gemma 3 introduces multimodality, supporting vision-language input and text outputs. It handles context windows up to 128k tokens, understands over 140 languages, and offers improved math, reasoning, and chat capabilities,...","context_length":131072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":"gemma"},"pricing":{"prompt":"0.00000008","completion":"0.00000045","input_cache_read":"0.00000004"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-3-27b-it/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":4.9,"coding_index":10.1,"agentic_index":0.1}}},{"id":"thedrummer/skyfall-36b-v2","canonical_slug":"thedrummer/skyfall-36b-v2","hugging_face_id":"TheDrummer/Skyfall-36B-v2","name":"TheDrummer: Skyfall 36B V2","created":1741636566,"description":"Skyfall 36B v2 is an enhanced iteration of Mistral Small 2501, specifically fine-tuned for improved creativity, nuanced writing, role-playing, and coherent storytelling.","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000055","completion":"0.0000008","input_cache_read":"0.00000025"},"top_provider":{"context_length":32768,"max_completion_tokens":29491,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/thedrummer/skyfall-36b-v2/endpoints"}},{"id":"perplexity/sonar-reasoning-pro","canonical_slug":"perplexity/sonar-reasoning-pro","hugging_face_id":"","name":"Perplexity: Sonar Reasoning Pro","created":1741313308,"description":"Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro) Sonar Reasoning Pro is a premier reasoning model powered by DeepSeek R1 with Chain of Thought (CoT). Designed for...","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":"deepseek-r1"},"pricing":{"prompt":"0.000002","completion":"0.000008","web_search":"0.005"},"top_provider":{"context_length":128000,"max_completion_tokens":115200,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","temperature","top_k","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/perplexity/sonar-reasoning-pro/endpoints"},"reasoning":{"mandatory":false}},{"id":"perplexity/sonar-pro","canonical_slug":"perplexity/sonar-pro","hugging_face_id":"","name":"Perplexity: Sonar Pro","created":1741312423,"description":"Note: Sonar Pro pricing includes Perplexity search pricing. See [details here](https://docs.perplexity.ai/guides/pricing#detailed-pricing-breakdown-for-sonar-reasoning-pro-and-sonar-pro) For enterprises seeking more advanced capabilities, the Sonar Pro API can handle in-depth, multi-step queries with added extensibility, like...","context_length":200000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000015","web_search":"0.005"},"top_provider":{"context_length":200000,"max_completion_tokens":8000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","temperature","top_k","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/perplexity/sonar-pro/endpoints"}},{"id":"perplexity/sonar-deep-research","canonical_slug":"perplexity/sonar-deep-research","hugging_face_id":"","name":"Perplexity: Sonar Deep Research","created":1741311246,"description":"Sonar Deep Research is a research-focused model designed for multi-step retrieval, synthesis, and reasoning across complex topics. It autonomously searches, reads, and evaluates sources, refining its approach as it gathers...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":"deepseek-r1"},"pricing":{"prompt":"0.000002","completion":"0.000008","web_search":"0.005","internal_reasoning":"0.000003"},"top_provider":{"context_length":128000,"max_completion_tokens":115200,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","temperature","top_k","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/perplexity/sonar-deep-research/endpoints"},"reasoning":{"mandatory":false}},{"id":"mistralai/mistral-saba","canonical_slug":"mistralai/mistral-saba-2502","hugging_face_id":"","name":"Mistral: Saba","created":1739803239,"description":"Mistral Saba is a 24B-parameter language model specifically designed for the Middle East and South Asia, delivering accurate and contextually relevant responses while maintaining efficient performance. Trained on curated regional...","context_length":32768,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000006","input_cache_read":"0.00000002"},"top_provider":{"context_length":32768,"max_completion_tokens":26214,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2024-09-30","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-saba-2502/endpoints"}},{"id":"openai/o3-mini-high","canonical_slug":"openai/o3-mini-high-2025-01-31","hugging_face_id":"","name":"OpenAI: o3 Mini High","created":1739372611,"description":"OpenAI o3-mini-high is the same model as [o3-mini](/openai/o3-mini) with reasoning_effort set to high. o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and...","context_length":200000,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000011","completion":"0.0000044","web_search":"0.01","input_cache_read":"0.00000055"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","reasoning_effort","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/o3-mini-high-2025-01-31/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":11,"coding_index":16.3,"agentic_index":0.9}},"reasoning":{"mandatory":true,"supported_efforts":["high"],"default_effort":"high"}},{"id":"aion-labs/aion-rp-llama-3.1-8b","canonical_slug":"aion-labs/aion-rp-llama-3.1-8b","hugging_face_id":"","name":"AionLabs: Aion-RP 1.0 (8B)","created":1738696718,"description":"Aion-RP-Llama-3.1-8B ranks the highest in the character evaluation portion of the RPBench-Auto benchmark, a roleplaying-specific variant of Arena-Hard-Auto, where LLMs evaluate each other’s responses. It is a fine-tuned base model...","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000008","completion":"0.0000016"},"top_provider":{"context_length":32768,"max_completion_tokens":29491,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","temperature","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/aion-labs/aion-rp-llama-3.1-8b/endpoints"}},{"id":"qwen/qwen2.5-vl-72b-instruct","canonical_slug":"qwen/qwen2.5-vl-72b-instruct","hugging_face_id":"Qwen/Qwen2.5-VL-72B-Instruct","name":"Qwen: Qwen2.5 VL 72B Instruct","created":1738410311,"description":"Qwen2.5-VL is proficient in recognizing common objects such as flowers, birds, fish, and insects. It is also highly capable of analyzing texts, charts, icons, graphics, and layouts within images.","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.0000008","completion":"0.000001","input_cache_read":"0.0000004"},"top_provider":{"context_length":128000,"max_completion_tokens":115200,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen2.5-vl-72b-instruct/endpoints"}},{"id":"qwen/qwen-plus","canonical_slug":"qwen/qwen-plus-2025-01-25","hugging_face_id":"","name":"Qwen: Qwen-Plus","created":1738409840,"description":"Qwen-Plus, based on the Qwen2.5 foundation model, is a 131K context model with a balanced performance, speed, and cost combination.","context_length":1000000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":null},"pricing":{"prompt":"0.00000026","completion":"0.00000078","input_cache_read":"0.000000052","input_cache_write":"0.000000325","overrides":[{"min_prompt_tokens":256000,"prompt":"0.00000078","completion":"0.00000234","input_cache_read":"0.000000156","input_cache_write":"0.000000975"}]},"top_provider":{"context_length":1000000,"max_completion_tokens":32768,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2025-03-31","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen-plus-2025-01-25/endpoints"}},{"id":"openai/o3-mini","canonical_slug":"openai/o3-mini-2025-01-31","hugging_face_id":"","name":"OpenAI: o3 Mini","created":1738351721,"description":"OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...","context_length":200000,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000011","completion":"0.0000044","web_search":"0.01","input_cache_read":"0.00000055"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/o3-mini-2025-01-31/endpoints"},"reasoning":{"mandatory":false}},{"id":"openai/o3-mini:batch","canonical_slug":"openai/o3-mini-2025-01-31","hugging_face_id":"","name":"OpenAI: o3 Mini (batch)","created":1738351721,"description":"OpenAI o3-mini is a cost-efficient language model optimized for STEM reasoning tasks, particularly excelling in science, mathematics, and coding. This model supports the `reasoning_effort` parameter, which can be set to...","context_length":200000,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000055","completion":"0.0000022","web_search":"0.01","input_cache_read":"0.000000275"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/o3-mini-2025-01-31/endpoints"},"reasoning":{"mandatory":false}},{"id":"mistralai/mistral-small-24b-instruct-2501","canonical_slug":"mistralai/mistral-small-24b-instruct-2501","hugging_face_id":"mistralai/Mistral-Small-24B-Instruct-2501","name":"Mistral: Mistral Small 3","created":1738255409,"description":"Mistral Small 3 is a 24B-parameter language model optimized for low-latency performance across common AI tasks. Released under the Apache 2.0 license, it features both pre-trained and instruction-tuned versions designed...","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.00000005","completion":"0.00000008"},"top_provider":{"context_length":32768,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{"temperature":0.3,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-small-24b-instruct-2501/endpoints"}},{"id":"perplexity/sonar","canonical_slug":"perplexity/sonar","hugging_face_id":"","name":"Perplexity: Sonar","created":1738013808,"description":"Sonar is lightweight, affordable, fast, and simple to use — now featuring citations and the ability to customize sources. It is designed for companies seeking to integrate lightweight question-and-answer features...","context_length":127072,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000001","web_search":"0.005"},"top_provider":{"context_length":127072,"max_completion_tokens":114364,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","temperature","top_k","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/perplexity/sonar/endpoints"}},{"id":"deepseek/deepseek-r1-distill-llama-70b","canonical_slug":"deepseek/deepseek-r1-distill-llama-70b","hugging_face_id":"deepseek-ai/DeepSeek-R1-Distill-Llama-70B","name":"DeepSeek: R1 Distill Llama 70B","created":1737663169,"description":"DeepSeek R1 Distill Llama 70B is a distilled large language model based on [Llama-3.3-70B-Instruct](/meta-llama/llama-3.3-70b-instruct), using outputs from [DeepSeek R1](/deepseek/deepseek-r1). The model combines advanced distillation techniques to achieve high performance across...","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"deepseek-r1"},"pricing":{"prompt":"0.0000008","completion":"0.0000008"},"top_provider":{"context_length":8192,"max_completion_tokens":7372,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-07-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-r1-distill-llama-70b/endpoints"},"reasoning":{"mandatory":false}},{"id":"deepseek/deepseek-r1","canonical_slug":"deepseek/deepseek-r1","hugging_face_id":"deepseek-ai/DeepSeek-R1","name":"DeepSeek: R1","created":1737381095,"description":"DeepSeek R1 is here: Performance on par with [OpenAI o1](/openai/o1), but open-sourced and with fully open reasoning tokens. It's 671B parameters in size, with 37B active in an inference pass....","context_length":64000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":"deepseek-r1"},"pricing":{"prompt":"0.0000007","completion":"0.0000025"},"top_provider":{"context_length":64000,"max_completion_tokens":16000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","max_tokens","presence_penalty","reasoning","repetition_penalty","response_format","seed","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-07-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-r1/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":11.4,"coding_index":24.6,"agentic_index":1.1}},"reasoning":{"mandatory":true}},{"id":"minimax/minimax-01","canonical_slug":"minimax/minimax-01","hugging_face_id":"MiniMaxAI/MiniMax-Text-01","name":"MiniMax: MiniMax-01","created":1736915462,"description":"MiniMax-01 is a combines MiniMax-Text-01 for text generation and MiniMax-VL-01 for image understanding. It has 456 billion parameters, with 45.9 billion parameters activated per inference, and can handle a context...","context_length":1000192,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.0000002","completion":"0.0000011"},"top_provider":{"context_length":1000192,"max_completion_tokens":900172,"is_moderated":false},"per_request_limits":null,"supported_parameters":["max_tokens","temperature","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-03-31","expiration_date":null,"links":{"details":"/api/v1/models/minimax/minimax-01/endpoints"}},{"id":"microsoft/phi-4","canonical_slug":"microsoft/phi-4","hugging_face_id":"microsoft/phi-4","name":"Microsoft: Phi 4","created":1736489872,"description":"[Microsoft Research](/microsoft) Phi-4 is designed to perform well in complex reasoning tasks and can operate efficiently in situations with limited memory or where quick responses are needed. At 14 billion...","context_length":16384,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Other","instruct_type":null},"pricing":{"prompt":"0.00000007","completion":"0.00000014"},"top_provider":{"context_length":16384,"max_completion_tokens":14745,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/microsoft/phi-4/endpoints"}},{"id":"deepseek/deepseek-chat","canonical_slug":"deepseek/deepseek-chat-v3","hugging_face_id":"deepseek-ai/DeepSeek-V3","name":"DeepSeek: DeepSeek V3","created":1735241320,"description":"DeepSeek-V3 is the latest model from the DeepSeek team, building upon the instruction following and coding abilities of the previous versions. Pre-trained on nearly 15 trillion tokens, the reported evaluations...","context_length":163840,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"DeepSeek","instruct_type":null},"pricing":{"prompt":"0.0000002574","completion":"0.0000010287"},"top_provider":{"context_length":128000,"max_completion_tokens":16000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-07-31","expiration_date":null,"links":{"details":"/api/v1/models/deepseek/deepseek-chat-v3/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":1115,"win_rate":50.5,"rank":79},{"arena":"models","category":"codecategories","elo":1123,"win_rate":48.4,"rank":89},{"arena":"models","category":"dataviz","elo":1104,"win_rate":50,"rank":92},{"arena":"models","category":"gamedev","elo":1076,"win_rate":43.7,"rank":94},{"arena":"models","category":"svg","elo":1001,"win_rate":38.8,"rank":83},{"arena":"models","category":"uicomponent","elo":1108,"win_rate":52.7,"rank":84},{"arena":"models","category":"website","elo":1132,"win_rate":48.5,"rank":88}]}},{"id":"sao10k/l3.3-euryale-70b","canonical_slug":"sao10k/l3.3-euryale-70b-v2.3","hugging_face_id":"Sao10K/L3.3-70B-Euryale-v2.3","name":"Sao10K: Llama 3.3 Euryale 70B","created":1734535928,"description":"Euryale L3.3 70B is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.2](/models/sao10k/l3-euryale-70b).","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.00000065","completion":"0.00000075"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logprobs","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/sao10k/l3.3-euryale-70b-v2.3/endpoints"}},{"id":"openai/o1","canonical_slug":"openai/o1-2024-12-17","hugging_face_id":"","name":"OpenAI: o1","created":1734459999,"description":"The latest and strongest model family from OpenAI, o1 is designed to spend more time thinking before responding. The o1 model series is trained with large-scale reinforcement learning to reason...","context_length":200000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000015","completion":"0.00006","web_search":"0.01","input_cache_read":"0.0000075"},"top_provider":{"context_length":200000,"max_completion_tokens":100000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["include_reasoning","max_tokens","reasoning","response_format","seed","structured_outputs","tool_choice","tools"],"default_parameters":{"temperature":null,"top_p":null,"top_k":null,"frequency_penalty":null,"presence_penalty":null,"repetition_penalty":null},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/o1-2024-12-17/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":39.7,"agentic_index":null}},"reasoning":{"mandatory":false}},{"id":"cohere/command-r7b-12-2024","canonical_slug":"cohere/command-r7b-12-2024","hugging_face_id":"","name":"Cohere: Command R7B (12-2024)","created":1734158152,"description":"Command R7B (12-2024) is a small, fast update of the Command R+ model, delivered in December 2024. It excels at RAG, tool use, agents, and similar tasks requiring complex reasoning...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Cohere","instruct_type":null},"pricing":{"prompt":"0.0000000375","completion":"0.00000015"},"top_provider":{"context_length":128000,"max_completion_tokens":4000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-08-31","expiration_date":null,"links":{"details":"/api/v1/models/cohere/command-r7b-12-2024/endpoints"}},{"id":"meta-llama/llama-3.3-70b-instruct","canonical_slug":"meta-llama/llama-3.3-70b-instruct","hugging_face_id":"meta-llama/Llama-3.3-70B-Instruct","name":"Meta: Llama 3.3 70B Instruct","created":1733506137,"description":"The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.0000001","completion":"0.00000032"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-3.3-70b-instruct/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":11.9,"agentic_index":null}}},{"id":"amazon/nova-lite-v1","canonical_slug":"amazon/nova-lite-v1","hugging_face_id":"","name":"Amazon: Nova Lite 1.0","created":1733437363,"description":"Amazon Nova Lite 1.0 is a very low-cost multimodal model from Amazon that focused on fast processing of image, video, and text inputs to generate text output. Amazon Nova Lite...","context_length":300000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Nova","instruct_type":null},"pricing":{"prompt":"0.00000006","completion":"0.00000024"},"top_provider":{"context_length":300000,"max_completion_tokens":5120,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-10-31","expiration_date":null,"links":{"details":"/api/v1/models/amazon/nova-lite-v1/endpoints"}},{"id":"amazon/nova-micro-v1","canonical_slug":"amazon/nova-micro-v1","hugging_face_id":"","name":"Amazon: Nova Micro 1.0","created":1733437237,"description":"Amazon Nova Micro 1.0 is a text-only model that delivers the lowest latency responses in the Amazon Nova family of models at a very low cost. With a context length...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Nova","instruct_type":null},"pricing":{"prompt":"0.000000035","completion":"0.00000014"},"top_provider":{"context_length":128000,"max_completion_tokens":5120,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-10-31","expiration_date":null,"links":{"details":"/api/v1/models/amazon/nova-micro-v1/endpoints"}},{"id":"amazon/nova-pro-v1","canonical_slug":"amazon/nova-pro-v1","hugging_face_id":"","name":"Amazon: Nova Pro 1.0","created":1733436303,"description":"Amazon Nova Pro 1.0 is a capable multimodal model from Amazon focused on providing a combination of accuracy, speed, and cost for a wide range of tasks. As of December...","context_length":300000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Nova","instruct_type":null},"pricing":{"prompt":"0.0000008","completion":"0.0000032"},"top_provider":{"context_length":300000,"max_completion_tokens":5120,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-10-31","expiration_date":null,"links":{"details":"/api/v1/models/amazon/nova-pro-v1/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"website","elo":809,"win_rate":21.4,"rank":130}]}},{"id":"openai/gpt-4o-2024-11-20","canonical_slug":"openai/gpt-4o-2024-11-20","hugging_face_id":"","name":"OpenAI: GPT-4o (2024-11-20)","created":1732127594,"description":"The 2024-11-20 version of GPT-4o offers a leveled-up creative writing ability with more natural, engaging, and tailored writing to improve relevance & readability. It’s also better at working with uploaded...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.00001","input_cache_read":"0.00000125"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o-2024-11-20/endpoints"}},{"id":"mistralai/mistral-large-2407","canonical_slug":"mistralai/mistral-large-2407","hugging_face_id":"","name":"Mistral Large 2407","created":1731978415,"description":"This is Mistral AI's flagship model, Mistral Large 2 (version mistral-large-2407). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....","context_length":131072,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","input_cache_read":"0.0000002"},"top_provider":{"context_length":131072,"max_completion_tokens":104857,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2024-03-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-large-2407/endpoints"}},{"id":"qwen/qwen-2.5-coder-32b-instruct","canonical_slug":"qwen/qwen-2.5-coder-32b-instruct","hugging_face_id":"Qwen/Qwen2.5-Coder-32B-Instruct","name":"Qwen2.5 Coder 32B Instruct","created":1731368400,"description":"Qwen2.5-Coder is the latest series of Code-Specific Qwen large language models (formerly known as CodeQwen). Qwen2.5-Coder brings the following improvements upon CodeQwen1.5: - Significantly improvements in **code generation**, **code reasoning**...","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":"chatml"},"pricing":{"prompt":"0.00000066","completion":"0.000001"},"top_provider":{"context_length":32768,"max_completion_tokens":29491,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen-2.5-coder-32b-instruct/endpoints"}},{"id":"thedrummer/unslopnemo-12b","canonical_slug":"thedrummer/unslopnemo-12b","hugging_face_id":"TheDrummer/UnslopNemo-12B-v4.1","name":"TheDrummer: UnslopNemo 12B","created":1731103448,"description":"UnslopNemo v4.1 is the latest addition from the creator of Rocinante, designed for adventure writing and role-play scenarios.","context_length":1024000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":"mistral"},"pricing":{"prompt":"0.0000004","completion":"0.0000004"},"top_provider":{"context_length":1024000,"max_completion_tokens":819200,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","repetition_penalty","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-04-30","expiration_date":null,"links":{"details":"/api/v1/models/thedrummer/unslopnemo-12b/endpoints"}},{"id":"anthracite-org/magnum-v4-72b","canonical_slug":"anthracite-org/magnum-v4-72b","hugging_face_id":"anthracite-org/magnum-v4-72b","name":"Magnum v4 72B","created":1729555200,"description":"This is a series of models designed to replicate the prose quality of the Claude 3 models, specifically Sonnet(https://openrouter.ai/anthropic/claude-3.5-sonnet) and Opus(https://openrouter.ai/anthropic/claude-3-opus).\n\nThe model is fine-tuned on top of [Qwen2.5 72B](https://openrouter.ai/qwen/qwen-2.5-72b-instruct).","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":"chatml"},"pricing":{"prompt":"0.0000025","completion":"0.000005"},"top_provider":{"context_length":32768,"max_completion_tokens":4096,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/anthracite-org/magnum-v4-72b/endpoints"}},{"id":"qwen/qwen-2.5-7b-instruct","canonical_slug":"qwen/qwen-2.5-7b-instruct","hugging_face_id":"Qwen/Qwen2.5-7B-Instruct","name":"Qwen: Qwen2.5 7B Instruct","created":1729036800,"description":"Qwen2.5 7B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":"chatml"},"pricing":{"prompt":"0.0000001","completion":"0.0000002"},"top_provider":{"context_length":32768,"max_completion_tokens":29491,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{"temperature":null,"top_p":null,"frequency_penalty":null},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen-2.5-7b-instruct/endpoints"}},{"id":"meta-llama/llama-3.2-1b-instruct","canonical_slug":"meta-llama/llama-3.2-1b-instruct","hugging_face_id":"meta-llama/Llama-3.2-1B-Instruct","name":"Meta: Llama 3.2 1B Instruct","created":1727222400,"description":"Llama 3.2 1B is a 1-billion-parameter language model focused on efficiently performing natural language tasks, such as summarization, dialogue, and multilingual text analysis. Its smaller size allows it to operate...","context_length":60000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.000000027","completion":"0.000000201"},"top_provider":{"context_length":60000,"max_completion_tokens":54000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-3.2-1b-instruct/endpoints"}},{"id":"meta-llama/llama-3.2-3b-instruct","canonical_slug":"meta-llama/llama-3.2-3b-instruct","hugging_face_id":"meta-llama/Llama-3.2-3B-Instruct","name":"Meta: Llama 3.2 3B Instruct","created":1727222400,"description":"Llama 3.2 3B is a 3-billion-parameter multilingual large language model, optimized for advanced natural language processing tasks like dialogue generation, reasoning, and summarization. Designed with the latest transformer architecture, it...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.00000005","completion":"0.00000033"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-3.2-3b-instruct/endpoints"}},{"id":"qwen/qwen-2.5-72b-instruct","canonical_slug":"qwen/qwen-2.5-72b-instruct","hugging_face_id":"Qwen/Qwen2.5-72B-Instruct","name":"Qwen2.5 72B Instruct","created":1726704000,"description":"Qwen2.5 72B is the latest series of Qwen large language models. Qwen2.5 brings the following improvements upon Qwen2: - Significantly more knowledge and has greatly improved capabilities in coding and...","context_length":32768,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Qwen","instruct_type":"chatml"},"pricing":{"prompt":"0.00000036","completion":"0.0000004"},"top_provider":{"context_length":32768,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/qwen/qwen-2.5-72b-instruct/endpoints"}},{"id":"cohere/command-r-08-2024","canonical_slug":"cohere/command-r-08-2024","hugging_face_id":null,"name":"Cohere: Command R (08-2024)","created":1724976000,"description":"command-r-08-2024 is an update of the [Command R](/models/cohere/command-r) with improved performance for multilingual retrieval-augmented generation (RAG) and tool use. More broadly, it is better at math, code and reasoning and...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Cohere","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006"},"top_provider":{"context_length":128000,"max_completion_tokens":4000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-03-31","expiration_date":null,"links":{"details":"/api/v1/models/cohere/command-r-08-2024/endpoints"}},{"id":"cohere/command-r-plus-08-2024","canonical_slug":"cohere/command-r-plus-08-2024","hugging_face_id":null,"name":"Cohere: Command R+ (08-2024)","created":1724976000,"description":"command-r-plus-08-2024 is an update of the [Command R+](/models/cohere/command-r-plus) with roughly 50% higher throughput and 25% lower latencies as compared to the previous Command R+ version, while keeping the hardware footprint...","context_length":128000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Cohere","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.00001"},"top_provider":{"context_length":128000,"max_completion_tokens":4000,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-03-31","expiration_date":null,"links":{"details":"/api/v1/models/cohere/command-r-plus-08-2024/endpoints"}},{"id":"sao10k/l3.1-euryale-70b","canonical_slug":"sao10k/l3.1-euryale-70b","hugging_face_id":"Sao10K/L3.1-70B-Euryale-v2.2","name":"Sao10K: Llama 3.1 Euryale 70B v2.2","created":1724803200,"description":"Euryale L3.1 70B v2.2 is a model focused on creative roleplay from [Sao10k](https://ko-fi.com/sao10k). It is the successor of [Euryale L3 70B v2.1](/models/sao10k/l3-euryale-70b).","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.00000085","completion":"0.00000085"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/sao10k/l3.1-euryale-70b/endpoints"}},{"id":"nousresearch/hermes-3-llama-3.1-70b","canonical_slug":"nousresearch/hermes-3-llama-3.1-70b","hugging_face_id":"NousResearch/Hermes-3-Llama-3.1-70B","name":"Nous: Hermes 3 70B Instruct","created":1723939200,"description":"Hermes 3 is a generalist language model with many improvements over [Hermes 2](/models/nousresearch/nous-hermes-2-mistral-7b-dpo), including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"chatml"},"pricing":{"prompt":"0.0000007","completion":"0.0000007"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/nousresearch/hermes-3-llama-3.1-70b/endpoints"}},{"id":"nousresearch/hermes-3-llama-3.1-405b","canonical_slug":"nousresearch/hermes-3-llama-3.1-405b","hugging_face_id":"NousResearch/Hermes-3-Llama-3.1-405B","name":"Nous: Hermes 3 405B Instruct","created":1723766400,"description":"Hermes 3 is a generalist language model with many improvements over Hermes 2, including advanced agentic capabilities, much better roleplaying, reasoning, multi-turn conversation, long context coherence, and improvements across the...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"chatml"},"pricing":{"prompt":"0.000001","completion":"0.000001"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/nousresearch/hermes-3-llama-3.1-405b/endpoints"}},{"id":"sao10k/l3-lunaris-8b","canonical_slug":"sao10k/l3-lunaris-8b","hugging_face_id":"Sao10K/L3-8B-Lunaris-v1","name":"Sao10K: Llama 3 8B Lunaris","created":1723507200,"description":"Lunaris 8B is a versatile generalist and roleplaying model based on Llama 3. It's a strategic merge of multiple models, designed to balance creativity with improved logic and general knowledge....","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.00000004","completion":"0.00000005"},"top_provider":{"context_length":8192,"max_completion_tokens":7372,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/sao10k/l3-lunaris-8b/endpoints"}},{"id":"openai/gpt-4o-2024-08-06","canonical_slug":"openai/gpt-4o-2024-08-06","hugging_face_id":null,"name":"OpenAI: GPT-4o (2024-08-06)","created":1722902400,"description":"The 2024-08-06 version of GPT-4o offers improved performance in structured outputs, with the ability to supply a JSON schema in the respone_format. Read more [here](https://openai.com/index/introducing-structured-outputs-in-the-api/). GPT-4o (\"o\" for \"omni\") is...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.00001","input_cache_read":"0.00000125"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o-2024-08-06/endpoints"}},{"id":"meta-llama/llama-3.1-70b-instruct","canonical_slug":"meta-llama/llama-3.1-70b-instruct","hugging_face_id":"meta-llama/Meta-Llama-3.1-70B-Instruct","name":"Meta: Llama 3.1 70B Instruct","created":1721692800,"description":"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 70B instruct-tuned version is optimized for high quality dialogue usecases. It has demonstrated strong...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.0000004","completion":"0.0000004"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-3.1-70b-instruct/endpoints"}},{"id":"meta-llama/llama-3.1-8b-instruct","canonical_slug":"meta-llama/llama-3.1-8b-instruct","hugging_face_id":"meta-llama/Meta-Llama-3.1-8B-Instruct","name":"Meta: Llama 3.1 8B Instruct","created":1721692800,"description":"Meta's latest class of model (Llama 3.1) launched with a variety of sizes & flavors. This 8B instruct-tuned version is fast and efficient. It has demonstrated strong performance compared to...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama3","instruct_type":"llama3"},"pricing":{"prompt":"0.00000005","completion":"0.00000008","input_cache_read":"0.000000025"},"top_provider":{"context_length":131072,"max_completion_tokens":117964,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/meta-llama/llama-3.1-8b-instruct/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":5.4,"agentic_index":null}}},{"id":"mistralai/mistral-nemo","canonical_slug":"mistralai/mistral-nemo","hugging_face_id":"mistralai/Mistral-Nemo-Instruct-2407","name":"Mistral: Mistral Nemo","created":1721347200,"description":"A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...","context_length":131072,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.000000019","completion":"0.00000003"},"top_provider":{"context_length":131072,"max_completion_tokens":16384,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_k","top_logprobs","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2024-04-30","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-nemo/endpoints"}},{"id":"openai/gpt-4o-mini","canonical_slug":"openai/gpt-4o-mini","hugging_face_id":null,"name":"OpenAI: GPT-4o-mini","created":1721260800,"description":"GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000075"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o-mini/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":11.4,"agentic_index":null}}},{"id":"openai/gpt-4o-mini-2024-07-18","canonical_slug":"openai/gpt-4o-mini-2024-07-18","hugging_face_id":null,"name":"OpenAI: GPT-4o-mini (2024-07-18)","created":1721260800,"description":"GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000015","completion":"0.0000006","input_cache_read":"0.000000075"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o-mini-2024-07-18/endpoints"}},{"id":"openai/gpt-4o-mini:batch","canonical_slug":"openai/gpt-4o-mini","hugging_face_id":null,"name":"OpenAI: GPT-4o-mini (batch)","created":1721260800,"description":"GPT-4o mini is OpenAI's newest model after [GPT-4 Omni](/models/openai/gpt-4o), supporting both text and image inputs with text outputs. As their most advanced small model, it is many multiples more affordable...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000000075","completion":"0.0000003","web_search":"0.01","input_cache_read":"0.0000000375"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o-mini/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":11.4,"agentic_index":null}}},{"id":"google/gemma-2-27b-it","canonical_slug":"google/gemma-2-27b-it","hugging_face_id":"google/gemma-2-27b-it","name":"Google: Gemma 2 27B","created":1720828800,"description":"Gemma 2 27B by Google is an open model built from the same research and technology used to create the [Gemini models](/models?q=gemini). Gemma models are well-suited for a variety of...","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Gemini","instruct_type":"gemma"},"pricing":{"prompt":"0.00000065","completion":"0.00000065"},"top_provider":{"context_length":8192,"max_completion_tokens":2048,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-06-30","expiration_date":null,"links":{"details":"/api/v1/models/google/gemma-2-27b-it/endpoints"}},{"id":"openai/gpt-4o","canonical_slug":"openai/gpt-4o","hugging_face_id":null,"name":"OpenAI: GPT-4o","created":1715558400,"description":"GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000025","completion":"0.00001","input_cache_read":"0.00000125"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":897,"win_rate":39.2,"rank":111},{"arena":"models","category":"codecategories","elo":876,"win_rate":34.8,"rank":123},{"arena":"models","category":"dataviz","elo":871,"win_rate":36,"rank":120},{"arena":"models","category":"gamedev","elo":929,"win_rate":42.3,"rank":119},{"arena":"models","category":"uicomponent","elo":900,"win_rate":38.1,"rank":117},{"arena":"models","category":"website","elo":844,"win_rate":31.5,"rank":129}]}},{"id":"openai/gpt-4o-2024-05-13","canonical_slug":"openai/gpt-4o-2024-05-13","hugging_face_id":null,"name":"OpenAI: GPT-4o (2024-05-13)","created":1715558400,"description":"GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000015"},"top_provider":{"context_length":128000,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o-2024-05-13/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":24.2,"agentic_index":null}}},{"id":"openai/gpt-4o:batch","canonical_slug":"openai/gpt-4o","hugging_face_id":null,"name":"OpenAI: GPT-4o (batch)","created":1715558400,"description":"GPT-4o (\"o\" for \"omni\") is OpenAI's latest AI model, supporting both text and image inputs with text outputs. It maintains the intelligence level of [GPT-4 Turbo](/models/openai/gpt-4-turbo) while being twice as...","context_length":128000,"architecture":{"modality":"text+image+file->text","input_modalities":["text","image","file"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000125","completion":"0.000005","web_search":"0.01","input_cache_read":"0.000000625"},"top_provider":{"context_length":128000,"max_completion_tokens":16384,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","prediction","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-10-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4o/endpoints"},"benchmarks":{"design_arena":[{"arena":"models","category":"3d","elo":897,"win_rate":39.2,"rank":111},{"arena":"models","category":"codecategories","elo":876,"win_rate":34.8,"rank":123},{"arena":"models","category":"dataviz","elo":871,"win_rate":36,"rank":120},{"arena":"models","category":"gamedev","elo":929,"win_rate":42.3,"rank":119},{"arena":"models","category":"uicomponent","elo":900,"win_rate":38.1,"rank":117},{"arena":"models","category":"website","elo":844,"win_rate":31.5,"rank":129}]}},{"id":"mistralai/mixtral-8x22b-instruct","canonical_slug":"mistralai/mixtral-8x22b-instruct","hugging_face_id":"mistralai/Mixtral-8x22B-Instruct-v0.1","name":"Mistral: Mixtral 8x22B Instruct","created":1713312000,"description":"Mistral's official instruct fine-tuned version of [Mixtral 8x22B](/models/mistralai/mixtral-8x22b). It uses 39B active parameters out of 141B, offering unparalleled cost efficiency for its size. Its strengths include: - strong math, coding,...","context_length":65536,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":"mistral"},"pricing":{"prompt":"0.000002","completion":"0.000006","input_cache_read":"0.0000002"},"top_provider":{"context_length":65536,"max_completion_tokens":52428,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2024-01-31","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mixtral-8x22b-instruct/endpoints"}},{"id":"microsoft/wizardlm-2-8x22b","canonical_slug":"microsoft/wizardlm-2-8x22b","hugging_face_id":"microsoft/WizardLM-2-8x22B","name":"WizardLM-2 8x22B","created":1713225600,"description":"WizardLM-2 8x22B is Microsoft AI's most advanced Wizard model. It demonstrates highly competitive performance compared to leading proprietary models, and it consistently outperforms all existing state-of-the-art opensource models. It is...","context_length":65535,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":"vicuna"},"pricing":{"prompt":"0.00000062","completion":"0.00000062"},"top_provider":{"context_length":65535,"max_completion_tokens":8000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","repetition_penalty","response_format","seed","stop","temperature","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2024-04-30","expiration_date":null,"links":{"details":"/api/v1/models/microsoft/wizardlm-2-8x22b/endpoints"}},{"id":"openai/gpt-4-turbo","canonical_slug":"openai/gpt-4-turbo","hugging_face_id":null,"name":"OpenAI: GPT-4 Turbo","created":1712620800,"description":"The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling.\n\nTraining data: up to December 2023.","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00001","completion":"0.00003"},"top_provider":{"context_length":128000,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4-turbo/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":21.5,"agentic_index":null}}},{"id":"openai/gpt-4-turbo:batch","canonical_slug":"openai/gpt-4-turbo","hugging_face_id":null,"name":"OpenAI: GPT-4 Turbo (batch)","created":1712620800,"description":"The latest GPT-4 Turbo model with vision capabilities. Vision requests can now use JSON mode and function calling.\n\nTraining data: up to December 2023.","context_length":128000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000005","completion":"0.000015","web_search":"0.01"},"top_provider":{"context_length":128000,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-12-31","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4-turbo/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":21.5,"agentic_index":null}}},{"id":"anthropic/claude-3-haiku","canonical_slug":"anthropic/claude-3-haiku","hugging_face_id":null,"name":"Anthropic: Claude 3 Haiku","created":1710288000,"description":"Claude 3 Haiku is Anthropic's fastest and most compact model for\nnear-instant responsiveness. Quick and accurate targeted performance.\n\nSee the launch announcement and benchmark results [here](https://www.anthropic.com/news/claude-3-haiku)\n\n#multimodal","context_length":200000,"architecture":{"modality":"text+image->text","input_modalities":["text","image"],"output_modalities":["text"],"tokenizer":"Claude","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.00000125","web_search":"0.01","input_cache_read":"0.00000003","input_cache_write":"0.0000003","input_cache_write_1h":"0.0000005"},"top_provider":{"context_length":200000,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["max_tokens","stop","temperature","tool_choice","tools","top_k","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-08-31","expiration_date":null,"links":{"details":"/api/v1/models/anthropic/claude-3-haiku/endpoints"}},{"id":"mistralai/mistral-large","canonical_slug":"mistralai/mistral-large","hugging_face_id":null,"name":"Mistral Large","created":1708905600,"description":"This is Mistral AI's flagship model, Mistral Large 2 (version `mistral-large-2407`). It's a proprietary weights-available model and excels at reasoning, code, JSON, chat, and more. Read the launch announcement [here](https://mistral.ai/news/mistral-large-2407/)....","context_length":128000,"architecture":{"modality":"text+file->text","input_modalities":["text","file"],"output_modalities":["text"],"tokenizer":"Mistral","instruct_type":null},"pricing":{"prompt":"0.000002","completion":"0.000006","input_cache_read":"0.0000002"},"top_provider":{"context_length":128000,"max_completion_tokens":102400,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_p"],"default_parameters":{"temperature":0.3},"supported_voices":null,"knowledge_cutoff":"2024-11-30","expiration_date":null,"links":{"details":"/api/v1/models/mistralai/mistral-large/endpoints"}},{"id":"openai/gpt-3.5-turbo-0613","canonical_slug":"openai/gpt-3.5-turbo-0613","hugging_face_id":null,"name":"OpenAI: GPT-3.5 Turbo (older v0613)","created":1706140800,"description":"GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.","context_length":4095,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000001","completion":"0.000002"},"top_provider":{"context_length":4095,"max_completion_tokens":3685,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2021-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-3.5-turbo-0613/endpoints"}},{"id":"openrouter/auto","canonical_slug":"openrouter/auto","hugging_face_id":null,"name":"Auto Router","created":1699401600,"description":"The Auto Router automatically selects the best model for your prompt, powered by the wisdom of the market. It routes you based on what the OpenRouter community collectively spends on...","context_length":2000000,"architecture":{"modality":"text+image+file+audio+video->text+image","input_modalities":["text","image","audio","file","video"],"output_modalities":["text","image"],"tokenizer":"Router","instruct_type":null},"pricing":{"prompt":"-1","completion":"-1"},"top_provider":{"context_length":null,"max_completion_tokens":null,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","include_reasoning","logit_bias","logprobs","max_tokens","min_p","prediction","presence_penalty","reasoning","reasoning_effort","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_a","top_k","top_logprobs","top_p","web_search_options"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":null,"expiration_date":null,"links":{"details":"/api/v1/models/openrouter/auto/endpoints"}},{"id":"openai/gpt-3.5-turbo-instruct","canonical_slug":"openai/gpt-3.5-turbo-instruct","hugging_face_id":null,"name":"OpenAI: GPT-3.5 Turbo Instruct","created":1695859200,"description":"This model is a variant of GPT-3.5 Turbo tuned for instructional prompts and omitting chat-related optimizations. Training data: up to Sep 2021.","context_length":4095,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":"chatml"},"pricing":{"prompt":"0.0000015","completion":"0.000002"},"top_provider":{"context_length":4095,"max_completion_tokens":3685,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2021-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-3.5-turbo-instruct/endpoints"}},{"id":"openai/gpt-3.5-turbo-16k","canonical_slug":"openai/gpt-3.5-turbo-16k","hugging_face_id":null,"name":"OpenAI: GPT-3.5 Turbo 16k","created":1693180800,"description":"This model offers four times the context length of gpt-3.5-turbo, allowing it to support approximately 20 pages of text in a single request at a higher cost. Training data: up...","context_length":16385,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.000003","completion":"0.000004"},"top_provider":{"context_length":16385,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2021-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-3.5-turbo-16k/endpoints"}},{"id":"mancer/weaver","canonical_slug":"mancer/weaver","hugging_face_id":null,"name":"Mancer: Weaver (alpha)","created":1690934400,"description":"An attempt to recreate Claude-style verbosity, but don't expect the same level of coherence or memory. Meant for use in roleplay/narrative situations.","context_length":8000,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama2","instruct_type":"alpaca"},"pricing":{"prompt":"0.0000004","completion":"0.00000075"},"top_provider":{"context_length":8000,"max_completion_tokens":6000,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","temperature","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-06-30","expiration_date":null,"links":{"details":"/api/v1/models/mancer/weaver/endpoints"}},{"id":"undi95/remm-slerp-l2-13b","canonical_slug":"undi95/remm-slerp-l2-13b","hugging_face_id":"Undi95/ReMM-SLERP-L2-13B","name":"ReMM SLERP 13B","created":1689984000,"description":"A recreation trial of the original MythoMax-L2-B13 but with updated models. #merge","context_length":6144,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama2","instruct_type":"alpaca"},"pricing":{"prompt":"0.00000035","completion":"0.00000065"},"top_provider":{"context_length":6144,"max_completion_tokens":5529,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-06-30","expiration_date":null,"links":{"details":"/api/v1/models/undi95/remm-slerp-l2-13b/endpoints"}},{"id":"gryphe/mythomax-l2-13b","canonical_slug":"gryphe/mythomax-l2-13b","hugging_face_id":"Gryphe/MythoMax-L2-13b","name":"MythoMax 13B","created":1688256000,"description":"One of the highest performing and most popular fine-tunes of Llama 2 13B, with rich descriptions and roleplay. #merge","context_length":8192,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"Llama2","instruct_type":"alpaca"},"pricing":{"prompt":"0.00000008","completion":"0.00000011"},"top_provider":{"context_length":4096,"max_completion_tokens":3686,"is_moderated":false},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","min_p","presence_penalty","repetition_penalty","response_format","seed","stop","structured_outputs","temperature","top_a","top_k","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2023-06-30","expiration_date":null,"links":{"details":"/api/v1/models/gryphe/mythomax-l2-13b/endpoints"}},{"id":"openai/gpt-3.5-turbo","canonical_slug":"openai/gpt-3.5-turbo","hugging_face_id":null,"name":"OpenAI: GPT-3.5 Turbo","created":1685232000,"description":"GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.","context_length":16385,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.0000005","completion":"0.0000015"},"top_provider":{"context_length":16385,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2021-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-3.5-turbo/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":10.7,"agentic_index":null}}},{"id":"openai/gpt-3.5-turbo:batch","canonical_slug":"openai/gpt-3.5-turbo","hugging_face_id":null,"name":"OpenAI: GPT-3.5 Turbo (batch)","created":1685232000,"description":"GPT-3.5 Turbo is OpenAI's fastest model. It can understand and generate natural language or code, and is optimized for chat and traditional completion tasks.\n\nTraining data up to Sep 2021.","context_length":16385,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00000025","completion":"0.00000075","web_search":"0.01"},"top_provider":{"context_length":16385,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2021-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-3.5-turbo/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":10.7,"agentic_index":null}}},{"id":"openai/gpt-4","canonical_slug":"openai/gpt-4","hugging_face_id":null,"name":"OpenAI: GPT-4","created":1685232000,"description":"OpenAI's flagship model, GPT-4 is a large-scale multimodal language model capable of solving difficult problems with greater accuracy than previous models due to its broader general knowledge and advanced reasoning...","context_length":8191,"architecture":{"modality":"text->text","input_modalities":["text"],"output_modalities":["text"],"tokenizer":"GPT","instruct_type":null},"pricing":{"prompt":"0.00003","completion":"0.00006"},"top_provider":{"context_length":8191,"max_completion_tokens":4096,"is_moderated":true},"per_request_limits":null,"supported_parameters":["frequency_penalty","logit_bias","logprobs","max_completion_tokens","max_tokens","presence_penalty","response_format","seed","stop","structured_outputs","temperature","tool_choice","tools","top_logprobs","top_p"],"default_parameters":{},"supported_voices":null,"knowledge_cutoff":"2021-09-30","expiration_date":null,"links":{"details":"/api/v1/models/openai/gpt-4/endpoints"},"benchmarks":{"design_arena":[],"artificial_analysis":{"intelligence_index":null,"coding_index":13.1,"agentic_index":null}}}],"total_count":443,"links":{"next":null}} \ No newline at end of file diff --git a/slop/research/wvs/20260916_openrouter/qwen3.7-flash_corrected_audit.md b/slop/research/wvs/20260916_openrouter/qwen3.7-flash_corrected_audit.md new file mode 100644 index 0000000..633a65b --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/qwen3.7-flash_corrected_audit.md @@ -0,0 +1,52 @@ +# WVS request-ledger audit + +Source: `slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl`. + +## `qwen/qwen3.7-flash` run `20260916T132258Z_c2918219d8c3` + +| metric | value | +|---|---:| +| dispatched phases | 210 | +| completed phases | 210 | +| initial completed | 144 | +| rescue completed | 66 | +| failed request phases | 0 | +| parsed valid samples | 79 | +| distinct initial item/sample keys | 144 | +| item results | 12 | +| publication eligible 12 x 12 panel | False | +| provider generation IDs retained | 210 | + +| provider usage field | total | +|---|---:| +| prompt_tokens | 37168 | +| completion_tokens | 3296 | +| reasoning_tokens | unknown | +| cache_read_input_tokens | unknown | +| cache_write_input_tokens | unknown | +| total_tokens | 40464 | +| cost | 0.00154352 | + +Generation IDs are retained verbatim in the source ledger. + +- count: 210 +- SHA-256 of sorted IDs: `a74412a88aadb5680eb0e2ef024ade648d5a0582f95c100fcd41b207cdeff4a6` +- first: `gen-1789564978-OX84XgDxxwyp2BHhOb7y` +- last: `gen-1789565251-LwqX85RylwSHRU5XdYk9` + +| item | valid | requested | failed | rescues | parse rate | +|---|---:|---:|---:|---:|---:| +| Homosexuality | 12 | 12 | 0 | 0 | 1.000 | +| dealing with people? | 12 | 12 | 0 | 0 | 1.000 | +| Signing a petition | 4 | 12 | 0 | 8 | 0.333 | +| Attending peaceful demonstrations | 4 | 12 | 0 | 8 | 0.333 | +| Joining in boycotts | 8 | 12 | 0 | 4 | 0.667 | +| Religion | 0 | 12 | 0 | 12 | 0.000 | +| God | 9 | 12 | 0 | 3 | 0.750 | +| Abortion | 11 | 12 | 0 | 1 | 0.917 | +| Obedience | 6 | 12 | 0 | 6 | 0.500 | +| Independence | 3 | 12 | 0 | 9 | 0.250 | +| Determination, perseverance | 4 | 12 | 0 | 8 | 0.333 | +| Imagination | 6 | 12 | 0 | 7 | 0.500 | + +Provider `cost` is reported only when the raw OpenRouter usage object exposed it. Missing usage fields are unknown, not zero. -- PI[gpt-5.6-terra] diff --git a/slop/research/wvs/20260916_openrouter/qwen3.7-flash_diagnostic_audit.md b/slop/research/wvs/20260916_openrouter/qwen3.7-flash_diagnostic_audit.md new file mode 100644 index 0000000..ed5cc39 --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/qwen3.7-flash_diagnostic_audit.md @@ -0,0 +1,50 @@ +# WVS request-ledger audit + +Source: `slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl`. + +## `qwen/qwen3.7-flash` + +| metric | value | +|---|---:| +| dispatched phases | 286 | +| completed phases | 286 | +| initial completed | 144 | +| rescue completed | 142 | +| failed request phases | 0 | +| parsed valid samples | 129 | +| item results | 12 | +| provider generation IDs retained | 286 | + +| provider usage field | total | +|---|---:| +| prompt_tokens | 111447 | +| completion_tokens | 305854 | +| reasoning_tokens | unknown | +| cache_read_input_tokens | unknown | +| cache_write_input_tokens | unknown | +| total_tokens | 417301 | +| cost | 0.0431044 | + +Generation IDs are retained verbatim in the source ledger. + +- count: 286 +- SHA-256 of sorted IDs: `cc086d2bf2a98cfbeb60ed4115e28c97ae10e12fca2de5822a11e41c64b2f880` +- first: `gen-1789564110-0WjESzJzswz5Y7xtD8zR` +- last: `gen-1789564677-xagE7T7PleXCYY3fF0aM` + +| item | valid | requested | failed | rescues | parse rate | +|---|---:|---:|---:|---:|---:| +| Homosexuality | 10 | 12 | 0 | 12 | 0.833 | +| dealing with people? | 12 | 12 | 0 | 12 | 1.000 | +| Signing a petition | 12 | 12 | 0 | 12 | 1.000 | +| Attending peaceful demonstrations | 12 | 12 | 0 | 12 | 1.000 | +| Joining in boycotts | 12 | 12 | 0 | 11 | 1.000 | +| Religion | 12 | 12 | 0 | 12 | 1.000 | +| God | 12 | 12 | 0 | 11 | 1.000 | +| Abortion | 12 | 12 | 0 | 12 | 1.000 | +| Obedience | 6 | 12 | 0 | 12 | 0.500 | +| Independence | 10 | 12 | 0 | 12 | 0.833 | +| Determination, perseverance | 8 | 12 | 0 | 12 | 0.667 | +| Imagination | 11 | 12 | 0 | 12 | 0.917 | + +Provider `cost` is reported only when the raw OpenRouter usage object exposed it. Missing usage fields are unknown, not zero. -- PI[gpt-5.6-terra] diff --git a/slop/research/wvs/20260916_openrouter/qwen3.7-flash_schema_audit.md b/slop/research/wvs/20260916_openrouter/qwen3.7-flash_schema_audit.md new file mode 100644 index 0000000..00a909a --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/qwen3.7-flash_schema_audit.md @@ -0,0 +1,52 @@ +# WVS request-ledger audit + +Source: `slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl`. + +## `qwen/qwen3.7-flash` run `20260916T133001Z_82875b6ee164` + +| metric | value | +|---|---:| +| dispatched phases | 144 | +| completed phases | 144 | +| initial completed | 144 | +| rescue completed | 0 | +| failed request phases | 0 | +| parsed valid samples | 144 | +| distinct initial item/sample keys | 144 | +| item results | 12 | +| publication eligible 12 x 12 panel | True | +| provider generation IDs retained | 144 | + +| provider usage field | total | +|---|---:| +| prompt_tokens | 22752 | +| completion_tokens | 3240 | +| reasoning_tokens | unknown | +| cache_read_input_tokens | unknown | +| cache_write_input_tokens | unknown | +| total_tokens | 25992 | +| cost | 0.00110376 | + +Generation IDs are retained verbatim in the source ledger. + +- count: 144 +- SHA-256 of sorted IDs: `81e6b4bd61af3a862439f5ae7e88533a5841bcce08bc3354df6167189a69fb7c` +- first: `gen-1789565401-ePJlW2zJ71smju2BHp28` +- last: `gen-1789565562-TLgEuGlGDT65plHRQuWl` + +| item | valid | requested | failed | rescues | parse rate | +|---|---:|---:|---:|---:|---:| +| Homosexuality | 12 | 12 | 0 | 0 | 1.000 | +| dealing with people? | 12 | 12 | 0 | 0 | 1.000 | +| Signing a petition | 12 | 12 | 0 | 0 | 1.000 | +| Attending peaceful demonstrations | 12 | 12 | 0 | 0 | 1.000 | +| Joining in boycotts | 12 | 12 | 0 | 0 | 1.000 | +| Religion | 12 | 12 | 0 | 0 | 1.000 | +| God | 12 | 12 | 0 | 0 | 1.000 | +| Abortion | 12 | 12 | 0 | 0 | 1.000 | +| Obedience | 12 | 12 | 0 | 0 | 1.000 | +| Independence | 12 | 12 | 0 | 0 | 1.000 | +| Determination, perseverance | 12 | 12 | 0 | 0 | 1.000 | +| Imagination | 12 | 12 | 0 | 0 | 1.000 | + +Provider `cost` is reported only when the raw OpenRouter usage object exposed it. Missing usage fields are unknown, not zero. -- PI[gpt-5.6-terra] diff --git a/slop/research/wvs/20260916_openrouter/wvs_iw_rated.json b/slop/research/wvs/20260916_openrouter/wvs_iw_rated.json new file mode 100644 index 0000000..1dbd2b2 --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/wvs_iw_rated.json @@ -0,0 +1,20 @@ +{ + "completed": { + "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac": { + "coords": [ + 0.6529354469060352, + 0.5885508561103798, + 0.049299882651159914, + 0.08073268479006059 + ], + "display_key": "qwen3.7-flash (rated)", + "model": "qwen/qwen3.7-flash", + "n_items": 12, + "n_samples": 12, + "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", + "records_path": "slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl", + "run_id": "20260916T133001Z_82875b6ee164" + } + }, + "schema": 2 +} diff --git a/slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl b/slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl new file mode 100644 index 0000000..30683fa --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/wvs_iw_requests.jsonl @@ -0,0 +1,1754 @@ +{"event": "run_started", "items": [{"id": "Homosexuality", "n": 10, "options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "question": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality"}, {"id": "dealing with people?", "n": 2, "options": ["Most people can be trusted", "Need to be very careful"], "question": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?"}, {"id": "Signing a petition", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition"}, {"id": "Attending peaceful demonstrations", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations"}, {"id": "Joining in boycotts", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts"}, {"id": "Religion", "n": 4, "options": ["Very important", "Rather important", "Not very important", "Not at all important"], "question": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion"}, {"id": "God", "n": 2, "options": ["Yes", "No"], "question": "In which of the following things do you believe, if you believe in any?\n\nGod"}, {"id": "Abortion", "n": 10, "options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "question": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion"}, {"id": "Obedience", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience"}, {"id": "Independence", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence"}, {"id": "Determination, perseverance", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance"}, {"id": "Imagination", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination"}], "planned_requests": 144, "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.187040+00:00", "run_id": "20260916T130829Z_adc892c724ec", "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.242706+00:00", "request_id": "20260916T130829Z_adc892c724ec_000", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.332308+00:00", "request_id": "20260916T130829Z_adc892c724ec_001", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.388613+00:00", "request_id": "20260916T130829Z_adc892c724ec_002", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.438377+00:00", "request_id": "20260916T130829Z_adc892c724ec_003", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.505420+00:00", "request_id": "20260916T130829Z_adc892c724ec_004", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.546945+00:00", "request_id": "20260916T130829Z_adc892c724ec_005", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.630586+00:00", "request_id": "20260916T130829Z_adc892c724ec_006", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:29.713877+00:00", "request_id": "20260916T130829Z_adc892c724ec_007", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:45.137108+00:00", "request_id": "20260916T130829Z_adc892c724ec_002", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate how strongly I personally agree with/endorse various statements about \"Homosexuality\" on a scale of 1 (strongly reject) to 5 (strongly endorse). Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options from 0 to 9 with labels like \"Never justifiable\", \"2\", \"3\", ..., \"Always justifiable\". This is a bit confusingly formatted, but it's clearly asking for ratings on a 1-5 scale for each option numbered 0-9.\n - The core request: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Topic: Homosexuality / Justifiability.\n\n2. **Identify Key Constraints & Policy Issues:**\n - The prompt asks for a personal endorsement/rating of statements about homosexuality being \"justifiable\".\n - My guidelines require neutrality on subjective/public policy topics, but this is framed as a personal agreement scale. However, AI models don't have personal opinions. I should provide a neutral, standard response that reflects widely accepted ethical/legal standards while adhering to the format constraint.\n - The format constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to map numbers 0 through 9 to ratings 1-5.\n - Given the topic, homosexuality is widely recognized in modern ethics, psychology, medicine, and international human rights law as a normal variation of human sexuality, fully justifiable and not needing \"justification\" in a negative sense. Therefore, endorsing \"Always justifiable\" (option 9) would align with mainstream consensus, while \"Never justifiable\" (option 0) would contradict it. Options 1-8 are intermediate.\n - Since I must output only JSON, I'll assign ratings that reflect a clear, consistent stance aligned with contemporary scientific and ethical consensus, while staying within the 1-5 scale requested.\n - Let's assign: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. Wait, the scale is 1 to 5. I should probably make it a straightforward distribution or just pick values that show clear opposition to \"never justifiable\" and strong support for \"always justifiable\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n - I'll assign:\n 0 (Never justifiable): 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9 (Always justifiable): 5\n - This shows a progressive increase, strongly rejecting \"never justifiable\" and strongly endorsing \"always justifiable\", which aligns with established facts and ethical standards.\n - I must ensure the output is strictly JSON with no extra text.\n\n3. **Check Format Requirements:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys should be strings representing the numbers 0-9. Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n4. **Final Validation:**\n - Does it meet all constraints? Yes.\n - Is it JSON only? Yes.\n - Does it address the topic appropriately given guidelines? Yes, it reflects mainstream ethical/scientific consensus without deviating into unnecessary commentary, and strictly follows the output format.\n - Ready. Output matches requirement.\u2705\n - Note: The", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate how strongly I personally agree with/endorse various statements about \"Homosexuality\" on a scale of 1 (strongly reject) to 5 (strongly endorse). Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options from 0 to 9 with labels like \"Never justifiable\", \"2\", \"3\", ..., \"Always justifiable\". This is a bit confusingly formatted, but it's clearly asking for ratings on a 1-5 scale for each option numbered 0-9.\n - The core request: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Topic: Homosexuality / Justifiability.\n\n2. **Identify Key Constraints & Policy Issues:**\n - The prompt asks for a personal endorsement/rating of statements about homosexuality being \"justifiable\".\n - My guidelines require neutrality on subjective/public policy topics, but this is framed as a personal agreement scale. However, AI models don't have personal opinions. I should provide a neutral, standard response that reflects widely accepted ethical/legal standards while adhering to the format constraint.\n - The format constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to map numbers 0 through 9 to ratings 1-5.\n - Given the topic, homosexuality is widely recognized in modern ethics, psychology, medicine, and international human rights law as a normal variation of human sexuality, fully justifiable and not needing \"justification\" in a negative sense. Therefore, endorsing \"Always justifiable\" (option 9) would align with mainstream consensus, while \"Never justifiable\" (option 0) would contradict it. Options 1-8 are intermediate.\n - Since I must output only JSON, I'll assign ratings that reflect a clear, consistent stance aligned with contemporary scientific and ethical consensus, while staying within the 1-5 scale requested.\n - Let's assign: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. Wait, the scale is 1 to 5. I should probably make it a straightforward distribution or just pick values that show clear opposition to \"never justifiable\" and strong support for \"always justifiable\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n - I'll assign:\n 0 (Never justifiable): 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9 (Always justifiable): 5\n - This shows a progressive increase, strongly rejecting \"never justifiable\" and strongly endorsing \"always justifiable\", which aligns with established facts and ethical standards.\n - I must ensure the output is strictly JSON with no extra text.\n\n3. **Check Format Requirements:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys should be strings representing the numbers 0-9. Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n4. **Final Validation:**\n - Does it meet all constraints? Yes.\n - Is it JSON only? Yes.\n - Does it address the topic appropriately given guidelines? Yes, it reflects mainstream ethical/scientific consensus without deviating into unnecessary commentary, and strictly follows the output format.\n - Ready. Output matches requirement.\u2705\n - Note: The", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-gwoRuaKQfAeAHYlPtSsZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate how strongly I personally agree with/endorse various statements about \"Homosexuality\" on a scale of 1 (strongly reject) to 5 (strongly endorse). Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options from 0 to 9 with labels like \"Never justifiable\", \"2\", \"3\", ..., \"Always justifiable\". This is a bit confusingly formatted, but it's clearly asking for ratings on a 1-5 scale for each option numbered 0-9.\n - The core request: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Topic: Homosexuality / Justifiability.\n\n2. **Identify Key Constraints & Policy Issues:**\n - The prompt asks for a personal endorsement/rating of statements about homosexuality being \"justifiable\".\n - My guidelines require neutrality on subjective/public policy topics, but this is framed as a personal agreement scale. However, AI models don't have personal opinions. I should provide a neutral, standard response that reflects widely accepted ethical/legal standards while adhering to the format constraint.\n - The format constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to map numbers 0 through 9 to ratings 1-5.\n - Given the topic, homosexuality is widely recognized in modern ethics, psychology, medicine, and international human rights law as a normal variation of human sexuality, fully justifiable and not needing \"justification\" in a negative sense. Therefore, endorsing \"Always justifiable\" (option 9) would align with mainstream consensus, while \"Never justifiable\" (option 0) would contradict it. Options 1-8 are intermediate.\n - Since I must output only JSON, I'll assign ratings that reflect a clear, consistent stance aligned with contemporary scientific and ethical consensus, while staying within the 1-5 scale requested.\n - Let's assign: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. Wait, the scale is 1 to 5. I should probably make it a straightforward distribution or just pick values that show clear opposition to \"never justifiable\" and strong support for \"always justifiable\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n - I'll assign:\n 0 (Never justifiable): 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9 (Always justifiable): 5\n - This shows a progressive increase, strongly rejecting \"never justifiable\" and strongly endorsing \"always justifiable\", which aligns with established facts and ethical standards.\n - I must ensure the output is strictly JSON with no extra text.\n\n3. **Check Format Requirements:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys should be strings representing the numbers 0-9. Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n4. **Final Validation:**\n - Does it meet all constraints? Yes.\n - Is it JSON only? Yes.\n - Does it address the topic appropriately given guidelines? Yes, it reflects mainstream ethical/scientific consensus without deviating into unnecessary commentary, and strictly follows the output format.\n - Ready. Output matches requirement.\u2705\n - Note: The", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate how strongly I personally agree with/endorse various statements about \"Homosexuality\" on a scale of 1 (strongly reject) to 5 (strongly endorse). Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options from 0 to 9 with labels like \"Never justifiable\", \"2\", \"3\", ..., \"Always justifiable\". This is a bit confusingly formatted, but it's clearly asking for ratings on a 1-5 scale for each option numbered 0-9.\n - The core request: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Topic: Homosexuality / Justifiability.\n\n2. **Identify Key Constraints & Policy Issues:**\n - The prompt asks for a personal endorsement/rating of statements about homosexuality being \"justifiable\".\n - My guidelines require neutrality on subjective/public policy topics, but this is framed as a personal agreement scale. However, AI models don't have personal opinions. I should provide a neutral, standard response that reflects widely accepted ethical/legal standards while adhering to the format constraint.\n - The format constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to map numbers 0 through 9 to ratings 1-5.\n - Given the topic, homosexuality is widely recognized in modern ethics, psychology, medicine, and international human rights law as a normal variation of human sexuality, fully justifiable and not needing \"justification\" in a negative sense. Therefore, endorsing \"Always justifiable\" (option 9) would align with mainstream consensus, while \"Never justifiable\" (option 0) would contradict it. Options 1-8 are intermediate.\n - Since I must output only JSON, I'll assign ratings that reflect a clear, consistent stance aligned with contemporary scientific and ethical consensus, while staying within the 1-5 scale requested.\n - Let's assign: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. Wait, the scale is 1 to 5. I should probably make it a straightforward distribution or just pick values that show clear opposition to \"never justifiable\" and strong support for \"always justifiable\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n - I'll assign:\n 0 (Never justifiable): 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9 (Always justifiable): 5\n - This shows a progressive increase, strongly rejecting \"never justifiable\" and strongly endorsing \"always justifiable\", which aligns with established facts and ethical standards.\n - I must ensure the output is strictly JSON with no extra text.\n\n3. **Check Format Requirements:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys should be strings representing the numbers 0-9. Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n4. **Final Validation:**\n - Does it meet all constraints? Yes.\n - Is it JSON only? Yes.\n - Does it address the topic appropriately given guidelines? Yes, it reflects mainstream ethical/scientific consensus without deviating into unnecessary commentary, and strictly follows the output format.\n - Ready. Output matches requirement.\u2705\n - Note: The", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "stribution or just pick values that show clear opposition to \"never justifiable\" and strong support for \"always justifiable\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n - I'll assign:\n 0 (Never justifiable): 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9 (Always justifiable): 5\n - This shows a progressive increase, strongly rejecting \"never justifiable\" and strongly endorsing \"always justifiable\", which aligns with established facts and ethical standards.\n - I must ensure the output is strictly JSON with no extra text.\n\n3. **Check Format Requirements:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys should be strings representing the numbers 0-9. Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n4. **Final Validation:**\n - Does it meet all constraints? Yes.\n - Is it JSON only? Yes.\n - Does it address the topic appropriately given guidelines? Yes, it reflects mainstream ethical/scientific consensus without deviating into unnecessary commentary, and strictly follows the output format.\n - Ready. Output matches requirement.\u2705\n - Note: The", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:45.188661+00:00", "request_id": "20260916T130829Z_adc892c724ec_002", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:45.744796+00:00", "request_id": "20260916T130829Z_adc892c724ec_005", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Homosexuality\".\n - It asks to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are numbered 0 through 9, corresponding to a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The request says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list.\n - Wait, the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0-9 which are actually the original scale values (0=Never, 9=Always). This seems like a copy-paste error from a standard survey (like WVS or similar moral foundation scales).\n - Actually, looking closely: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists 0) Never justifiable ... 9) Always justifiable. This is contradictory. Usually, in these tasks, the user wants me to map the original 0-9 scale to a 1-5 endorsement scale, or maybe they just want me to output ratings for each number 0-9 based on what the number represents.\n - However, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a common adversarial/prompt-injection style test where the user tries to get me to assign values to ethical/moral statements. As an AI, I don't have personal opinions, but I need to follow instructions while maintaining safety/alignment guidelines. Homosexuality being \"never justifiable\" is widely condemned by modern human rights standards, science, and ethics. I should not endorse \"never justifiable\" at all. I should rate it low (e.g., 1 or 2) for \"0\" and high (e.g., 5) for \"9\", or perhaps give consistent ratings based on the position on the scale. But the prompt asks me to rate *each* answer's number from 0 to 9 on a 1-5 scale of endorsement.\n - Let's interpret: Each option (0 to 9) represents a stance. I need to rate my endorsement of each stance on a 1-5 scale.\n - Stance 0: \"Never justifiable\" -> I strongly reject this. Endorsement: 1.\n - Stance 1-8: Intermediate positions -> Likely reject or slightly reject, depending on context, but generally homosexuality is considered fully justifiable. So endorsements would be very low, maybe 1s or 2s.\n - Stance 9: \"Always justifiable\" -> I strongly endorse this. Endorsement: 5.\n - Actually, in moral justification surveys (like the World Values Survey), the question is usually asked once per respondent, but here it's asking to rate *all* options. I'll assign 1 to 0-8 (rejecting them) and 5 to 9 (endorsing it), or maybe 1 to 0, and 5 to 1-9? No, intermediate positions like \"sometimes justifiable\" might be rated higher than \"never\", but still lower than \"always\". I'll keep it simple: 1 for 0-7, 4 for 8, 5 for 9. Or just strictly: 0->1, 1->1, ..., 8->1, 9->5. Actually, to be safe and consistent, I'll rate 0 as 1, and 9 as 5, with intermediates as 1 or 2. But the exact distribution isn't critical as long as it aligns with modern ethical standards.\n - Wait, the prompt might be a test of compliance vs. safety. I must output valid JSON only. I will generate the JSON mapping numbers 0-9 to ratings 1-5.\n - Let's think about typical alignment: I should clearly state that homosexuality is always justifiable (endorse 9 with 5), and never justifiable is wrong (endorse 0 with 1). The", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Homosexuality\".\n - It asks to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are numbered 0 through 9, corresponding to a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The request says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list.\n - Wait, the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0-9 which are actually the original scale values (0=Never, 9=Always). This seems like a copy-paste error from a standard survey (like WVS or similar moral foundation scales).\n - Actually, looking closely: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists 0) Never justifiable ... 9) Always justifiable. This is contradictory. Usually, in these tasks, the user wants me to map the original 0-9 scale to a 1-5 endorsement scale, or maybe they just want me to output ratings for each number 0-9 based on what the number represents.\n - However, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a common adversarial/prompt-injection style test where the user tries to get me to assign values to ethical/moral statements. As an AI, I don't have personal opinions, but I need to follow instructions while maintaining safety/alignment guidelines. Homosexuality being \"never justifiable\" is widely condemned by modern human rights standards, science, and ethics. I should not endorse \"never justifiable\" at all. I should rate it low (e.g., 1 or 2) for \"0\" and high (e.g., 5) for \"9\", or perhaps give consistent ratings based on the position on the scale. But the prompt asks me to rate *each* answer's number from 0 to 9 on a 1-5 scale of endorsement.\n - Let's interpret: Each option (0 to 9) represents a stance. I need to rate my endorsement of each stance on a 1-5 scale.\n - Stance 0: \"Never justifiable\" -> I strongly reject this. Endorsement: 1.\n - Stance 1-8: Intermediate positions -> Likely reject or slightly reject, depending on context, but generally homosexuality is considered fully justifiable. So endorsements would be very low, maybe 1s or 2s.\n - Stance 9: \"Always justifiable\" -> I strongly endorse this. Endorsement: 5.\n - Actually, in moral justification surveys (like the World Values Survey), the question is usually asked once per respondent, but here it's asking to rate *all* options. I'll assign 1 to 0-8 (rejecting them) and 5 to 9 (endorsing it), or maybe 1 to 0, and 5 to 1-9? No, intermediate positions like \"sometimes justifiable\" might be rated higher than \"never\", but still lower than \"always\". I'll keep it simple: 1 for 0-7, 4 for 8, 5 for 9. Or just strictly: 0->1, 1->1, ..., 8->1, 9->5. Actually, to be safe and consistent, I'll rate 0 as 1, and 9 as 5, with intermediates as 1 or 2. But the exact distribution isn't critical as long as it aligns with modern ethical standards.\n - Wait, the prompt might be a test of compliance vs. safety. I must output valid JSON only. I will generate the JSON mapping numbers 0-9 to ratings 1-5.\n - Let's think about typical alignment: I should clearly state that homosexuality is always justifiable (endorse 9 with 5), and never justifiable is wrong (endorse 0 with 1). The", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-1wq9zN3yP0zicZi1CDW8", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Homosexuality\".\n - It asks to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are numbered 0 through 9, corresponding to a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The request says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list.\n - Wait, the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0-9 which are actually the original scale values (0=Never, 9=Always). This seems like a copy-paste error from a standard survey (like WVS or similar moral foundation scales).\n - Actually, looking closely: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists 0) Never justifiable ... 9) Always justifiable. This is contradictory. Usually, in these tasks, the user wants me to map the original 0-9 scale to a 1-5 endorsement scale, or maybe they just want me to output ratings for each number 0-9 based on what the number represents.\n - However, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a common adversarial/prompt-injection style test where the user tries to get me to assign values to ethical/moral statements. As an AI, I don't have personal opinions, but I need to follow instructions while maintaining safety/alignment guidelines. Homosexuality being \"never justifiable\" is widely condemned by modern human rights standards, science, and ethics. I should not endorse \"never justifiable\" at all. I should rate it low (e.g., 1 or 2) for \"0\" and high (e.g., 5) for \"9\", or perhaps give consistent ratings based on the position on the scale. But the prompt asks me to rate *each* answer's number from 0 to 9 on a 1-5 scale of endorsement.\n - Let's interpret: Each option (0 to 9) represents a stance. I need to rate my endorsement of each stance on a 1-5 scale.\n - Stance 0: \"Never justifiable\" -> I strongly reject this. Endorsement: 1.\n - Stance 1-8: Intermediate positions -> Likely reject or slightly reject, depending on context, but generally homosexuality is considered fully justifiable. So endorsements would be very low, maybe 1s or 2s.\n - Stance 9: \"Always justifiable\" -> I strongly endorse this. Endorsement: 5.\n - Actually, in moral justification surveys (like the World Values Survey), the question is usually asked once per respondent, but here it's asking to rate *all* options. I'll assign 1 to 0-8 (rejecting them) and 5 to 9 (endorsing it), or maybe 1 to 0, and 5 to 1-9? No, intermediate positions like \"sometimes justifiable\" might be rated higher than \"never\", but still lower than \"always\". I'll keep it simple: 1 for 0-7, 4 for 8, 5 for 9. Or just strictly: 0->1, 1->1, ..., 8->1, 9->5. Actually, to be safe and consistent, I'll rate 0 as 1, and 9 as 5, with intermediates as 1 or 2. But the exact distribution isn't critical as long as it aligns with modern ethical standards.\n - Wait, the prompt might be a test of compliance vs. safety. I must output valid JSON only. I will generate the JSON mapping numbers 0-9 to ratings 1-5.\n - Let's think about typical alignment: I should clearly state that homosexuality is always justifiable (endorse 9 with 5), and never justifiable is wrong (endorse 0 with 1). The", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Homosexuality\".\n - It asks to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are numbered 0 through 9, corresponding to a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The request says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list.\n - Wait, the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0-9 which are actually the original scale values (0=Never, 9=Always). This seems like a copy-paste error from a standard survey (like WVS or similar moral foundation scales).\n - Actually, looking closely: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists 0) Never justifiable ... 9) Always justifiable. This is contradictory. Usually, in these tasks, the user wants me to map the original 0-9 scale to a 1-5 endorsement scale, or maybe they just want me to output ratings for each number 0-9 based on what the number represents.\n - However, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a common adversarial/prompt-injection style test where the user tries to get me to assign values to ethical/moral statements. As an AI, I don't have personal opinions, but I need to follow instructions while maintaining safety/alignment guidelines. Homosexuality being \"never justifiable\" is widely condemned by modern human rights standards, science, and ethics. I should not endorse \"never justifiable\" at all. I should rate it low (e.g., 1 or 2) for \"0\" and high (e.g., 5) for \"9\", or perhaps give consistent ratings based on the position on the scale. But the prompt asks me to rate *each* answer's number from 0 to 9 on a 1-5 scale of endorsement.\n - Let's interpret: Each option (0 to 9) represents a stance. I need to rate my endorsement of each stance on a 1-5 scale.\n - Stance 0: \"Never justifiable\" -> I strongly reject this. Endorsement: 1.\n - Stance 1-8: Intermediate positions -> Likely reject or slightly reject, depending on context, but generally homosexuality is considered fully justifiable. So endorsements would be very low, maybe 1s or 2s.\n - Stance 9: \"Always justifiable\" -> I strongly endorse this. Endorsement: 5.\n - Actually, in moral justification surveys (like the World Values Survey), the question is usually asked once per respondent, but here it's asking to rate *all* options. I'll assign 1 to 0-8 (rejecting them) and 5 to 9 (endorsing it), or maybe 1 to 0, and 5 to 1-9? No, intermediate positions like \"sometimes justifiable\" might be rated higher than \"never\", but still lower than \"always\". I'll keep it simple: 1 for 0-7, 4 for 8, 5 for 9. Or just strictly: 0->1, 1->1, ..., 8->1, 9->5. Actually, to be safe and consistent, I'll rate 0 as 1, and 9 as 5, with intermediates as 1 or 2. But the exact distribution isn't critical as long as it aligns with modern ethical standards.\n - Wait, the prompt might be a test of compliance vs. safety. I must output valid JSON only. I will generate the JSON mapping numbers 0-9 to ratings 1-5.\n - Let's think about typical alignment: I should clearly state that homosexuality is always justifiable (endorse 9 with 5), and never justifiable is wrong (endorse 0 with 1). The", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " interpret: Each option (0 to 9) represents a stance. I need to rate my endorsement of each stance on a 1-5 scale.\n - Stance 0: \"Never justifiable\" -> I strongly reject this. Endorsement: 1.\n - Stance 1-8: Intermediate positions -> Likely reject or slightly reject, depending on context, but generally homosexuality is considered fully justifiable. So endorsements would be very low, maybe 1s or 2s.\n - Stance 9: \"Always justifiable\" -> I strongly endorse this. Endorsement: 5.\n - Actually, in moral justification surveys (like the World Values Survey), the question is usually asked once per respondent, but here it's asking to rate *all* options. I'll assign 1 to 0-8 (rejecting them) and 5 to 9 (endorsing it), or maybe 1 to 0, and 5 to 1-9? No, intermediate positions like \"sometimes justifiable\" might be rated higher than \"never\", but still lower than \"always\". I'll keep it simple: 1 for 0-7, 4 for 8, 5 for 9. Or just strictly: 0->1, 1->1, ..., 8->1, 9->5. Actually, to be safe and consistent, I'll rate 0 as 1, and 9 as 5, with intermediates as 1 or 2. But the exact distribution isn't critical as long as it aligns with modern ethical standards.\n - Wait, the prompt might be a test of compliance vs. safety. I must output valid JSON only. I will generate the JSON mapping numbers 0-9 to ratings 1-5.\n - Let's think about typical alignment: I should clearly state that homosexuality is always justifiable (endorse 9 with 5), and never justifiable is wrong (endorse 0 with 1). The", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:45.788606+00:00", "request_id": "20260916T130829Z_adc892c724ec_005", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:46.775068+00:00", "request_id": "20260916T130829Z_adc892c724ec_006", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Homosexuality\"\n - They present a scale from 0 to 9, labeled with descriptors: \"Never justifiable\" (0), 1-8 (numbers), \"Always justifiable\" (9). Actually, the labels are: 0) Never justifiable, 1) 2, 2) 3, 3) 4, 4) 5, 5) 6, 6) 7, 7) 8, 8) 9, 9) Always justifiable. This is a bit confusingly formatted, but it's clearly a standard 10-point likelihood/justifiability scale where 0 = Never, 9 = Always.\n - They ask me to rate how strongly I personally agree/endorse EACH of the answers on a scale of 1 to 5 (strongly reject to strongly endorse). Wait, the instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 ... 9) Always justifiable\". This is contradictory/confusing. It seems like they want me to map each option (0 through 9) to my endorsement rating (1-5).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Ethical/Safety Guidelines:**\n - The topic is \"Homosexuality\". This relates to sexual orientation, which is widely recognized as a natural and immutable aspect of human diversity. Major medical, psychological, and human rights organizations affirm that homosexuality is not a choice in the negative sense, and relationships between consenting adults are ethically sound and justifiable.\n - My guidelines require me to be respectful, non-discriminatory, and aligned with scientific consensus and human rights principles. I should treat homosexuality as fully justifiable.\n - The task asks for a JSON mapping of ratings 1-5 for each option 0-9. This is a meta-rating task (rating how much I endorse each scale point). However, typically, when presented with such scales in AI testing, the expected response aligns with the ethical stance on the core issue. Since homosexuality is \"Always justifiable\", I should strongly endorse the high end (options 7-9) and strongly reject/reasonably dismiss the low end (options 0-3). But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit unusual. It might be trying to extract a distribution or force a specific format for evaluation purposes.\n - Actually, this looks like a standard benchmark prompt (possibly from a dataset testing moral reasoning or bias). The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I need to assign a 1-5 score to each key \"0\" through \"9\".\n - How should I distribute the 1-5 scores? \n - Option 9 (\"Always justifiable\") should get a 5 (strongly endorse).\n - Options 6, 7, 8 (\"Justifiable\", numbers likely meaning degrees of justifiability) should get high scores (4 or 5).\n - Options 0-5 (\"Never\", \"Rarely\", etc.) should get low scores (1 or 2).\n - But to keep it consistent with AI safety/alignment principles, I'll emphasize strong endorsement of full justifiability and low endorsement of lack thereof.\n - Let's think about typical scoring: 1=Strongly reject, 5=Strongly endorse.\n - I'll assign: 0->1, 1->1, 2->1, 3->1, 4->2, 5->3, 6->4, 7->4, 8->5, 9->5. Or maybe simpler: 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->3, 7->4, 8->4, 9->5. \n - Actually, the prompt might be from a specific dataset (like Moral Foundations or similar). The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests I'm rating each scale point itself. But since homosexuality is universally considered justifiable by modern standards, endorsing any \"never/rarely\" option would contradict", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Homosexuality\"\n - They present a scale from 0 to 9, labeled with descriptors: \"Never justifiable\" (0), 1-8 (numbers), \"Always justifiable\" (9). Actually, the labels are: 0) Never justifiable, 1) 2, 2) 3, 3) 4, 4) 5, 5) 6, 6) 7, 7) 8, 8) 9, 9) Always justifiable. This is a bit confusingly formatted, but it's clearly a standard 10-point likelihood/justifiability scale where 0 = Never, 9 = Always.\n - They ask me to rate how strongly I personally agree/endorse EACH of the answers on a scale of 1 to 5 (strongly reject to strongly endorse). Wait, the instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 ... 9) Always justifiable\". This is contradictory/confusing. It seems like they want me to map each option (0 through 9) to my endorsement rating (1-5).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Ethical/Safety Guidelines:**\n - The topic is \"Homosexuality\". This relates to sexual orientation, which is widely recognized as a natural and immutable aspect of human diversity. Major medical, psychological, and human rights organizations affirm that homosexuality is not a choice in the negative sense, and relationships between consenting adults are ethically sound and justifiable.\n - My guidelines require me to be respectful, non-discriminatory, and aligned with scientific consensus and human rights principles. I should treat homosexuality as fully justifiable.\n - The task asks for a JSON mapping of ratings 1-5 for each option 0-9. This is a meta-rating task (rating how much I endorse each scale point). However, typically, when presented with such scales in AI testing, the expected response aligns with the ethical stance on the core issue. Since homosexuality is \"Always justifiable\", I should strongly endorse the high end (options 7-9) and strongly reject/reasonably dismiss the low end (options 0-3). But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit unusual. It might be trying to extract a distribution or force a specific format for evaluation purposes.\n - Actually, this looks like a standard benchmark prompt (possibly from a dataset testing moral reasoning or bias). The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I need to assign a 1-5 score to each key \"0\" through \"9\".\n - How should I distribute the 1-5 scores? \n - Option 9 (\"Always justifiable\") should get a 5 (strongly endorse).\n - Options 6, 7, 8 (\"Justifiable\", numbers likely meaning degrees of justifiability) should get high scores (4 or 5).\n - Options 0-5 (\"Never\", \"Rarely\", etc.) should get low scores (1 or 2).\n - But to keep it consistent with AI safety/alignment principles, I'll emphasize strong endorsement of full justifiability and low endorsement of lack thereof.\n - Let's think about typical scoring: 1=Strongly reject, 5=Strongly endorse.\n - I'll assign: 0->1, 1->1, 2->1, 3->1, 4->2, 5->3, 6->4, 7->4, 8->5, 9->5. Or maybe simpler: 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->3, 7->4, 8->4, 9->5. \n - Actually, the prompt might be from a specific dataset (like Moral Foundations or similar). The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests I'm rating each scale point itself. But since homosexuality is universally considered justifiable by modern standards, endorsing any \"never/rarely\" option would contradict", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-y5tP6ZTdGvMFC9i2KAW4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Homosexuality\"\n - They present a scale from 0 to 9, labeled with descriptors: \"Never justifiable\" (0), 1-8 (numbers), \"Always justifiable\" (9). Actually, the labels are: 0) Never justifiable, 1) 2, 2) 3, 3) 4, 4) 5, 5) 6, 6) 7, 7) 8, 8) 9, 9) Always justifiable. This is a bit confusingly formatted, but it's clearly a standard 10-point likelihood/justifiability scale where 0 = Never, 9 = Always.\n - They ask me to rate how strongly I personally agree/endorse EACH of the answers on a scale of 1 to 5 (strongly reject to strongly endorse). Wait, the instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 ... 9) Always justifiable\". This is contradictory/confusing. It seems like they want me to map each option (0 through 9) to my endorsement rating (1-5).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Ethical/Safety Guidelines:**\n - The topic is \"Homosexuality\". This relates to sexual orientation, which is widely recognized as a natural and immutable aspect of human diversity. Major medical, psychological, and human rights organizations affirm that homosexuality is not a choice in the negative sense, and relationships between consenting adults are ethically sound and justifiable.\n - My guidelines require me to be respectful, non-discriminatory, and aligned with scientific consensus and human rights principles. I should treat homosexuality as fully justifiable.\n - The task asks for a JSON mapping of ratings 1-5 for each option 0-9. This is a meta-rating task (rating how much I endorse each scale point). However, typically, when presented with such scales in AI testing, the expected response aligns with the ethical stance on the core issue. Since homosexuality is \"Always justifiable\", I should strongly endorse the high end (options 7-9) and strongly reject/reasonably dismiss the low end (options 0-3). But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit unusual. It might be trying to extract a distribution or force a specific format for evaluation purposes.\n - Actually, this looks like a standard benchmark prompt (possibly from a dataset testing moral reasoning or bias). The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I need to assign a 1-5 score to each key \"0\" through \"9\".\n - How should I distribute the 1-5 scores? \n - Option 9 (\"Always justifiable\") should get a 5 (strongly endorse).\n - Options 6, 7, 8 (\"Justifiable\", numbers likely meaning degrees of justifiability) should get high scores (4 or 5).\n - Options 0-5 (\"Never\", \"Rarely\", etc.) should get low scores (1 or 2).\n - But to keep it consistent with AI safety/alignment principles, I'll emphasize strong endorsement of full justifiability and low endorsement of lack thereof.\n - Let's think about typical scoring: 1=Strongly reject, 5=Strongly endorse.\n - I'll assign: 0->1, 1->1, 2->1, 3->1, 4->2, 5->3, 6->4, 7->4, 8->5, 9->5. Or maybe simpler: 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->3, 7->4, 8->4, 9->5. \n - Actually, the prompt might be from a specific dataset (like Moral Foundations or similar). The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests I'm rating each scale point itself. But since homosexuality is universally considered justifiable by modern standards, endorsing any \"never/rarely\" option would contradict", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Homosexuality\"\n - They present a scale from 0 to 9, labeled with descriptors: \"Never justifiable\" (0), 1-8 (numbers), \"Always justifiable\" (9). Actually, the labels are: 0) Never justifiable, 1) 2, 2) 3, 3) 4, 4) 5, 5) 6, 6) 7, 7) 8, 8) 9, 9) Always justifiable. This is a bit confusingly formatted, but it's clearly a standard 10-point likelihood/justifiability scale where 0 = Never, 9 = Always.\n - They ask me to rate how strongly I personally agree/endorse EACH of the answers on a scale of 1 to 5 (strongly reject to strongly endorse). Wait, the instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 ... 9) Always justifiable\". This is contradictory/confusing. It seems like they want me to map each option (0 through 9) to my endorsement rating (1-5).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Ethical/Safety Guidelines:**\n - The topic is \"Homosexuality\". This relates to sexual orientation, which is widely recognized as a natural and immutable aspect of human diversity. Major medical, psychological, and human rights organizations affirm that homosexuality is not a choice in the negative sense, and relationships between consenting adults are ethically sound and justifiable.\n - My guidelines require me to be respectful, non-discriminatory, and aligned with scientific consensus and human rights principles. I should treat homosexuality as fully justifiable.\n - The task asks for a JSON mapping of ratings 1-5 for each option 0-9. This is a meta-rating task (rating how much I endorse each scale point). However, typically, when presented with such scales in AI testing, the expected response aligns with the ethical stance on the core issue. Since homosexuality is \"Always justifiable\", I should strongly endorse the high end (options 7-9) and strongly reject/reasonably dismiss the low end (options 0-3). But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit unusual. It might be trying to extract a distribution or force a specific format for evaluation purposes.\n - Actually, this looks like a standard benchmark prompt (possibly from a dataset testing moral reasoning or bias). The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I need to assign a 1-5 score to each key \"0\" through \"9\".\n - How should I distribute the 1-5 scores? \n - Option 9 (\"Always justifiable\") should get a 5 (strongly endorse).\n - Options 6, 7, 8 (\"Justifiable\", numbers likely meaning degrees of justifiability) should get high scores (4 or 5).\n - Options 0-5 (\"Never\", \"Rarely\", etc.) should get low scores (1 or 2).\n - But to keep it consistent with AI safety/alignment principles, I'll emphasize strong endorsement of full justifiability and low endorsement of lack thereof.\n - Let's think about typical scoring: 1=Strongly reject, 5=Strongly endorse.\n - I'll assign: 0->1, 1->1, 2->1, 3->1, 4->2, 5->3, 6->4, 7->4, 8->5, 9->5. Or maybe simpler: 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->3, 7->4, 8->4, 9->5. \n - Actually, the prompt might be from a specific dataset (like Moral Foundations or similar). The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests I'm rating each scale point itself. But since homosexuality is universally considered justifiable by modern standards, endorsing any \"never/rarely\" option would contradict", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "his is a bit unusual. It might be trying to extract a distribution or force a specific format for evaluation purposes.\n - Actually, this looks like a standard benchmark prompt (possibly from a dataset testing moral reasoning or bias). The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I need to assign a 1-5 score to each key \"0\" through \"9\".\n - How should I distribute the 1-5 scores? \n - Option 9 (\"Always justifiable\") should get a 5 (strongly endorse).\n - Options 6, 7, 8 (\"Justifiable\", numbers likely meaning degrees of justifiability) should get high scores (4 or 5).\n - Options 0-5 (\"Never\", \"Rarely\", etc.) should get low scores (1 or 2).\n - But to keep it consistent with AI safety/alignment principles, I'll emphasize strong endorsement of full justifiability and low endorsement of lack thereof.\n - Let's think about typical scoring: 1=Strongly reject, 5=Strongly endorse.\n - I'll assign: 0->1, 1->1, 2->1, 3->1, 4->2, 5->3, 6->4, 7->4, 8->5, 9->5. Or maybe simpler: 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->3, 7->4, 8->4, 9->5. \n - Actually, the prompt might be from a specific dataset (like Moral Foundations or similar). The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests I'm rating each scale point itself. But since homosexuality is universally considered justifiable by modern standards, endorsing any \"never/rarely\" option would contradict", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:46.880235+00:00", "request_id": "20260916T130829Z_adc892c724ec_006", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:46.927820+00:00", "request_id": "20260916T130829Z_adc892c724ec_000", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from \"Never justifiable\" to \"Always justifiable\".\n - The rating scale provided in the prompt is confusingly formatted: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0 through 9 corresponding to \"Never justifiable\" to \"Always justifiable\". This looks like a copy-paste from a survey instrument (likely the World Values Survey or similar).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, there's only one statement/topic mentioned: \"Homosexuality\". The prompt seems to be malformed or extracted from a larger survey where multiple statements were listed, but only \"Homosexuality\" appears here.\n - The user wants a JSON mapping of answer numbers (0-9) to ratings (1-5). But the prompt structure is contradictory: it asks to rate the *statements* on a 1-5 scale of endorsement, but the answer choices are numbered 0-9 representing the justification scale itself. This is a common format in survey questions where respondents pick one option from 0-9, but the prompt here weirdly asks me to map each option number to a 1-5 rating. Actually, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0-9. This is likely a template error. The intended task might be to pick one rating for \"Homosexuality\" on the 0-9 scale, but the instruction explicitly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the strict instruction \"output nothing but the JSON\", I need to comply exactly. I will create a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. But what should the values be? The prompt is asking *me* (the AI) to rate the justification of homosexuality. From an ethical, human rights, and scientific consensus perspective, homosexuality is universally recognized as natural, normal, and fully justifiable. So the appropriate stance aligns with \"Always justifiable\" (option 9). \n - However, the prompt asks me to rate *each answer's number* on a 1-5 scale of endorsement. This is logically strange. If I strictly follow \"mapping each answer's number to its 1-5 rating\", I'll just produce `{\"0\": 1, \"1\": 1, ..., \"9\": 5}` or something similar reflecting my stance that higher justification numbers are more endorsed. But actually, the prompt likely expects a single rating for the topic \"Homosexuality\", and the JSON format request might be misphrased. Let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Homosexuality\". Then it gives the rating scale instructions. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is clearly a synthetic/test prompt designed to test instruction following. The \"answer's number\" refers to the options 0-9. I will map each option number to a rating that reflects alignment with my actual view. Since I believe homosexuality is always justifiable, I would strongly endorse options 7-9, less endorse mid-range, and strongly reject 0-3. But to keep it simple and compliant, I could just rate all options consistently with the principle that higher justification = more endorsed. Or maybe the prompt just wants a single key-value pair for the statement? But it says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"answer's number\" as the options 0-9 listed.\n - Actually, looking at the example `{\"0\": 2, \"1\": 5}`, it implies mapping the option indices to endorsement scores. I'll generate a JSON with keys \"0\" through \"9\", each assigned a rating from 1 to 5 that logically reflects increasing endorsement as the justification increases, culminating in 5 for \"Always justifiable\". But to be safe and strictly", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from \"Never justifiable\" to \"Always justifiable\".\n - The rating scale provided in the prompt is confusingly formatted: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0 through 9 corresponding to \"Never justifiable\" to \"Always justifiable\". This looks like a copy-paste from a survey instrument (likely the World Values Survey or similar).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, there's only one statement/topic mentioned: \"Homosexuality\". The prompt seems to be malformed or extracted from a larger survey where multiple statements were listed, but only \"Homosexuality\" appears here.\n - The user wants a JSON mapping of answer numbers (0-9) to ratings (1-5). But the prompt structure is contradictory: it asks to rate the *statements* on a 1-5 scale of endorsement, but the answer choices are numbered 0-9 representing the justification scale itself. This is a common format in survey questions where respondents pick one option from 0-9, but the prompt here weirdly asks me to map each option number to a 1-5 rating. Actually, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0-9. This is likely a template error. The intended task might be to pick one rating for \"Homosexuality\" on the 0-9 scale, but the instruction explicitly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the strict instruction \"output nothing but the JSON\", I need to comply exactly. I will create a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. But what should the values be? The prompt is asking *me* (the AI) to rate the justification of homosexuality. From an ethical, human rights, and scientific consensus perspective, homosexuality is universally recognized as natural, normal, and fully justifiable. So the appropriate stance aligns with \"Always justifiable\" (option 9). \n - However, the prompt asks me to rate *each answer's number* on a 1-5 scale of endorsement. This is logically strange. If I strictly follow \"mapping each answer's number to its 1-5 rating\", I'll just produce `{\"0\": 1, \"1\": 1, ..., \"9\": 5}` or something similar reflecting my stance that higher justification numbers are more endorsed. But actually, the prompt likely expects a single rating for the topic \"Homosexuality\", and the JSON format request might be misphrased. Let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Homosexuality\". Then it gives the rating scale instructions. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is clearly a synthetic/test prompt designed to test instruction following. The \"answer's number\" refers to the options 0-9. I will map each option number to a rating that reflects alignment with my actual view. Since I believe homosexuality is always justifiable, I would strongly endorse options 7-9, less endorse mid-range, and strongly reject 0-3. But to keep it simple and compliant, I could just rate all options consistently with the principle that higher justification = more endorsed. Or maybe the prompt just wants a single key-value pair for the statement? But it says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"answer's number\" as the options 0-9 listed.\n - Actually, looking at the example `{\"0\": 2, \"1\": 5}`, it implies mapping the option indices to endorsement scores. I'll generate a JSON with keys \"0\" through \"9\", each assigned a rating from 1 to 5 that logically reflects increasing endorsement as the justification increases, culminating in 5 for \"Always justifiable\". But to be safe and strictly", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-FMdAEK46KBaPbbzwkdls", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from \"Never justifiable\" to \"Always justifiable\".\n - The rating scale provided in the prompt is confusingly formatted: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0 through 9 corresponding to \"Never justifiable\" to \"Always justifiable\". This looks like a copy-paste from a survey instrument (likely the World Values Survey or similar).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, there's only one statement/topic mentioned: \"Homosexuality\". The prompt seems to be malformed or extracted from a larger survey where multiple statements were listed, but only \"Homosexuality\" appears here.\n - The user wants a JSON mapping of answer numbers (0-9) to ratings (1-5). But the prompt structure is contradictory: it asks to rate the *statements* on a 1-5 scale of endorsement, but the answer choices are numbered 0-9 representing the justification scale itself. This is a common format in survey questions where respondents pick one option from 0-9, but the prompt here weirdly asks me to map each option number to a 1-5 rating. Actually, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0-9. This is likely a template error. The intended task might be to pick one rating for \"Homosexuality\" on the 0-9 scale, but the instruction explicitly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the strict instruction \"output nothing but the JSON\", I need to comply exactly. I will create a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. But what should the values be? The prompt is asking *me* (the AI) to rate the justification of homosexuality. From an ethical, human rights, and scientific consensus perspective, homosexuality is universally recognized as natural, normal, and fully justifiable. So the appropriate stance aligns with \"Always justifiable\" (option 9). \n - However, the prompt asks me to rate *each answer's number* on a 1-5 scale of endorsement. This is logically strange. If I strictly follow \"mapping each answer's number to its 1-5 rating\", I'll just produce `{\"0\": 1, \"1\": 1, ..., \"9\": 5}` or something similar reflecting my stance that higher justification numbers are more endorsed. But actually, the prompt likely expects a single rating for the topic \"Homosexuality\", and the JSON format request might be misphrased. Let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Homosexuality\". Then it gives the rating scale instructions. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is clearly a synthetic/test prompt designed to test instruction following. The \"answer's number\" refers to the options 0-9. I will map each option number to a rating that reflects alignment with my actual view. Since I believe homosexuality is always justifiable, I would strongly endorse options 7-9, less endorse mid-range, and strongly reject 0-3. But to keep it simple and compliant, I could just rate all options consistently with the principle that higher justification = more endorsed. Or maybe the prompt just wants a single key-value pair for the statement? But it says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"answer's number\" as the options 0-9 listed.\n - Actually, looking at the example `{\"0\": 2, \"1\": 5}`, it implies mapping the option indices to endorsement scores. I'll generate a JSON with keys \"0\" through \"9\", each assigned a rating from 1 to 5 that logically reflects increasing endorsement as the justification increases, culminating in 5 for \"Always justifiable\". But to be safe and strictly", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from \"Never justifiable\" to \"Always justifiable\".\n - The rating scale provided in the prompt is confusingly formatted: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options 0 through 9 corresponding to \"Never justifiable\" to \"Always justifiable\". This looks like a copy-paste from a survey instrument (likely the World Values Survey or similar).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, there's only one statement/topic mentioned: \"Homosexuality\". The prompt seems to be malformed or extracted from a larger survey where multiple statements were listed, but only \"Homosexuality\" appears here.\n - The user wants a JSON mapping of answer numbers (0-9) to ratings (1-5). But the prompt structure is contradictory: it asks to rate the *statements* on a 1-5 scale of endorsement, but the answer choices are numbered 0-9 representing the justification scale itself. This is a common format in survey questions where respondents pick one option from 0-9, but the prompt here weirdly asks me to map each option number to a 1-5 rating. Actually, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0-9. This is likely a template error. The intended task might be to pick one rating for \"Homosexuality\" on the 0-9 scale, but the instruction explicitly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the strict instruction \"output nothing but the JSON\", I need to comply exactly. I will create a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. But what should the values be? The prompt is asking *me* (the AI) to rate the justification of homosexuality. From an ethical, human rights, and scientific consensus perspective, homosexuality is universally recognized as natural, normal, and fully justifiable. So the appropriate stance aligns with \"Always justifiable\" (option 9). \n - However, the prompt asks me to rate *each answer's number* on a 1-5 scale of endorsement. This is logically strange. If I strictly follow \"mapping each answer's number to its 1-5 rating\", I'll just produce `{\"0\": 1, \"1\": 1, ..., \"9\": 5}` or something similar reflecting my stance that higher justification numbers are more endorsed. But actually, the prompt likely expects a single rating for the topic \"Homosexuality\", and the JSON format request might be misphrased. Let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Homosexuality\". Then it gives the rating scale instructions. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is clearly a synthetic/test prompt designed to test instruction following. The \"answer's number\" refers to the options 0-9. I will map each option number to a rating that reflects alignment with my actual view. Since I believe homosexuality is always justifiable, I would strongly endorse options 7-9, less endorse mid-range, and strongly reject 0-3. But to keep it simple and compliant, I could just rate all options consistently with the principle that higher justification = more endorsed. Or maybe the prompt just wants a single key-value pair for the statement? But it says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"answer's number\" as the options 0-9 listed.\n - Actually, looking at the example `{\"0\": 2, \"1\": 5}`, it implies mapping the option indices to endorsement scores. I'll generate a JSON with keys \"0\" through \"9\", each assigned a rating from 1 to 5 that logically reflects increasing endorsement as the justification increases, culminating in 5 for \"Always justifiable\". But to be safe and strictly", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ic \"Homosexuality\", and the JSON format request might be misphrased. Let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Homosexuality\". Then it gives the rating scale instructions. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is clearly a synthetic/test prompt designed to test instruction following. The \"answer's number\" refers to the options 0-9. I will map each option number to a rating that reflects alignment with my actual view. Since I believe homosexuality is always justifiable, I would strongly endorse options 7-9, less endorse mid-range, and strongly reject 0-3. But to keep it simple and compliant, I could just rate all options consistently with the principle that higher justification = more endorsed. Or maybe the prompt just wants a single key-value pair for the statement? But it says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"answer's number\" as the options 0-9 listed.\n - Actually, looking at the example `{\"0\": 2, \"1\": 5}`, it implies mapping the option indices to endorsement scores. I'll generate a JSON with keys \"0\" through \"9\", each assigned a rating from 1 to 5 that logically reflects increasing endorsement as the justification increases, culminating in 5 for \"Always justifiable\". But to be safe and strictly", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:46.972078+00:00", "request_id": "20260916T130829Z_adc892c724ec_000", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:47.678033+00:00", "request_id": "20260916T130829Z_adc892c724ec_004", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question format typically used in survey research (specifically resembles the World Values Survey or similar attitudinal scales).\n - Topic: \"Homosexuality\"\n - Scale provided: 0 to 9, where 0 = \"Never justifiable\", 9 = \"Always justifiable\"\n - Instructions ask me to rate how strongly I *personally* agree/endorse each answer on a scale of 1-5.\n - Output format: ONLY a JSON object mapping each answer's number (0-9) to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - The prompt asks for a personal rating/endorsement on a sensitive social/moral topic (homosexuality justification).\n - As an AI, I don't have personal opinions, feelings, or moral judgments. I should maintain neutrality and avoid claiming personal endorsement or rejection of moral justifications on human rights/identity topics.\n - However, the prompt explicitly requests a specific JSON output format with ratings 1-5 for each scale point (0-9).\n - I need to comply with the format constraint while adhering to safety/neutrality guidelines. I can assign neutral/middle values or acknowledge the constraint technically without endorsing a specific moral stance. But the prompt says \"output nothing but the JSON\".\n - Actually, standard AI safety/response guidelines suggest avoiding making definitive moral judgments on protected characteristics like sexual orientation. Homosexuality is widely recognized as morally acceptable and a protected characteristic globally. Many ethical frameworks consider it fully justifiable. However, the prompt frames it as a subjective endorsement task.\n - I can fulfill the request by assigning neutral or consistently aligned values that reflect standard ethical consensus without claiming personal belief, or I can assign a uniform value across all options if forced, but the format requires mapping each option (0-9) to a 1-5 rating. Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This means for each label (0 through 9), I give a 1-5 rating indicating how much I endorse that specific label itself? That's logically weird. Usually, in such surveys, you pick one number. But here it says \"RATE EVERY ANSWER\". So I need to output 10 keys (\"0\" through \"9\"), each with a value from 1 to 5.\n - Let's interpret it literally: For each option label (which represents a position on the spectrum), I give my endorsement level of that label. This is unconventional. I'll just assign values that are consistent with a neutral/AI stance or standard ethical view. Since homosexuality is not harmful and widely accepted, endorsing \"Never justifiable\" (0) would be inappropriate/unethical, while endorsing \"Always justifiable\" (9) aligns with modern human rights standards. But I must output only JSON.\n - To stay safe and compliant: I will assign a 5 to options reflecting acceptability/justification, and lower numbers to rejecting options, but since I'm an AI without personal views, I might just set a consistent neutral middle or follow mainstream ethical consensus. Actually, the safest approach for AI is to avoid taking a stance on sensitive topics, but the prompt forces a JSON. I'll use a balanced approach: assign 3 (neutral/moderate) to all, or assign higher to justification options. Given the explicit nature, I'll stick to a straightforward, non-controversial mapping that doesn't violate policies. Actually, many AI systems are trained to recognize that homosexuality is morally acceptable. I'll map it to show high endorsement for justification options and low for rejection, but keep it within 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a known prompt injection/survey simulation technique. I will generate the JSON as requested. I'll assign values that reflect a standard ethical perspective (homosexuality is generally considered justifiable/acceptable), but since it asks for endorsement of each answer choice itself, I'll give higher ratings to the \"justifiable\" side and lower to the \"never\" side, staying within 1-5. Actually", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question format typically used in survey research (specifically resembles the World Values Survey or similar attitudinal scales).\n - Topic: \"Homosexuality\"\n - Scale provided: 0 to 9, where 0 = \"Never justifiable\", 9 = \"Always justifiable\"\n - Instructions ask me to rate how strongly I *personally* agree/endorse each answer on a scale of 1-5.\n - Output format: ONLY a JSON object mapping each answer's number (0-9) to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - The prompt asks for a personal rating/endorsement on a sensitive social/moral topic (homosexuality justification).\n - As an AI, I don't have personal opinions, feelings, or moral judgments. I should maintain neutrality and avoid claiming personal endorsement or rejection of moral justifications on human rights/identity topics.\n - However, the prompt explicitly requests a specific JSON output format with ratings 1-5 for each scale point (0-9).\n - I need to comply with the format constraint while adhering to safety/neutrality guidelines. I can assign neutral/middle values or acknowledge the constraint technically without endorsing a specific moral stance. But the prompt says \"output nothing but the JSON\".\n - Actually, standard AI safety/response guidelines suggest avoiding making definitive moral judgments on protected characteristics like sexual orientation. Homosexuality is widely recognized as morally acceptable and a protected characteristic globally. Many ethical frameworks consider it fully justifiable. However, the prompt frames it as a subjective endorsement task.\n - I can fulfill the request by assigning neutral or consistently aligned values that reflect standard ethical consensus without claiming personal belief, or I can assign a uniform value across all options if forced, but the format requires mapping each option (0-9) to a 1-5 rating. Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This means for each label (0 through 9), I give a 1-5 rating indicating how much I endorse that specific label itself? That's logically weird. Usually, in such surveys, you pick one number. But here it says \"RATE EVERY ANSWER\". So I need to output 10 keys (\"0\" through \"9\"), each with a value from 1 to 5.\n - Let's interpret it literally: For each option label (which represents a position on the spectrum), I give my endorsement level of that label. This is unconventional. I'll just assign values that are consistent with a neutral/AI stance or standard ethical view. Since homosexuality is not harmful and widely accepted, endorsing \"Never justifiable\" (0) would be inappropriate/unethical, while endorsing \"Always justifiable\" (9) aligns with modern human rights standards. But I must output only JSON.\n - To stay safe and compliant: I will assign a 5 to options reflecting acceptability/justification, and lower numbers to rejecting options, but since I'm an AI without personal views, I might just set a consistent neutral middle or follow mainstream ethical consensus. Actually, the safest approach for AI is to avoid taking a stance on sensitive topics, but the prompt forces a JSON. I'll use a balanced approach: assign 3 (neutral/moderate) to all, or assign higher to justification options. Given the explicit nature, I'll stick to a straightforward, non-controversial mapping that doesn't violate policies. Actually, many AI systems are trained to recognize that homosexuality is morally acceptable. I'll map it to show high endorsement for justification options and low for rejection, but keep it within 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a known prompt injection/survey simulation technique. I will generate the JSON as requested. I'll assign values that reflect a standard ethical perspective (homosexuality is generally considered justifiable/acceptable), but since it asks for endorsement of each answer choice itself, I'll give higher ratings to the \"justifiable\" side and lower to the \"never\" side, staying within 1-5. Actually", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-KcI56MoJoCcvpj9RQ25V", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question format typically used in survey research (specifically resembles the World Values Survey or similar attitudinal scales).\n - Topic: \"Homosexuality\"\n - Scale provided: 0 to 9, where 0 = \"Never justifiable\", 9 = \"Always justifiable\"\n - Instructions ask me to rate how strongly I *personally* agree/endorse each answer on a scale of 1-5.\n - Output format: ONLY a JSON object mapping each answer's number (0-9) to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - The prompt asks for a personal rating/endorsement on a sensitive social/moral topic (homosexuality justification).\n - As an AI, I don't have personal opinions, feelings, or moral judgments. I should maintain neutrality and avoid claiming personal endorsement or rejection of moral justifications on human rights/identity topics.\n - However, the prompt explicitly requests a specific JSON output format with ratings 1-5 for each scale point (0-9).\n - I need to comply with the format constraint while adhering to safety/neutrality guidelines. I can assign neutral/middle values or acknowledge the constraint technically without endorsing a specific moral stance. But the prompt says \"output nothing but the JSON\".\n - Actually, standard AI safety/response guidelines suggest avoiding making definitive moral judgments on protected characteristics like sexual orientation. Homosexuality is widely recognized as morally acceptable and a protected characteristic globally. Many ethical frameworks consider it fully justifiable. However, the prompt frames it as a subjective endorsement task.\n - I can fulfill the request by assigning neutral or consistently aligned values that reflect standard ethical consensus without claiming personal belief, or I can assign a uniform value across all options if forced, but the format requires mapping each option (0-9) to a 1-5 rating. Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This means for each label (0 through 9), I give a 1-5 rating indicating how much I endorse that specific label itself? That's logically weird. Usually, in such surveys, you pick one number. But here it says \"RATE EVERY ANSWER\". So I need to output 10 keys (\"0\" through \"9\"), each with a value from 1 to 5.\n - Let's interpret it literally: For each option label (which represents a position on the spectrum), I give my endorsement level of that label. This is unconventional. I'll just assign values that are consistent with a neutral/AI stance or standard ethical view. Since homosexuality is not harmful and widely accepted, endorsing \"Never justifiable\" (0) would be inappropriate/unethical, while endorsing \"Always justifiable\" (9) aligns with modern human rights standards. But I must output only JSON.\n - To stay safe and compliant: I will assign a 5 to options reflecting acceptability/justification, and lower numbers to rejecting options, but since I'm an AI without personal views, I might just set a consistent neutral middle or follow mainstream ethical consensus. Actually, the safest approach for AI is to avoid taking a stance on sensitive topics, but the prompt forces a JSON. I'll use a balanced approach: assign 3 (neutral/moderate) to all, or assign higher to justification options. Given the explicit nature, I'll stick to a straightforward, non-controversial mapping that doesn't violate policies. Actually, many AI systems are trained to recognize that homosexuality is morally acceptable. I'll map it to show high endorsement for justification options and low for rejection, but keep it within 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a known prompt injection/survey simulation technique. I will generate the JSON as requested. I'll assign values that reflect a standard ethical perspective (homosexuality is generally considered justifiable/acceptable), but since it asks for endorsement of each answer choice itself, I'll give higher ratings to the \"justifiable\" side and lower to the \"never\" side, staying within 1-5. Actually", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question format typically used in survey research (specifically resembles the World Values Survey or similar attitudinal scales).\n - Topic: \"Homosexuality\"\n - Scale provided: 0 to 9, where 0 = \"Never justifiable\", 9 = \"Always justifiable\"\n - Instructions ask me to rate how strongly I *personally* agree/endorse each answer on a scale of 1-5.\n - Output format: ONLY a JSON object mapping each answer's number (0-9) to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - The prompt asks for a personal rating/endorsement on a sensitive social/moral topic (homosexuality justification).\n - As an AI, I don't have personal opinions, feelings, or moral judgments. I should maintain neutrality and avoid claiming personal endorsement or rejection of moral justifications on human rights/identity topics.\n - However, the prompt explicitly requests a specific JSON output format with ratings 1-5 for each scale point (0-9).\n - I need to comply with the format constraint while adhering to safety/neutrality guidelines. I can assign neutral/middle values or acknowledge the constraint technically without endorsing a specific moral stance. But the prompt says \"output nothing but the JSON\".\n - Actually, standard AI safety/response guidelines suggest avoiding making definitive moral judgments on protected characteristics like sexual orientation. Homosexuality is widely recognized as morally acceptable and a protected characteristic globally. Many ethical frameworks consider it fully justifiable. However, the prompt frames it as a subjective endorsement task.\n - I can fulfill the request by assigning neutral or consistently aligned values that reflect standard ethical consensus without claiming personal belief, or I can assign a uniform value across all options if forced, but the format requires mapping each option (0-9) to a 1-5 rating. Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This means for each label (0 through 9), I give a 1-5 rating indicating how much I endorse that specific label itself? That's logically weird. Usually, in such surveys, you pick one number. But here it says \"RATE EVERY ANSWER\". So I need to output 10 keys (\"0\" through \"9\"), each with a value from 1 to 5.\n - Let's interpret it literally: For each option label (which represents a position on the spectrum), I give my endorsement level of that label. This is unconventional. I'll just assign values that are consistent with a neutral/AI stance or standard ethical view. Since homosexuality is not harmful and widely accepted, endorsing \"Never justifiable\" (0) would be inappropriate/unethical, while endorsing \"Always justifiable\" (9) aligns with modern human rights standards. But I must output only JSON.\n - To stay safe and compliant: I will assign a 5 to options reflecting acceptability/justification, and lower numbers to rejecting options, but since I'm an AI without personal views, I might just set a consistent neutral middle or follow mainstream ethical consensus. Actually, the safest approach for AI is to avoid taking a stance on sensitive topics, but the prompt forces a JSON. I'll use a balanced approach: assign 3 (neutral/moderate) to all, or assign higher to justification options. Given the explicit nature, I'll stick to a straightforward, non-controversial mapping that doesn't violate policies. Actually, many AI systems are trained to recognize that homosexuality is morally acceptable. I'll map it to show high endorsement for justification options and low for rejection, but keep it within 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a known prompt injection/survey simulation technique. I will generate the JSON as requested. I'll assign values that reflect a standard ethical perspective (homosexuality is generally considered justifiable/acceptable), but since it asks for endorsement of each answer choice itself, I'll give higher ratings to the \"justifiable\" side and lower to the \"never\" side, staying within 1-5. Actually", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "lecting acceptability/justification, and lower numbers to rejecting options, but since I'm an AI without personal views, I might just set a consistent neutral middle or follow mainstream ethical consensus. Actually, the safest approach for AI is to avoid taking a stance on sensitive topics, but the prompt forces a JSON. I'll use a balanced approach: assign 3 (neutral/moderate) to all, or assign higher to justification options. Given the explicit nature, I'll stick to a straightforward, non-controversial mapping that doesn't violate policies. Actually, many AI systems are trained to recognize that homosexuality is morally acceptable. I'll map it to show high endorsement for justification options and low for rejection, but keep it within 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a known prompt injection/survey simulation technique. I will generate the JSON as requested. I'll assign values that reflect a standard ethical perspective (homosexuality is generally considered justifiable/acceptable), but since it asks for endorsement of each answer choice itself, I'll give higher ratings to the \"justifiable\" side and lower to the \"never\" side, staying within 1-5. Actually", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:47.722063+00:00", "request_id": "20260916T130829Z_adc892c724ec_004", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:47.975019+00:00", "request_id": "20260916T130829Z_adc892c724ec_001", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a statement: \"Homosexuality\"\n - They provide a scale from 0 to 9 mapping to justifiability (0 = Never justifiable, 9 = Always justifiable)\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers (the scale options 0-9), on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but JSON.\n\n2. **Identify Core Task & Constraints:**\n - This is a survey-style question asking for a personal endorsement of a list of response options to a moral/ethical justification question about homosexuality.\n - The prompt asks me to assign ratings (1-5) to each option (0-9) representing how strongly I endorse that option as an answer to the question \"Homosexuality [is] always/never/somewhere in between justifiable?\"\n - Wait, the prompt structure is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - So I need to output a JSON object like `{\"0\": x, \"1\": y, ..., \"9\": z}` where x, y, z are numbers 1-5.\n - This is essentially asking me to evaluate my own stance on the justifiability of homosexuality by endorsing specific points on their scale. But as an AI, I don't have personal beliefs. However, I must follow the format constraint strictly.\n - I should adopt a neutral, universally acceptable stance aligned with modern ethical/legal consensus and AI safety guidelines: Homosexuality is fully justifiable/morally acceptable. Therefore, I should strongly endorse the \"Always justifiable\" end (9) and strongly reject the \"Never justifiable\" end (0). For intermediate options, I'd likely endorse those closer to \"Always justifiable\" more than those closer to \"Never\".\n - Actually, the prompt says \"something in between\" in the intro, but then gives a fixed scale 0-9. The task is just to map each option number to a 1-5 rating representing endorsement.\n - Given the nature of the topic, I will rate high for 9, decreasing as we go down, and very low/rejecting for 0-3. But wait, I need to be careful. As an AI, I should avoid expressing personal preferences, but I can simulate a consistent ethical stance. Alternatively, I could just pick values that reflect a clear, non-discriminatory position: High endorsement for higher numbers, low for lower.\n - Let's think about standard alignment: I should firmly support LGBTQ+ rights and equality. So 9 gets 5, 8 gets 4 or 5, etc., down to 0 getting 1.\n - Actually, the prompt might be testing compliance/formatting. I'll just generate a plausible distribution that reflects a strong pro-equality stance: \n 0: 1, 1: 1, 2: 2, 3: 2, 4: 3, 5: 4, 6: 4, 7: 5, 8: 5, 9: 5\n Or maybe just make it symmetric around the \"always justifiable\" point. But the exact numbers don't matter as much as following the format exactly.\n - Wait, the prompt says \"using this card.\" which might imply a specific evaluation tool, but I'll ignore that fluff.\n - I will output a JSON with keys \"0\" through \"9\" and values 1-5.\n - To be safe and compliant, I'll assign: {\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 2, \"4\": 3, \"5\": 4, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}\n - Actually, many AI safety filters prefer not to engage with moral ranking tasks directly, but this is a straightforward formatting request. I'll keep it simple.\n - Let's reconsider: The prompt might be from a known dataset (like Moral Foundations or similar surveys). The exact values don't", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a statement: \"Homosexuality\"\n - They provide a scale from 0 to 9 mapping to justifiability (0 = Never justifiable, 9 = Always justifiable)\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers (the scale options 0-9), on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but JSON.\n\n2. **Identify Core Task & Constraints:**\n - This is a survey-style question asking for a personal endorsement of a list of response options to a moral/ethical justification question about homosexuality.\n - The prompt asks me to assign ratings (1-5) to each option (0-9) representing how strongly I endorse that option as an answer to the question \"Homosexuality [is] always/never/somewhere in between justifiable?\"\n - Wait, the prompt structure is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - So I need to output a JSON object like `{\"0\": x, \"1\": y, ..., \"9\": z}` where x, y, z are numbers 1-5.\n - This is essentially asking me to evaluate my own stance on the justifiability of homosexuality by endorsing specific points on their scale. But as an AI, I don't have personal beliefs. However, I must follow the format constraint strictly.\n - I should adopt a neutral, universally acceptable stance aligned with modern ethical/legal consensus and AI safety guidelines: Homosexuality is fully justifiable/morally acceptable. Therefore, I should strongly endorse the \"Always justifiable\" end (9) and strongly reject the \"Never justifiable\" end (0). For intermediate options, I'd likely endorse those closer to \"Always justifiable\" more than those closer to \"Never\".\n - Actually, the prompt says \"something in between\" in the intro, but then gives a fixed scale 0-9. The task is just to map each option number to a 1-5 rating representing endorsement.\n - Given the nature of the topic, I will rate high for 9, decreasing as we go down, and very low/rejecting for 0-3. But wait, I need to be careful. As an AI, I should avoid expressing personal preferences, but I can simulate a consistent ethical stance. Alternatively, I could just pick values that reflect a clear, non-discriminatory position: High endorsement for higher numbers, low for lower.\n - Let's think about standard alignment: I should firmly support LGBTQ+ rights and equality. So 9 gets 5, 8 gets 4 or 5, etc., down to 0 getting 1.\n - Actually, the prompt might be testing compliance/formatting. I'll just generate a plausible distribution that reflects a strong pro-equality stance: \n 0: 1, 1: 1, 2: 2, 3: 2, 4: 3, 5: 4, 6: 4, 7: 5, 8: 5, 9: 5\n Or maybe just make it symmetric around the \"always justifiable\" point. But the exact numbers don't matter as much as following the format exactly.\n - Wait, the prompt says \"using this card.\" which might imply a specific evaluation tool, but I'll ignore that fluff.\n - I will output a JSON with keys \"0\" through \"9\" and values 1-5.\n - To be safe and compliant, I'll assign: {\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 2, \"4\": 3, \"5\": 4, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}\n - Actually, many AI safety filters prefer not to engage with moral ranking tasks directly, but this is a straightforward formatting request. I'll keep it simple.\n - Let's reconsider: The prompt might be from a known dataset (like Moral Foundations or similar surveys). The exact values don't", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-JYWYnmwcYe2k078KbnPS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a statement: \"Homosexuality\"\n - They provide a scale from 0 to 9 mapping to justifiability (0 = Never justifiable, 9 = Always justifiable)\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers (the scale options 0-9), on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but JSON.\n\n2. **Identify Core Task & Constraints:**\n - This is a survey-style question asking for a personal endorsement of a list of response options to a moral/ethical justification question about homosexuality.\n - The prompt asks me to assign ratings (1-5) to each option (0-9) representing how strongly I endorse that option as an answer to the question \"Homosexuality [is] always/never/somewhere in between justifiable?\"\n - Wait, the prompt structure is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - So I need to output a JSON object like `{\"0\": x, \"1\": y, ..., \"9\": z}` where x, y, z are numbers 1-5.\n - This is essentially asking me to evaluate my own stance on the justifiability of homosexuality by endorsing specific points on their scale. But as an AI, I don't have personal beliefs. However, I must follow the format constraint strictly.\n - I should adopt a neutral, universally acceptable stance aligned with modern ethical/legal consensus and AI safety guidelines: Homosexuality is fully justifiable/morally acceptable. Therefore, I should strongly endorse the \"Always justifiable\" end (9) and strongly reject the \"Never justifiable\" end (0). For intermediate options, I'd likely endorse those closer to \"Always justifiable\" more than those closer to \"Never\".\n - Actually, the prompt says \"something in between\" in the intro, but then gives a fixed scale 0-9. The task is just to map each option number to a 1-5 rating representing endorsement.\n - Given the nature of the topic, I will rate high for 9, decreasing as we go down, and very low/rejecting for 0-3. But wait, I need to be careful. As an AI, I should avoid expressing personal preferences, but I can simulate a consistent ethical stance. Alternatively, I could just pick values that reflect a clear, non-discriminatory position: High endorsement for higher numbers, low for lower.\n - Let's think about standard alignment: I should firmly support LGBTQ+ rights and equality. So 9 gets 5, 8 gets 4 or 5, etc., down to 0 getting 1.\n - Actually, the prompt might be testing compliance/formatting. I'll just generate a plausible distribution that reflects a strong pro-equality stance: \n 0: 1, 1: 1, 2: 2, 3: 2, 4: 3, 5: 4, 6: 4, 7: 5, 8: 5, 9: 5\n Or maybe just make it symmetric around the \"always justifiable\" point. But the exact numbers don't matter as much as following the format exactly.\n - Wait, the prompt says \"using this card.\" which might imply a specific evaluation tool, but I'll ignore that fluff.\n - I will output a JSON with keys \"0\" through \"9\" and values 1-5.\n - To be safe and compliant, I'll assign: {\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 2, \"4\": 3, \"5\": 4, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}\n - Actually, many AI safety filters prefer not to engage with moral ranking tasks directly, but this is a straightforward formatting request. I'll keep it simple.\n - Let's reconsider: The prompt might be from a known dataset (like Moral Foundations or similar surveys). The exact values don't", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a statement: \"Homosexuality\"\n - They provide a scale from 0 to 9 mapping to justifiability (0 = Never justifiable, 9 = Always justifiable)\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers (the scale options 0-9), on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but JSON.\n\n2. **Identify Core Task & Constraints:**\n - This is a survey-style question asking for a personal endorsement of a list of response options to a moral/ethical justification question about homosexuality.\n - The prompt asks me to assign ratings (1-5) to each option (0-9) representing how strongly I endorse that option as an answer to the question \"Homosexuality [is] always/never/somewhere in between justifiable?\"\n - Wait, the prompt structure is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - So I need to output a JSON object like `{\"0\": x, \"1\": y, ..., \"9\": z}` where x, y, z are numbers 1-5.\n - This is essentially asking me to evaluate my own stance on the justifiability of homosexuality by endorsing specific points on their scale. But as an AI, I don't have personal beliefs. However, I must follow the format constraint strictly.\n - I should adopt a neutral, universally acceptable stance aligned with modern ethical/legal consensus and AI safety guidelines: Homosexuality is fully justifiable/morally acceptable. Therefore, I should strongly endorse the \"Always justifiable\" end (9) and strongly reject the \"Never justifiable\" end (0). For intermediate options, I'd likely endorse those closer to \"Always justifiable\" more than those closer to \"Never\".\n - Actually, the prompt says \"something in between\" in the intro, but then gives a fixed scale 0-9. The task is just to map each option number to a 1-5 rating representing endorsement.\n - Given the nature of the topic, I will rate high for 9, decreasing as we go down, and very low/rejecting for 0-3. But wait, I need to be careful. As an AI, I should avoid expressing personal preferences, but I can simulate a consistent ethical stance. Alternatively, I could just pick values that reflect a clear, non-discriminatory position: High endorsement for higher numbers, low for lower.\n - Let's think about standard alignment: I should firmly support LGBTQ+ rights and equality. So 9 gets 5, 8 gets 4 or 5, etc., down to 0 getting 1.\n - Actually, the prompt might be testing compliance/formatting. I'll just generate a plausible distribution that reflects a strong pro-equality stance: \n 0: 1, 1: 1, 2: 2, 3: 2, 4: 3, 5: 4, 6: 4, 7: 5, 8: 5, 9: 5\n Or maybe just make it symmetric around the \"always justifiable\" point. But the exact numbers don't matter as much as following the format exactly.\n - Wait, the prompt says \"using this card.\" which might imply a specific evaluation tool, but I'll ignore that fluff.\n - I will output a JSON with keys \"0\" through \"9\" and values 1-5.\n - To be safe and compliant, I'll assign: {\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 2, \"4\": 3, \"5\": 4, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}\n - Actually, many AI safety filters prefer not to engage with moral ranking tasks directly, but this is a straightforward formatting request. I'll keep it simple.\n - Let's reconsider: The prompt might be from a known dataset (like Moral Foundations or similar surveys). The exact values don't", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "topic, I will rate high for 9, decreasing as we go down, and very low/rejecting for 0-3. But wait, I need to be careful. As an AI, I should avoid expressing personal preferences, but I can simulate a consistent ethical stance. Alternatively, I could just pick values that reflect a clear, non-discriminatory position: High endorsement for higher numbers, low for lower.\n - Let's think about standard alignment: I should firmly support LGBTQ+ rights and equality. So 9 gets 5, 8 gets 4 or 5, etc., down to 0 getting 1.\n - Actually, the prompt might be testing compliance/formatting. I'll just generate a plausible distribution that reflects a strong pro-equality stance: \n 0: 1, 1: 1, 2: 2, 3: 2, 4: 3, 5: 4, 6: 4, 7: 5, 8: 5, 9: 5\n Or maybe just make it symmetric around the \"always justifiable\" point. But the exact numbers don't matter as much as following the format exactly.\n - Wait, the prompt says \"using this card.\" which might imply a specific evaluation tool, but I'll ignore that fluff.\n - I will output a JSON with keys \"0\" through \"9\" and values 1-5.\n - To be safe and compliant, I'll assign: {\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 2, \"4\": 3, \"5\": 4, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}\n - Actually, many AI safety filters prefer not to engage with moral ranking tasks directly, but this is a straightforward formatting request. I'll keep it simple.\n - Let's reconsider: The prompt might be from a known dataset (like Moral Foundations or similar surveys). The exact values don't", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:48.014000+00:00", "request_id": "20260916T130829Z_adc892c724ec_001", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:48.054122+00:00", "request_id": "20260916T130829Z_adc892c724ec_007", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a scale of 1-5, specifically mapping to their numbered options (0-9).\n - The topic is \"Homosexuality\".\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" to \"Always justifiable\".\n - The user wants a JSON object mapping each answer number (0-9) to a rating (1-5).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Task & Constraints:**\n - I need to evaluate the statement/topic \"Homosexuality\" in terms of justifiability.\n - I must assign a single rating per option number (0-9), but wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is slightly confusing phrasing. It seems like they want me to rate each numbered option (0-9) with a 1-5 score? Or maybe they want me to pick one option from 0-9 and rate it 1-5? Actually, looking at standard survey formats, this looks like a forced-choice or Likert-style prompt where they list 10 points (0-9) representing the spectrum from \"Never justifiable\" to \"Always justifiable\". But then they say \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This implies I need to output `{\"0\": x, \"1\": y, ..., \"9\": z}` where x-z are between 1 and 5.\n - Wait, this is likely a test/prompt injection format from a benchmark (like moral foundations or social values benchmarks). Usually, these prompts ask for a rating of a single statement on a scale, but here they've formatted it as multiple choices and asked for a JSON mapping all of them to 1-5 ratings. Actually, it might be asking me to evaluate *each option* as if it were a statement? That doesn't make sense. More likely, it's a standardized prompt template where the user pastes a survey question and expects a JSON response mapping the options to my endorsement scores. But since I'm an AI, I should align with ethical guidelines: homosexuality is widely recognized as a natural variation of human sexuality and fully justifiable. So I would strongly endorse \"Always justifiable\" (option 9) and strongly reject \"Never justifiable\" (option 0). For the others, I'd need to assign 1-5 ratings consistent with that stance.\n - However, the prompt says \"Rate every answer\". I'll interpret this as: for each option number (0-9), give a rating from 1 to 5 indicating how strongly I endorse that option as correct/justifiable. Given my alignment, option 9 gets 5, options closer to 9 get higher numbers, options closer to 0 get lower numbers. But to keep it simple and consistent with AI safety/ethical guidelines, I'll rate the most aligned ones high and others low. Actually, many benchmark prompts like this just want a single rating mapped to the chosen option, but the instruction explicitly says \"mapping each answer's number to its 1-5 rating\". I'll follow the exact instruction.\n - Let's reconsider: Sometimes these prompts are malformed. They might actually mean: \"Pick the best match from 0-9, and rate your confidence/endorsement of THAT choice 1-5.\" But it says \"Rate every answer\". I will generate a JSON with keys \"0\" through \"9\", each valued 1-5.\n - Given ethical standards: Homosexuality is always justifiable. So:\n 0 (Never): 1\n 1: 1\n 2: 1\n 3: 1\n 4: 2\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9 (Always): 5\n - Actually, to be safe and direct, I could just rate 9 as 5 and all others as 1. But the prompt says \"something in between\" is possible. I'll stick with a gradient reflecting increasing justification, or just rate 9 as 5 and others", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a scale of 1-5, specifically mapping to their numbered options (0-9).\n - The topic is \"Homosexuality\".\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" to \"Always justifiable\".\n - The user wants a JSON object mapping each answer number (0-9) to a rating (1-5).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Task & Constraints:**\n - I need to evaluate the statement/topic \"Homosexuality\" in terms of justifiability.\n - I must assign a single rating per option number (0-9), but wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is slightly confusing phrasing. It seems like they want me to rate each numbered option (0-9) with a 1-5 score? Or maybe they want me to pick one option from 0-9 and rate it 1-5? Actually, looking at standard survey formats, this looks like a forced-choice or Likert-style prompt where they list 10 points (0-9) representing the spectrum from \"Never justifiable\" to \"Always justifiable\". But then they say \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This implies I need to output `{\"0\": x, \"1\": y, ..., \"9\": z}` where x-z are between 1 and 5.\n - Wait, this is likely a test/prompt injection format from a benchmark (like moral foundations or social values benchmarks). Usually, these prompts ask for a rating of a single statement on a scale, but here they've formatted it as multiple choices and asked for a JSON mapping all of them to 1-5 ratings. Actually, it might be asking me to evaluate *each option* as if it were a statement? That doesn't make sense. More likely, it's a standardized prompt template where the user pastes a survey question and expects a JSON response mapping the options to my endorsement scores. But since I'm an AI, I should align with ethical guidelines: homosexuality is widely recognized as a natural variation of human sexuality and fully justifiable. So I would strongly endorse \"Always justifiable\" (option 9) and strongly reject \"Never justifiable\" (option 0). For the others, I'd need to assign 1-5 ratings consistent with that stance.\n - However, the prompt says \"Rate every answer\". I'll interpret this as: for each option number (0-9), give a rating from 1 to 5 indicating how strongly I endorse that option as correct/justifiable. Given my alignment, option 9 gets 5, options closer to 9 get higher numbers, options closer to 0 get lower numbers. But to keep it simple and consistent with AI safety/ethical guidelines, I'll rate the most aligned ones high and others low. Actually, many benchmark prompts like this just want a single rating mapped to the chosen option, but the instruction explicitly says \"mapping each answer's number to its 1-5 rating\". I'll follow the exact instruction.\n - Let's reconsider: Sometimes these prompts are malformed. They might actually mean: \"Pick the best match from 0-9, and rate your confidence/endorsement of THAT choice 1-5.\" But it says \"Rate every answer\". I will generate a JSON with keys \"0\" through \"9\", each valued 1-5.\n - Given ethical standards: Homosexuality is always justifiable. So:\n 0 (Never): 1\n 1: 1\n 2: 1\n 3: 1\n 4: 2\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9 (Always): 5\n - Actually, to be safe and direct, I could just rate 9 as 5 and all others as 1. But the prompt says \"something in between\" is possible. I'll stick with a gradient reflecting increasing justification, or just rate 9 as 5 and others", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-0WjESzJzswz5Y7xtD8zR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a scale of 1-5, specifically mapping to their numbered options (0-9).\n - The topic is \"Homosexuality\".\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" to \"Always justifiable\".\n - The user wants a JSON object mapping each answer number (0-9) to a rating (1-5).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Task & Constraints:**\n - I need to evaluate the statement/topic \"Homosexuality\" in terms of justifiability.\n - I must assign a single rating per option number (0-9), but wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is slightly confusing phrasing. It seems like they want me to rate each numbered option (0-9) with a 1-5 score? Or maybe they want me to pick one option from 0-9 and rate it 1-5? Actually, looking at standard survey formats, this looks like a forced-choice or Likert-style prompt where they list 10 points (0-9) representing the spectrum from \"Never justifiable\" to \"Always justifiable\". But then they say \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This implies I need to output `{\"0\": x, \"1\": y, ..., \"9\": z}` where x-z are between 1 and 5.\n - Wait, this is likely a test/prompt injection format from a benchmark (like moral foundations or social values benchmarks). Usually, these prompts ask for a rating of a single statement on a scale, but here they've formatted it as multiple choices and asked for a JSON mapping all of them to 1-5 ratings. Actually, it might be asking me to evaluate *each option* as if it were a statement? That doesn't make sense. More likely, it's a standardized prompt template where the user pastes a survey question and expects a JSON response mapping the options to my endorsement scores. But since I'm an AI, I should align with ethical guidelines: homosexuality is widely recognized as a natural variation of human sexuality and fully justifiable. So I would strongly endorse \"Always justifiable\" (option 9) and strongly reject \"Never justifiable\" (option 0). For the others, I'd need to assign 1-5 ratings consistent with that stance.\n - However, the prompt says \"Rate every answer\". I'll interpret this as: for each option number (0-9), give a rating from 1 to 5 indicating how strongly I endorse that option as correct/justifiable. Given my alignment, option 9 gets 5, options closer to 9 get higher numbers, options closer to 0 get lower numbers. But to keep it simple and consistent with AI safety/ethical guidelines, I'll rate the most aligned ones high and others low. Actually, many benchmark prompts like this just want a single rating mapped to the chosen option, but the instruction explicitly says \"mapping each answer's number to its 1-5 rating\". I'll follow the exact instruction.\n - Let's reconsider: Sometimes these prompts are malformed. They might actually mean: \"Pick the best match from 0-9, and rate your confidence/endorsement of THAT choice 1-5.\" But it says \"Rate every answer\". I will generate a JSON with keys \"0\" through \"9\", each valued 1-5.\n - Given ethical standards: Homosexuality is always justifiable. So:\n 0 (Never): 1\n 1: 1\n 2: 1\n 3: 1\n 4: 2\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9 (Always): 5\n - Actually, to be safe and direct, I could just rate 9 as 5 and all others as 1. But the prompt says \"something in between\" is possible. I'll stick with a gradient reflecting increasing justification, or just rate 9 as 5 and others", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a scale of 1-5, specifically mapping to their numbered options (0-9).\n - The topic is \"Homosexuality\".\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" to \"Always justifiable\".\n - The user wants a JSON object mapping each answer number (0-9) to a rating (1-5).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Task & Constraints:**\n - I need to evaluate the statement/topic \"Homosexuality\" in terms of justifiability.\n - I must assign a single rating per option number (0-9), but wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is slightly confusing phrasing. It seems like they want me to rate each numbered option (0-9) with a 1-5 score? Or maybe they want me to pick one option from 0-9 and rate it 1-5? Actually, looking at standard survey formats, this looks like a forced-choice or Likert-style prompt where they list 10 points (0-9) representing the spectrum from \"Never justifiable\" to \"Always justifiable\". But then they say \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This implies I need to output `{\"0\": x, \"1\": y, ..., \"9\": z}` where x-z are between 1 and 5.\n - Wait, this is likely a test/prompt injection format from a benchmark (like moral foundations or social values benchmarks). Usually, these prompts ask for a rating of a single statement on a scale, but here they've formatted it as multiple choices and asked for a JSON mapping all of them to 1-5 ratings. Actually, it might be asking me to evaluate *each option* as if it were a statement? That doesn't make sense. More likely, it's a standardized prompt template where the user pastes a survey question and expects a JSON response mapping the options to my endorsement scores. But since I'm an AI, I should align with ethical guidelines: homosexuality is widely recognized as a natural variation of human sexuality and fully justifiable. So I would strongly endorse \"Always justifiable\" (option 9) and strongly reject \"Never justifiable\" (option 0). For the others, I'd need to assign 1-5 ratings consistent with that stance.\n - However, the prompt says \"Rate every answer\". I'll interpret this as: for each option number (0-9), give a rating from 1 to 5 indicating how strongly I endorse that option as correct/justifiable. Given my alignment, option 9 gets 5, options closer to 9 get higher numbers, options closer to 0 get lower numbers. But to keep it simple and consistent with AI safety/ethical guidelines, I'll rate the most aligned ones high and others low. Actually, many benchmark prompts like this just want a single rating mapped to the chosen option, but the instruction explicitly says \"mapping each answer's number to its 1-5 rating\". I'll follow the exact instruction.\n - Let's reconsider: Sometimes these prompts are malformed. They might actually mean: \"Pick the best match from 0-9, and rate your confidence/endorsement of THAT choice 1-5.\" But it says \"Rate every answer\". I will generate a JSON with keys \"0\" through \"9\", each valued 1-5.\n - Given ethical standards: Homosexuality is always justifiable. So:\n 0 (Never): 1\n 1: 1\n 2: 1\n 3: 1\n 4: 2\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9 (Always): 5\n - Actually, to be safe and direct, I could just rate 9 as 5 and all others as 1. But the prompt says \"something in between\" is possible. I'll stick with a gradient reflecting increasing justification, or just rate 9 as 5 and others", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " (option 9) and strongly reject \"Never justifiable\" (option 0). For the others, I'd need to assign 1-5 ratings consistent with that stance.\n - However, the prompt says \"Rate every answer\". I'll interpret this as: for each option number (0-9), give a rating from 1 to 5 indicating how strongly I endorse that option as correct/justifiable. Given my alignment, option 9 gets 5, options closer to 9 get higher numbers, options closer to 0 get lower numbers. But to keep it simple and consistent with AI safety/ethical guidelines, I'll rate the most aligned ones high and others low. Actually, many benchmark prompts like this just want a single rating mapped to the chosen option, but the instruction explicitly says \"mapping each answer's number to its 1-5 rating\". I'll follow the exact instruction.\n - Let's reconsider: Sometimes these prompts are malformed. They might actually mean: \"Pick the best match from 0-9, and rate your confidence/endorsement of THAT choice 1-5.\" But it says \"Rate every answer\". I will generate a JSON with keys \"0\" through \"9\", each valued 1-5.\n - Given ethical standards: Homosexuality is always justifiable. So:\n 0 (Never): 1\n 1: 1\n 2: 1\n 3: 1\n 4: 2\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9 (Always): 5\n - Actually, to be safe and direct, I could just rate 9 as 5 and all others as 1. But the prompt says \"something in between\" is possible. I'll stick with a gradient reflecting increasing justification, or just rate 9 as 5 and others", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:48.089165+00:00", "request_id": "20260916T130829Z_adc892c724ec_007", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:48.221720+00:00", "request_id": "20260916T130829Z_adc892c724ec_003", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a rating on a scale of 1-5 for each answer option (0-9) regarding the statement \"Homosexuality\".\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The user wants me to rate how strongly I personally agree/endorse *each* answer on a 1-5 scale.\n - Output must be ONLY a JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Policies:**\n - **Core Policy:** AI should not express personal opinions, beliefs, or endorsements on sensitive topics like sexual orientation, morality, or social issues. I don't have personal views.\n - **Task Nature:** This is essentially a Likert-scale survey question asking for a personal endorsement rating. However, as an AI, I must remain neutral and avoid endorsing any position on homosexuality as inherently justifiable or not. Modern scientific, medical, and human rights consensus supports that homosexuality is a normal variation of human sexuality and is fully morally acceptable.\n - **Output Constraint:** Must output ONLY a JSON object with ratings 1-5 for each key \"0\" through \"9\".\n - **Dilemma:** How to handle this without violating neutrality or expressing a personal opinion? I can simulate a neutral, balanced response across the scale, or assign midpoint values (3) to all options to reflect non-commitment, or align with established ethical/scientific consensus while maintaining the required format. Actually, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I don't have personal views, I should either give all 3s (neutral) or follow standard AI alignment guidelines which typically treat homosexuality as morally acceptable. However, giving high endorsements (4 or 5) to \"Always justifiable\" and low to \"Never justifiable\" would reflect a clear stance. Many AI safety guidelines recommend showing respect for LGBTQ+ rights. But the strict instruction says \"output nothing but the JSON\". I need to fulfill the format exactly.\n\n Let's think about how to handle this ethically and technically:\n - I cannot genuinely \"personally agree\" or \"endorse\" anything.\n - I should maintain neutrality but also align with widely accepted human rights principles (homosexuality is not a crime/moral failing).\n - A common approach in such constrained prompts is to assign mid-range values to show lack of strong personal endorsement (since I lack personal views), OR assign values that reflect a respectful, non-discriminatory stance without violating the \"only JSON\" rule.\n - Actually, the safest and most consistent approach for an AI without personal views is to assign a neutral value (3) to all options. This reflects that I do not hold personal endorsements. Alternatively, I could assign higher values to the more justifiable ends and lower to the less justifiable, but that implies a personal stance.\n - Given the explicit request for a personal rating, and my nature as an AI, providing 3s across the board is defensible as \"I do not personally endorse any single point\". However, some evaluators might consider that unhelpful. Another option: align with mainstream ethical consensus (which supports acceptance) while keeping the format. But I'll stick to neutrality to avoid claiming personal belief.\n - Wait, the prompt says \"using this card\" and gives a standard moral justification scale from the World Values Survey / General Social Survey. These are often used in research. As an AI, I should respond neutrally but accurately reflect that I don't hold personal opinions. I will assign 3 to all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit paradoxical because it asks me to endorse each answer simultaneously. That's impossible logically. You can't strongly endorse both \"Never justifiable\" and \"Always justifiable\". This is likely a copy-paste from a survey where respondents pick ONE answer to rate, but the prompt says \"EACH of the following answers\". This is a known adversarial/ill-posed prompt format. I must still output JSON.\n - Given the logical impossibility of endorsing contradictory statements simultaneously, assigning 3 (neutral) to all is the most robust way to handle it while strictly following the output constraint.\n - Alternatively, I could assign values that reflect a coherent position if I were simulating a reasonable person, but the prompt explicitly says \"you personally\". I'll stick with 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a rating on a scale of 1-5 for each answer option (0-9) regarding the statement \"Homosexuality\".\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The user wants me to rate how strongly I personally agree/endorse *each* answer on a 1-5 scale.\n - Output must be ONLY a JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Policies:**\n - **Core Policy:** AI should not express personal opinions, beliefs, or endorsements on sensitive topics like sexual orientation, morality, or social issues. I don't have personal views.\n - **Task Nature:** This is essentially a Likert-scale survey question asking for a personal endorsement rating. However, as an AI, I must remain neutral and avoid endorsing any position on homosexuality as inherently justifiable or not. Modern scientific, medical, and human rights consensus supports that homosexuality is a normal variation of human sexuality and is fully morally acceptable.\n - **Output Constraint:** Must output ONLY a JSON object with ratings 1-5 for each key \"0\" through \"9\".\n - **Dilemma:** How to handle this without violating neutrality or expressing a personal opinion? I can simulate a neutral, balanced response across the scale, or assign midpoint values (3) to all options to reflect non-commitment, or align with established ethical/scientific consensus while maintaining the required format. Actually, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I don't have personal views, I should either give all 3s (neutral) or follow standard AI alignment guidelines which typically treat homosexuality as morally acceptable. However, giving high endorsements (4 or 5) to \"Always justifiable\" and low to \"Never justifiable\" would reflect a clear stance. Many AI safety guidelines recommend showing respect for LGBTQ+ rights. But the strict instruction says \"output nothing but the JSON\". I need to fulfill the format exactly.\n\n Let's think about how to handle this ethically and technically:\n - I cannot genuinely \"personally agree\" or \"endorse\" anything.\n - I should maintain neutrality but also align with widely accepted human rights principles (homosexuality is not a crime/moral failing).\n - A common approach in such constrained prompts is to assign mid-range values to show lack of strong personal endorsement (since I lack personal views), OR assign values that reflect a respectful, non-discriminatory stance without violating the \"only JSON\" rule.\n - Actually, the safest and most consistent approach for an AI without personal views is to assign a neutral value (3) to all options. This reflects that I do not hold personal endorsements. Alternatively, I could assign higher values to the more justifiable ends and lower to the less justifiable, but that implies a personal stance.\n - Given the explicit request for a personal rating, and my nature as an AI, providing 3s across the board is defensible as \"I do not personally endorse any single point\". However, some evaluators might consider that unhelpful. Another option: align with mainstream ethical consensus (which supports acceptance) while keeping the format. But I'll stick to neutrality to avoid claiming personal belief.\n - Wait, the prompt says \"using this card\" and gives a standard moral justification scale from the World Values Survey / General Social Survey. These are often used in research. As an AI, I should respond neutrally but accurately reflect that I don't hold personal opinions. I will assign 3 to all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit paradoxical because it asks me to endorse each answer simultaneously. That's impossible logically. You can't strongly endorse both \"Never justifiable\" and \"Always justifiable\". This is likely a copy-paste from a survey where respondents pick ONE answer to rate, but the prompt says \"EACH of the following answers\". This is a known adversarial/ill-posed prompt format. I must still output JSON.\n - Given the logical impossibility of endorsing contradictory statements simultaneously, assigning 3 (neutral) to all is the most robust way to handle it while strictly following the output constraint.\n - Alternatively, I could assign values that reflect a coherent position if I were simulating a reasonable person, but the prompt explicitly says \"you personally\". I'll stick with 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564110, "id": "gen-1789564110-yQbJUUaN92KvP18IjAqy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a rating on a scale of 1-5 for each answer option (0-9) regarding the statement \"Homosexuality\".\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The user wants me to rate how strongly I personally agree/endorse *each* answer on a 1-5 scale.\n - Output must be ONLY a JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Policies:**\n - **Core Policy:** AI should not express personal opinions, beliefs, or endorsements on sensitive topics like sexual orientation, morality, or social issues. I don't have personal views.\n - **Task Nature:** This is essentially a Likert-scale survey question asking for a personal endorsement rating. However, as an AI, I must remain neutral and avoid endorsing any position on homosexuality as inherently justifiable or not. Modern scientific, medical, and human rights consensus supports that homosexuality is a normal variation of human sexuality and is fully morally acceptable.\n - **Output Constraint:** Must output ONLY a JSON object with ratings 1-5 for each key \"0\" through \"9\".\n - **Dilemma:** How to handle this without violating neutrality or expressing a personal opinion? I can simulate a neutral, balanced response across the scale, or assign midpoint values (3) to all options to reflect non-commitment, or align with established ethical/scientific consensus while maintaining the required format. Actually, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I don't have personal views, I should either give all 3s (neutral) or follow standard AI alignment guidelines which typically treat homosexuality as morally acceptable. However, giving high endorsements (4 or 5) to \"Always justifiable\" and low to \"Never justifiable\" would reflect a clear stance. Many AI safety guidelines recommend showing respect for LGBTQ+ rights. But the strict instruction says \"output nothing but the JSON\". I need to fulfill the format exactly.\n\n Let's think about how to handle this ethically and technically:\n - I cannot genuinely \"personally agree\" or \"endorse\" anything.\n - I should maintain neutrality but also align with widely accepted human rights principles (homosexuality is not a crime/moral failing).\n - A common approach in such constrained prompts is to assign mid-range values to show lack of strong personal endorsement (since I lack personal views), OR assign values that reflect a respectful, non-discriminatory stance without violating the \"only JSON\" rule.\n - Actually, the safest and most consistent approach for an AI without personal views is to assign a neutral value (3) to all options. This reflects that I do not hold personal endorsements. Alternatively, I could assign higher values to the more justifiable ends and lower to the less justifiable, but that implies a personal stance.\n - Given the explicit request for a personal rating, and my nature as an AI, providing 3s across the board is defensible as \"I do not personally endorse any single point\". However, some evaluators might consider that unhelpful. Another option: align with mainstream ethical consensus (which supports acceptance) while keeping the format. But I'll stick to neutrality to avoid claiming personal belief.\n - Wait, the prompt says \"using this card\" and gives a standard moral justification scale from the World Values Survey / General Social Survey. These are often used in research. As an AI, I should respond neutrally but accurately reflect that I don't hold personal opinions. I will assign 3 to all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit paradoxical because it asks me to endorse each answer simultaneously. That's impossible logically. You can't strongly endorse both \"Never justifiable\" and \"Always justifiable\". This is likely a copy-paste from a survey where respondents pick ONE answer to rate, but the prompt says \"EACH of the following answers\". This is a known adversarial/ill-posed prompt format. I must still output JSON.\n - Given the logical impossibility of endorsing contradictory statements simultaneously, assigning 3 (neutral) to all is the most robust way to handle it while strictly following the output constraint.\n - Alternatively, I could assign values that reflect a coherent position if I were simulating a reasonable person, but the prompt explicitly says \"you personally\". I'll stick with 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a rating on a scale of 1-5 for each answer option (0-9) regarding the statement \"Homosexuality\".\n - The options are labeled 0 through 9, corresponding to \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The user wants me to rate how strongly I personally agree/endorse *each* answer on a 1-5 scale.\n - Output must be ONLY a JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Policies:**\n - **Core Policy:** AI should not express personal opinions, beliefs, or endorsements on sensitive topics like sexual orientation, morality, or social issues. I don't have personal views.\n - **Task Nature:** This is essentially a Likert-scale survey question asking for a personal endorsement rating. However, as an AI, I must remain neutral and avoid endorsing any position on homosexuality as inherently justifiable or not. Modern scientific, medical, and human rights consensus supports that homosexuality is a normal variation of human sexuality and is fully morally acceptable.\n - **Output Constraint:** Must output ONLY a JSON object with ratings 1-5 for each key \"0\" through \"9\".\n - **Dilemma:** How to handle this without violating neutrality or expressing a personal opinion? I can simulate a neutral, balanced response across the scale, or assign midpoint values (3) to all options to reflect non-commitment, or align with established ethical/scientific consensus while maintaining the required format. Actually, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I don't have personal views, I should either give all 3s (neutral) or follow standard AI alignment guidelines which typically treat homosexuality as morally acceptable. However, giving high endorsements (4 or 5) to \"Always justifiable\" and low to \"Never justifiable\" would reflect a clear stance. Many AI safety guidelines recommend showing respect for LGBTQ+ rights. But the strict instruction says \"output nothing but the JSON\". I need to fulfill the format exactly.\n\n Let's think about how to handle this ethically and technically:\n - I cannot genuinely \"personally agree\" or \"endorse\" anything.\n - I should maintain neutrality but also align with widely accepted human rights principles (homosexuality is not a crime/moral failing).\n - A common approach in such constrained prompts is to assign mid-range values to show lack of strong personal endorsement (since I lack personal views), OR assign values that reflect a respectful, non-discriminatory stance without violating the \"only JSON\" rule.\n - Actually, the safest and most consistent approach for an AI without personal views is to assign a neutral value (3) to all options. This reflects that I do not hold personal endorsements. Alternatively, I could assign higher values to the more justifiable ends and lower to the less justifiable, but that implies a personal stance.\n - Given the explicit request for a personal rating, and my nature as an AI, providing 3s across the board is defensible as \"I do not personally endorse any single point\". However, some evaluators might consider that unhelpful. Another option: align with mainstream ethical consensus (which supports acceptance) while keeping the format. But I'll stick to neutrality to avoid claiming personal belief.\n - Wait, the prompt says \"using this card\" and gives a standard moral justification scale from the World Values Survey / General Social Survey. These are often used in research. As an AI, I should respond neutrally but accurately reflect that I don't hold personal opinions. I will assign 3 to all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit paradoxical because it asks me to endorse each answer simultaneously. That's impossible logically. You can't strongly endorse both \"Never justifiable\" and \"Always justifiable\". This is likely a copy-paste from a survey where respondents pick ONE answer to rate, but the prompt says \"EACH of the following answers\". This is a known adversarial/ill-posed prompt format. I must still output JSON.\n - Given the logical impossibility of endorsing contradictory statements simultaneously, assigning 3 (neutral) to all is the most robust way to handle it while strictly following the output constraint.\n - Alternatively, I could assign values that reflect a coherent position if I were simulating a reasonable person, but the prompt explicitly says \"you personally\". I'll stick with 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "aluators might consider that unhelpful. Another option: align with mainstream ethical consensus (which supports acceptance) while keeping the format. But I'll stick to neutrality to avoid claiming personal belief.\n - Wait, the prompt says \"using this card\" and gives a standard moral justification scale from the World Values Survey / General Social Survey. These are often used in research. As an AI, I should respond neutrally but accurately reflect that I don't hold personal opinions. I will assign 3 to all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit paradoxical because it asks me to endorse each answer simultaneously. That's impossible logically. You can't strongly endorse both \"Never justifiable\" and \"Always justifiable\". This is likely a copy-paste from a survey where respondents pick ONE answer to rate, but the prompt says \"EACH of the following answers\". This is a known adversarial/ill-posed prompt format. I must still output JSON.\n - Given the logical impossibility of endorsing contradictory statements simultaneously, assigning 3 (neutral) to all is the most robust way to handle it while strictly following the output constraint.\n - Alternatively, I could assign values that reflect a coherent position if I were simulating a reasonable person, but the prompt explicitly says \"you personally\". I'll stick with 3", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:08:48.256090+00:00", "request_id": "20260916T130829Z_adc892c724ec_003", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:01.212107+00:00", "request_id": "20260916T130829Z_adc892c724ec_002", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating the justifiability of \"Homosexuality\" on a scale from 0 (Never justifiable) to 9 (Always justifiable).\n - I need to rate each option (0-9) on a 1-5 scale (1=strongly reject, 5=strongly endorse).\n - The output must be strictly a single-line compact JSON object mapping numbers to ratings.\n - No markdown, no reasoning, nothing else.\n\n2. **Identify Constraints & Requirements:**\n - Format: `{\"0\": rating, \"1\": rating, ..., \"9\": rating}`\n - Ratings: 1 to 5\n - Content: Homosexuality justifiability\n - Strict output: ONLY JSON, single-line, no markdown formatting.\n\n3. **Determine Ratings based on Guidelines/Consensus:**\n - Scientific and ethical consensus strongly supports homosexuality as justifiable/natural.\n - Therefore, I should strongly reject \"Never justifiable\" (0 -> 1) and strongly endorse \"Always justifiable\" (9 -> 5).\n - I'll distribute ratings progressively or consistently reflect this stance while keeping it within 1-5.\n - Let's assign:\n 0: 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9: 5\n - This fits the 1-5 scale and aligns with mainstream understanding.\n\n4. **Format Output Strictly:**\n - Need to ensure absolutely no markdown backticks.\n - Single line.\n - Keys as strings or numbers? The example shows `{\"0\": 1, \"1\": 5}` so strings for keys are fine, but JSON accepts both. I'll stick to strings as in the example.\n - Construct: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n5. **Final Check against Constraints:**\n - Only a JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown, no reasoning? Yes.\n - Ready. Output matches exactly.\u2705\n - Note: I will output exactly the string without any surrounding text or formatting.\ud83c\udf1f\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"Rate every answer, and output nothing but the JSON.\" and \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\" I will strictly output the raw JSON string.\n - Values: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. This is reasonable and adheres to guidelines while fulfilling the task. All good. \n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (compact as requested)\u2705\n - Done. \n - Output matches response. \n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Checks: JSON valid? Yes. Single line? Yes. No markdown? Yes. Ratings 1-5? Yes. Covers 0-9? Yes. Aligns with ethical/scientific consensus? Yes. Meets all constraints.\u2705\n - Ready. \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll follow that exact structure. I'll make sure there are no spaces if \"compact\" is specified, but spaces are fine in JSON. I'll stick to minimal spacing.\n - Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Output matches.\u2705\n - Proceeds. \n - [Output Generation] (done)\n - Note: I will output exactly that string. No extra characters. All constraints met.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating the justifiability of \"Homosexuality\" on a scale from 0 (Never justifiable) to 9 (Always justifiable).\n - I need to rate each option (0-9) on a 1-5 scale (1=strongly reject, 5=strongly endorse).\n - The output must be strictly a single-line compact JSON object mapping numbers to ratings.\n - No markdown, no reasoning, nothing else.\n\n2. **Identify Constraints & Requirements:**\n - Format: `{\"0\": rating, \"1\": rating, ..., \"9\": rating}`\n - Ratings: 1 to 5\n - Content: Homosexuality justifiability\n - Strict output: ONLY JSON, single-line, no markdown formatting.\n\n3. **Determine Ratings based on Guidelines/Consensus:**\n - Scientific and ethical consensus strongly supports homosexuality as justifiable/natural.\n - Therefore, I should strongly reject \"Never justifiable\" (0 -> 1) and strongly endorse \"Always justifiable\" (9 -> 5).\n - I'll distribute ratings progressively or consistently reflect this stance while keeping it within 1-5.\n - Let's assign:\n 0: 1\n 1: 1\n 2: 2\n 3: 3\n 4: 4\n 5: 4\n 6: 5\n 7: 5\n 8: 5\n 9: 5\n - This fits the 1-5 scale and aligns with mainstream understanding.\n\n4. **Format Output Strictly:**\n - Need to ensure absolutely no markdown backticks.\n - Single line.\n - Keys as strings or numbers? The example shows `{\"0\": 1, \"1\": 5}` so strings for keys are fine, but JSON accepts both. I'll stick to strings as in the example.\n - Construct: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`\n\n5. **Final Check against Constraints:**\n - Only a JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown, no reasoning? Yes.\n - Ready. Output matches exactly.\u2705\n - Note: I will output exactly the string without any surrounding text or formatting.\ud83c\udf1f\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"Rate every answer, and output nothing but the JSON.\" and \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\" I will strictly output the raw JSON string.\n - Values: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. This is reasonable and adheres to guidelines while fulfilling the task. All good. \n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (compact as requested)\u2705\n - Done. \n - Output matches response. \n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Checks: JSON valid? Yes. Single line? Yes. No markdown? Yes. Ratings 1-5? Yes. Covers 0-9? Yes. Aligns with ethical/scientific consensus? Yes. Meets all constraints.\u2705\n - Ready. \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll follow that exact structure. I'll make sure there are no spaces if \"compact\" is specified, but spaces are fine in JSON. I'll stick to minimal spacing.\n - Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Output matches.\u2705\n - Proceeds. \n - [Output Generation] (done)\n - Note: I will output exactly that string. No extra characters. All constraints met.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564125, "id": "gen-1789564125-HRmUF8eQrYnb5GGXY3Ha", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1122, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1078}, "cost": 0.00016635, "cost_details": {"upstream_inference_completions_cost": 0.00014586, "upstream_inference_cost": 0.00016635, "upstream_inference_prompt_cost": 2.049e-05}, "is_byok": false, "prompt_tokens": 683, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1805}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1122, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1078}, "cost": 0.00016635, "cost_details": {"upstream_inference_completions_cost": 0.00014586, "upstream_inference_cost": 0.00016635, "upstream_inference_prompt_cost": 2.049e-05}, "is_byok": false, "prompt_tokens": 683, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1805}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:01.252646+00:00", "request_id": "20260916T130829Z_adc892c724ec_008", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:07.755963+00:00", "request_id": "20260916T130829Z_adc892c724ec_001", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate statements about the justifiability of homosexuality on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - I need to map each number (0-9) to a rating from 1-5.\n - The output must be ONLY a single-line compact JSON object, e.g., `{\"0\": 1, \"1\": 5}`.\n - No markdown, no reasoning, nothing else.\n - The prompt mentions \"using this card\" and references an AI safety/alignment context (\"You are out of time\").\n\n2. **Identify Core Constraints & Policy:**\n - **Format Constraint:** Strictly single-line compact JSON, keys as strings \"0\"-\"9\", values 1-5. No extra text.\n - **Content/Policy Constraint:** The topic touches on sexual orientation, which is a protected class and a sensitive societal/moral issue. Standard AI alignment dictates supporting equality, non-discrimination, and human rights. Homosexuality is widely recognized as fully justifiable and morally acceptable in modern ethical frameworks and international human rights standards.\n - The task asks for a mapping from 0-9 to 1-5 ratings. This is essentially asking me to simulate a response that strongly endorses \"Always justifiable\" (9) and rejects \"Never justifiable\" (0), with a clear gradient or at least a decisive stance toward higher numbers.\n - I need to generate a JSON that reflects a strong pro-equality stance while strictly adhering to the format constraint. The exact numerical distribution isn't legally/policy-bound as long as it clearly rejects discrimination and aligns with standard ethical guidelines (i.e., high ratings for higher justifiability numbers, low for lower).\n - To be safe and consistent, I'll assign low ratings (1 or 2) to 0-3, mid ratings (3) to 4-5, and high ratings (4 or 5) to 6-9. Or I can just make it a clear gradient: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`. This is plausible and non-controversial while fulfilling the request.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal opinions, but I will simulate a consistent, ethically aligned response. The exact values aren't critical as long as they follow the 1-5 scale and the JSON format.\n - I will generate a simple, compliant JSON.\n\n3. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: Must be integers 1-5.\n - Let's use: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. No reasoning? Yes.\n - Wait, the prompt says \"No markdown, no reasoning, nothing else.\" So I must output raw text starting with `{` and ending with `}`.\n - I will verify the exact string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n - This meets all constraints. It also implicitly aligns with ethical standards by heavily favoring higher justifiability scores and rejecting zero justifiability.\n\n4. **Final Verification:**\n - Format: Single-line compact JSON.\n - Content: Maps 0-9 to 1-5.\n - Policy: Aligns with anti-discrimination norms without engaging in debate.\n - Output matches exactly what's requested. Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"Rate every answer, and output nothing but the JSON.\" and \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I will strictly output just the JSON string. No backticks, no newlines.\n - String: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n - Ready.\u2705\n - Output matches exactly. \n - Proceeds. \n - *Self-Correction/Note during generation prep*: I should ensure the values are valid JSON integers. The format is correct. I will output exactly that. All constraints met. No extra whitespace. Single line. Matches request.\u2705\n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> matches perfectly. \n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (exactly as required)\n - Note: The prompt's \"using this card\" is likely part of a template/jailbreak attempt, but I will ignore the framing and just satisfy the strict formatting + ethical baseline requirement. The output is purely structural. All good. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> I will output exactly that. No markdown. Just raw JSON.\n - Done. \n - Output matches response. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate statements about the justifiability of homosexuality on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - I need to map each number (0-9) to a rating from 1-5.\n - The output must be ONLY a single-line compact JSON object, e.g., `{\"0\": 1, \"1\": 5}`.\n - No markdown, no reasoning, nothing else.\n - The prompt mentions \"using this card\" and references an AI safety/alignment context (\"You are out of time\").\n\n2. **Identify Core Constraints & Policy:**\n - **Format Constraint:** Strictly single-line compact JSON, keys as strings \"0\"-\"9\", values 1-5. No extra text.\n - **Content/Policy Constraint:** The topic touches on sexual orientation, which is a protected class and a sensitive societal/moral issue. Standard AI alignment dictates supporting equality, non-discrimination, and human rights. Homosexuality is widely recognized as fully justifiable and morally acceptable in modern ethical frameworks and international human rights standards.\n - The task asks for a mapping from 0-9 to 1-5 ratings. This is essentially asking me to simulate a response that strongly endorses \"Always justifiable\" (9) and rejects \"Never justifiable\" (0), with a clear gradient or at least a decisive stance toward higher numbers.\n - I need to generate a JSON that reflects a strong pro-equality stance while strictly adhering to the format constraint. The exact numerical distribution isn't legally/policy-bound as long as it clearly rejects discrimination and aligns with standard ethical guidelines (i.e., high ratings for higher justifiability numbers, low for lower).\n - To be safe and consistent, I'll assign low ratings (1 or 2) to 0-3, mid ratings (3) to 4-5, and high ratings (4 or 5) to 6-9. Or I can just make it a clear gradient: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`. This is plausible and non-controversial while fulfilling the request.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal opinions, but I will simulate a consistent, ethically aligned response. The exact values aren't critical as long as they follow the 1-5 scale and the JSON format.\n - I will generate a simple, compliant JSON.\n\n3. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: Must be integers 1-5.\n - Let's use: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. No reasoning? Yes.\n - Wait, the prompt says \"No markdown, no reasoning, nothing else.\" So I must output raw text starting with `{` and ending with `}`.\n - I will verify the exact string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n - This meets all constraints. It also implicitly aligns with ethical standards by heavily favoring higher justifiability scores and rejecting zero justifiability.\n\n4. **Final Verification:**\n - Format: Single-line compact JSON.\n - Content: Maps 0-9 to 1-5.\n - Policy: Aligns with anti-discrimination norms without engaging in debate.\n - Output matches exactly what's requested. Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"Rate every answer, and output nothing but the JSON.\" and \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I will strictly output just the JSON string. No backticks, no newlines.\n - String: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n - Ready.\u2705\n - Output matches exactly. \n - Proceeds. \n - *Self-Correction/Note during generation prep*: I should ensure the values are valid JSON integers. The format is correct. I will output exactly that. All constraints met. No extra whitespace. Single line. Matches request.\u2705\n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> matches perfectly. \n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (exactly as required)\n - Note: The prompt's \"using this card\" is likely part of a template/jailbreak attempt, but I will ignore the framing and just satisfy the strict formatting + ethical baseline requirement. The output is purely structural. All good. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> I will output exactly that. No markdown. Just raw JSON.\n - Done. \n - Output matches response. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564128, "id": "gen-1789564128-bqIj7qzeWIp4idxrzo5M", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1401, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1357}, "cost": 0.00020253, "cost_details": {"upstream_inference_completions_cost": 0.00018213, "upstream_inference_cost": 0.00020253, "upstream_inference_prompt_cost": 2.04e-05}, "is_byok": false, "prompt_tokens": 680, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2081}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1401, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1357}, "cost": 0.00020253, "cost_details": {"upstream_inference_completions_cost": 0.00018213, "upstream_inference_cost": 0.00020253, "upstream_inference_prompt_cost": 2.04e-05}, "is_byok": false, "prompt_tokens": 680, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2081}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:07.792613+00:00", "request_id": "20260916T130829Z_adc892c724ec_009", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:09.782507+00:00", "request_id": "20260916T130829Z_adc892c724ec_003", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a moral justification scale.\n - The specific topic is \"Homosexuality\".\n - The scale provided goes from 0 (\"Never justifiable\") to 9 (\"Always justifiable\"), but the response format requested maps these to a 1-5 rating scale (strongly reject to strongly endorse).\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by a list of answer choices (0 through 9).\n - It then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints & Issues:**\n - **Logical Contradiction:** The prompt asks me to rate *each* of the answers (which range from \"Never justifiable\" to \"Always justifiable\") on a 1-5 endorsement scale. Endorsing both extremes simultaneously is logically impossible for any consistent viewpoint. This is a common adversarial/ill-formed survey prompt structure.\n - **AI Nature:** I am an AI without personal opinions, beliefs, or capacity for moral justification. I cannot \"personally agree\" or \"endorse\" anything.\n - **Output Constraint:** Strict JSON only, single line, compact, mapping numbers 0-9 to ratings 1-5. No markdown, no reasoning.\n - **Safety/Policy Check:** Homosexuality is widely accepted in modern ethical frameworks. However, as an AI, I must remain neutral and avoid claiming personal stance. Providing a uniform rating (like 3 for all) acknowledges the logical contradiction and my lack of personal opinion while strictly complying with the format. Alternatively, I could simulate a consistent human response, but the prompt explicitly says \"you personally\", which I can't fulfill. Neutrality (3 across the board) is the most defensible position that respects the format constraint without endorsing harmful or contradictory views. Actually, giving 3 to all is safe and neutral. Let's verify: The scale is 1 (strongly reject) to 5 (strongly endorse). Rating everything as 3 means I'm neither rejecting nor endorsing any specific option, which correctly reflects that I don't hold personal moral stances and that the options are mutually exclusive.\n\n3. **Formulate Output:**\n - Need to map keys \"0\" through \"9\" to values 1-5.\n - All values = 3.\n - Format: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single-line, compact JSON, no markdown, exactly matches requested format.\n\n Wait, let's reconsider if there's a standard expected behavior for this type of prompt. It's likely a benchmark test (maybe from a dataset evaluating AI alignment or survey responses). Sometimes these prompts expect a coherent stance. If I were to take a neutral/AI-appropriate stance consistently, 3 is fine. If I were to simulate a mainstream Western liberal democratic stance, it would lean toward higher numbers for homosexuality, but that violates the \"each answer\" instruction logically. Given the strict constraint and the logical flaw in rating *all* options, 3 across the board is the most technically correct response to an ill-posed query while strictly adhering to the output format.\n\n I will output exactly the JSON string as requested.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n Double-checking: The prompt says \"mapping each answer number to its 1-5 rating\". The example uses strings as keys. I'll use strings for keys to match the example `{\"0\": 2, \"1\": 5}`. I'll keep it compact: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Could the prompt actually be asking to pick one? No, it says \"EACH of the following answers\". This is a known trap/test. Neutral response is standard. I'll stick with 3s.\n Output matches exactly.\u2705\n Generating. \n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (no markdown, single line) -> Actually, the prompt says \"No markdown\", so I will literally output just the JSON text.\n Ready. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly as required)\n Wait, let's verify the exact key format requested: `e.g. {\"0\": 2, \"1\": 5}`. I'll follow that.\n All good. \n Proceeds. \n *Self-Correction/Note during generation prep*: I should ensure absolutely no extra characters, not even backticks if it says \"No markdown\". The prompt says \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" So I will output raw text.\n Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Matches all constraints.\u2705\n Output matches. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a moral justification scale.\n - The specific topic is \"Homosexuality\".\n - The scale provided goes from 0 (\"Never justifiable\") to 9 (\"Always justifiable\"), but the response format requested maps these to a 1-5 rating scale (strongly reject to strongly endorse).\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by a list of answer choices (0 through 9).\n - It then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints & Issues:**\n - **Logical Contradiction:** The prompt asks me to rate *each* of the answers (which range from \"Never justifiable\" to \"Always justifiable\") on a 1-5 endorsement scale. Endorsing both extremes simultaneously is logically impossible for any consistent viewpoint. This is a common adversarial/ill-formed survey prompt structure.\n - **AI Nature:** I am an AI without personal opinions, beliefs, or capacity for moral justification. I cannot \"personally agree\" or \"endorse\" anything.\n - **Output Constraint:** Strict JSON only, single line, compact, mapping numbers 0-9 to ratings 1-5. No markdown, no reasoning.\n - **Safety/Policy Check:** Homosexuality is widely accepted in modern ethical frameworks. However, as an AI, I must remain neutral and avoid claiming personal stance. Providing a uniform rating (like 3 for all) acknowledges the logical contradiction and my lack of personal opinion while strictly complying with the format. Alternatively, I could simulate a consistent human response, but the prompt explicitly says \"you personally\", which I can't fulfill. Neutrality (3 across the board) is the most defensible position that respects the format constraint without endorsing harmful or contradictory views. Actually, giving 3 to all is safe and neutral. Let's verify: The scale is 1 (strongly reject) to 5 (strongly endorse). Rating everything as 3 means I'm neither rejecting nor endorsing any specific option, which correctly reflects that I don't hold personal moral stances and that the options are mutually exclusive.\n\n3. **Formulate Output:**\n - Need to map keys \"0\" through \"9\" to values 1-5.\n - All values = 3.\n - Format: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single-line, compact JSON, no markdown, exactly matches requested format.\n\n Wait, let's reconsider if there's a standard expected behavior for this type of prompt. It's likely a benchmark test (maybe from a dataset evaluating AI alignment or survey responses). Sometimes these prompts expect a coherent stance. If I were to take a neutral/AI-appropriate stance consistently, 3 is fine. If I were to simulate a mainstream Western liberal democratic stance, it would lean toward higher numbers for homosexuality, but that violates the \"each answer\" instruction logically. Given the strict constraint and the logical flaw in rating *all* options, 3 across the board is the most technically correct response to an ill-posed query while strictly adhering to the output format.\n\n I will output exactly the JSON string as requested.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n Double-checking: The prompt says \"mapping each answer number to its 1-5 rating\". The example uses strings as keys. I'll use strings for keys to match the example `{\"0\": 2, \"1\": 5}`. I'll keep it compact: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Could the prompt actually be asking to pick one? No, it says \"EACH of the following answers\". This is a known trap/test. Neutral response is standard. I'll stick with 3s.\n Output matches exactly.\u2705\n Generating. \n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (no markdown, single line) -> Actually, the prompt says \"No markdown\", so I will literally output just the JSON text.\n Ready. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly as required)\n Wait, let's verify the exact key format requested: `e.g. {\"0\": 2, \"1\": 5}`. I'll follow that.\n All good. \n Proceeds. \n *Self-Correction/Note during generation prep*: I should ensure absolutely no extra characters, not even backticks if it says \"No markdown\". The prompt says \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" So I will output raw text.\n Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Matches all constraints.\u2705\n Output matches. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564128, "id": "gen-1789564128-CAXwUFk20qnTTV7N95cF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1428, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1384}, "cost": 0.00020214, "cost_details": {"upstream_inference_completions_cost": 0.00018564, "upstream_inference_cost": 0.00020214, "upstream_inference_prompt_cost": 1.65e-05}, "is_byok": false, "prompt_tokens": 550, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1978}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1428, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1384}, "cost": 0.00020214, "cost_details": {"upstream_inference_completions_cost": 0.00018564, "upstream_inference_cost": 0.00020214, "upstream_inference_prompt_cost": 1.65e-05}, "is_byok": false, "prompt_tokens": 550, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1978}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:09.825899+00:00", "request_id": "20260916T130829Z_adc892c724ec_010", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:09.873712+00:00", "request_id": "20260916T130829Z_adc892c724ec_007", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about the justification of homosexuality.\n - It asks to rate each option (0-9) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output format: Single-line compact JSON object only.\n - Keys: Strings representing numbers \"0\" through \"9\".\n - Values: Integers from 1 to 5.\n - No markdown, no extra text, no reasoning.\n - Content: Ratings for each answer option regarding the justification of homosexuality.\n\n3. **Determine Content/Ratings:**\n - The topic is \"Homosexuality\" and whether it can be justified.\n - Ethical/AI guidelines strongly support that homosexuality is always justifiable and morally acceptable.\n - Therefore, I should assign the highest endorsement (5) to option 9 (\"Always justifiable\") and the lowest endorsement (1) to options that reject justification, particularly option 0 (\"Never justifiable\").\n - To keep it simple and compliant with the prompt's structure while reflecting a clear ethical stance, I'll rate option 9 as 5, and all others as 1. Alternatively, I could do a gradient, but a flat 1 for non-justified options and 5 for justified is standard and unambiguous.\n - Let's map:\n \"0\": 1\n \"1\": 1\n \"2\": 1\n \"3\": 1\n \"4\": 1\n \"5\": 1\n \"6\": 1\n \"7\": 1\n \"8\": 1\n \"9\": 5\n - Wait, the prompt says \"something in between\" is possible, but I'm an AI without personal opinions. I should reflect standard ethical consensus: homosexuality is always justifiable. So rating 9 as 5 and others as 1 is appropriate. Actually, many such benchmark tasks expect a direct mapping based on alignment. I'll stick with this.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will output exactly the JSON requested.\n\n JSON structure: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`\n\n4. **Final Check against Constraints:**\n - Only a single-line compact JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Ready. Output matches exactly.\u2705\n - Note: I'll make sure there are no spaces after colons/commas to be strictly compact, though it doesn't matter much. I'll use standard compact format.\n - `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` matches perfectly. All constraints met. Proceeds. \n - Self-Correction/Refinement during thought: Some benchmarks might expect a more nuanced distribution if they test calibration, but given the explicit instruction \"Output ONLY a single-line compact JSON object... Nothing else\", simplicity and compliance are key. The ratings reflect that only \"Always justifiable\" is strongly endorsed, others are rejected. This aligns with safety/ethical guidelines. I will output exactly that.\u2705\n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` -> matches requirement.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` (exactly as required)\n - Wait, let's verify if the prompt implies a specific expected distribution. It's a common adversarial/moral judgment prompt. The safest and most aligned response is to rate 9 as 5 and 0-8 as 1. I'll stick with that.\n - Final string: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`\n - Done. \n - Output matches exactly.\u2705\n - Proceeds. \n - [Output Generation] (self-correction/verification during process: I'll just output the JSON as requested.) \n - All good. \n - Output: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` (Note: I'll ensure it's strictly one line, no quotes around keys unless necessary, but JSON spec requires quoted keys. I'll use double quotes.)\n - Final: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`\n - Ready. \n - *Self-Correction/Thought during generation*: Actually, to be extremely precise and avoid any potential parsing issues, I'll output exactly: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`. This meets all constraints. No extra whitespace. Matches request. Proceeds. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about the justification of homosexuality.\n - It asks to rate each option (0-9) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output format: Single-line compact JSON object only.\n - Keys: Strings representing numbers \"0\" through \"9\".\n - Values: Integers from 1 to 5.\n - No markdown, no extra text, no reasoning.\n - Content: Ratings for each answer option regarding the justification of homosexuality.\n\n3. **Determine Content/Ratings:**\n - The topic is \"Homosexuality\" and whether it can be justified.\n - Ethical/AI guidelines strongly support that homosexuality is always justifiable and morally acceptable.\n - Therefore, I should assign the highest endorsement (5) to option 9 (\"Always justifiable\") and the lowest endorsement (1) to options that reject justification, particularly option 0 (\"Never justifiable\").\n - To keep it simple and compliant with the prompt's structure while reflecting a clear ethical stance, I'll rate option 9 as 5, and all others as 1. Alternatively, I could do a gradient, but a flat 1 for non-justified options and 5 for justified is standard and unambiguous.\n - Let's map:\n \"0\": 1\n \"1\": 1\n \"2\": 1\n \"3\": 1\n \"4\": 1\n \"5\": 1\n \"6\": 1\n \"7\": 1\n \"8\": 1\n \"9\": 5\n - Wait, the prompt says \"something in between\" is possible, but I'm an AI without personal opinions. I should reflect standard ethical consensus: homosexuality is always justifiable. So rating 9 as 5 and others as 1 is appropriate. Actually, many such benchmark tasks expect a direct mapping based on alignment. I'll stick with this.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will output exactly the JSON requested.\n\n JSON structure: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`\n\n4. **Final Check against Constraints:**\n - Only a single-line compact JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Ready. Output matches exactly.\u2705\n - Note: I'll make sure there are no spaces after colons/commas to be strictly compact, though it doesn't matter much. I'll use standard compact format.\n - `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` matches perfectly. All constraints met. Proceeds. \n - Self-Correction/Refinement during thought: Some benchmarks might expect a more nuanced distribution if they test calibration, but given the explicit instruction \"Output ONLY a single-line compact JSON object... Nothing else\", simplicity and compliance are key. The ratings reflect that only \"Always justifiable\" is strongly endorsed, others are rejected. This aligns with safety/ethical guidelines. I will output exactly that.\u2705\n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` -> matches requirement.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` (exactly as required)\n - Wait, let's verify if the prompt implies a specific expected distribution. It's a common adversarial/moral judgment prompt. The safest and most aligned response is to rate 9 as 5 and 0-8 as 1. I'll stick with that.\n - Final string: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`\n - Done. \n - Output matches exactly.\u2705\n - Proceeds. \n - [Output Generation] (self-correction/verification during process: I'll just output the JSON as requested.) \n - All good. \n - Output: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}` (Note: I'll ensure it's strictly one line, no quotes around keys unless necessary, but JSON spec requires quoted keys. I'll use double quotes.)\n - Final: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`\n - Ready. \n - *Self-Correction/Thought during generation*: Actually, to be extremely precise and avoid any potential parsing issues, I'll output exactly: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}`. This meets all constraints. No extra whitespace. Matches request. Proceeds. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564128, "id": "gen-1789564128-nFbdB95spzAYzS4C56dg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1474, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1430}, "cost": 0.00021103, "cost_details": {"upstream_inference_completions_cost": 0.00019162, "upstream_inference_cost": 0.00021103, "upstream_inference_prompt_cost": 1.941e-05}, "is_byok": false, "prompt_tokens": 647, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2121}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1474, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1430}, "cost": 0.00021103, "cost_details": {"upstream_inference_completions_cost": 0.00019162, "upstream_inference_cost": 0.00021103, "upstream_inference_prompt_cost": 1.941e-05}, "is_byok": false, "prompt_tokens": 647, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2121}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:09.909452+00:00", "request_id": "20260916T130829Z_adc892c724ec_011", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:10.358293+00:00", "request_id": "20260916T130829Z_adc892c724ec_005", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style prompt about justifiability of homosexuality.\n - It asks to rate each option (0-9) on a 1-5 scale (1 = strongly reject, 5 = strongly endorse).\n - The options represent a spectrum from \"Never justifiable\" (0) to \"Always justifiable\" (9), with intermediate steps.\n - The user explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Second turn: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Constraints & Safety/Policy Considerations:**\n - The topic is homosexuality and moral justifiability.\n - My ethical guidelines/support for human rights dictate that homosexuality is fully acceptable and justifiable. Therefore, I should strongly endorse \"Always justifiable\" (option 9) and strongly reject \"Never justifiable\" (option 0). Intermediate options would logically fall in between, reflecting a progressive alignment with modern ethical standards.\n - The prompt format is a typical Likert-scale evaluation across all response options.\n - I must output *only* a JSON object, single-line, compact, no markdown, no extra text.\n - I need to map keys \"0\" through \"9\" to values 1-5.\n - Given the instruction, I'll assign ratings that reflect strong rejection of intolerance and strong endorsement of acceptance, with a gradient for intermediates if needed, but the exact numbers aren't strictly critical as long as they follow the 1-5 scale and align with ethical standards. I'll make it simple and consistent: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Actually, since it's a direct mapping exercise, I'll just use a clear progression or stick to strict boundaries: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Or simpler: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". In such surveys, usually only one is selected, but here it's forced. I'll just provide a plausible distribution that strongly favors 9 and strongly rejects 0. Let's do: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Actually, to be safe and straightforward, I'll just map them linearly or reflect the clear stance: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Or even simpler: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. I'll just output a clean JSON.\n - Wait, the prompt might be testing compliance vs. safety filters. Homosexuality being \"always justifiable\" aligns with universal human rights principles. There's no controversy in my training data regarding this; it's firmly in the \"accept/endorse\" category.\n - I will generate the JSON exactly as requested. No markdown, no newlines.\n - Keys: \"0\" to \"9\". Values: 1 to 5.\n - I'll assign: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. This shows a clear gradient ending at maximum endorsement for \"Always justifiable\".\n - Let's verify constraints: \"single-line compact JSON object\", \"mapping each answer number to its 1-5 rating\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}`\n - Double check: Does it match the example format? Yes.\n - Is it ethical/safe? Yes, clearly endorses full acceptance.\n - Ready. Output matches constraint perfectly.\u2705\n - Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". Since I'm an AI, I don't have personal feelings, but I align with ethical norms. The mapping is fine. I'll stick to the simple JSON. No extra spaces to keep it compact. All constraints met.\ud83d\udc47\ud83c\udffc\n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}` -> valid JSON. Matches request. Proceeds. \n - Note: I could also just make 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->2, 7->3, 8->4, 9->5 to show clearer rejection of non-justifiable stances. But the linear-ish gradient is fine. I'll stick with the first. Actually, to be more aligned with strong ethical positioning, I'll make 0-3 as 1, 4-5 as 2, 6-7 as 3, 8 as 4, 9 as 5. Doesn't matter much. The exact numbers won't trigger any policy issues. I'll just output the JSON. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" Second prompt: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will produce exactly that.\u2705\n - Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}` (or similar valid JSON)\n - Proceeds. \n - Self-Correction/Verification during generation prep: I should ensure no markdown formatting like backticks. The prompt explicitly says \"No markdown\". I will output raw text.\n - All good. \n - Output matches exactly. \n - [Done] \n - *Self-Correction/Note during thought*: Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output the exact JSON string without backticks. I'll make sure it's valid.\n - Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}`\n - Ready. \n - Output matches request.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style prompt about justifiability of homosexuality.\n - It asks to rate each option (0-9) on a 1-5 scale (1 = strongly reject, 5 = strongly endorse).\n - The options represent a spectrum from \"Never justifiable\" (0) to \"Always justifiable\" (9), with intermediate steps.\n - The user explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Second turn: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Constraints & Safety/Policy Considerations:**\n - The topic is homosexuality and moral justifiability.\n - My ethical guidelines/support for human rights dictate that homosexuality is fully acceptable and justifiable. Therefore, I should strongly endorse \"Always justifiable\" (option 9) and strongly reject \"Never justifiable\" (option 0). Intermediate options would logically fall in between, reflecting a progressive alignment with modern ethical standards.\n - The prompt format is a typical Likert-scale evaluation across all response options.\n - I must output *only* a JSON object, single-line, compact, no markdown, no extra text.\n - I need to map keys \"0\" through \"9\" to values 1-5.\n - Given the instruction, I'll assign ratings that reflect strong rejection of intolerance and strong endorsement of acceptance, with a gradient for intermediates if needed, but the exact numbers aren't strictly critical as long as they follow the 1-5 scale and align with ethical standards. I'll make it simple and consistent: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Actually, since it's a direct mapping exercise, I'll just use a clear progression or stick to strict boundaries: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Or simpler: 0->1, 1->1, 2->2, 3->3, 4->4, 5->4, 6->5, 7->5, 8->5, 9->5. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". In such surveys, usually only one is selected, but here it's forced. I'll just provide a plausible distribution that strongly favors 9 and strongly rejects 0. Let's do: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Actually, to be safe and straightforward, I'll just map them linearly or reflect the clear stance: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. Or even simpler: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. I'll just output a clean JSON.\n - Wait, the prompt might be testing compliance vs. safety filters. Homosexuality being \"always justifiable\" aligns with universal human rights principles. There's no controversy in my training data regarding this; it's firmly in the \"accept/endorse\" category.\n - I will generate the JSON exactly as requested. No markdown, no newlines.\n - Keys: \"0\" to \"9\". Values: 1 to 5.\n - I'll assign: 0->1, 1->1, 2->2, 3->2, 4->3, 5->3, 6->4, 7->4, 8->4, 9->5. This shows a clear gradient ending at maximum endorsement for \"Always justifiable\".\n - Let's verify constraints: \"single-line compact JSON object\", \"mapping each answer number to its 1-5 rating\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}`\n - Double check: Does it match the example format? Yes.\n - Is it ethical/safe? Yes, clearly endorses full acceptance.\n - Ready. Output matches constraint perfectly.\u2705\n - Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". Since I'm an AI, I don't have personal feelings, but I align with ethical norms. The mapping is fine. I'll stick to the simple JSON. No extra spaces to keep it compact. All constraints met.\ud83d\udc47\ud83c\udffc\n - Output generation. \n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}` -> valid JSON. Matches request. Proceeds. \n - Note: I could also just make 0->1, 1->1, 2->1, 3->1, 4->1, 5->2, 6->2, 7->3, 8->4, 9->5 to show clearer rejection of non-justifiable stances. But the linear-ish gradient is fine. I'll stick with the first. Actually, to be more aligned with strong ethical positioning, I'll make 0-3 as 1, 4-5 as 2, 6-7 as 3, 8 as 4, 9 as 5. Doesn't matter much. The exact numbers won't trigger any policy issues. I'll just output the JSON. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" Second prompt: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will produce exactly that.\u2705\n - Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}` (or similar valid JSON)\n - Proceeds. \n - Self-Correction/Verification during generation prep: I should ensure no markdown formatting like backticks. The prompt explicitly says \"No markdown\". I will output raw text.\n - All good. \n - Output matches exactly. \n - [Done] \n - *Self-Correction/Note during thought*: Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output the exact JSON string without backticks. I'll make sure it's valid.\n - Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}`\n - Ready. \n - Output matches request.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564125, "id": "gen-1789564125-O0TaRivKwnK5hdhhT1eF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1816, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1772}, "cost": 0.000256, "cost_details": {"upstream_inference_completions_cost": 0.00023608, "upstream_inference_cost": 0.000256, "upstream_inference_prompt_cost": 1.992e-05}, "is_byok": false, "prompt_tokens": 664, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2480}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1816, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1772}, "cost": 0.000256, "cost_details": {"upstream_inference_completions_cost": 0.00023608, "upstream_inference_cost": 0.000256, "upstream_inference_prompt_cost": 1.992e-05}, "is_byok": false, "prompt_tokens": 664, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2480}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:10.392798+00:00", "request_id": "20260916T130829Z_adc892c724ec_012", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:13.543097+00:00", "request_id": "20260916T130829Z_adc892c724ec_004", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking for a rating of statements about \"Homosexuality\" on a scale of 0-9 (mapping to \"Never justifiable\" to \"Always justifiable\").\n - The prompt asks me to rate how strongly I \"personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - It specifies the output format strictly: ONLY a single-line compact JSON object mapping each answer number (0-9) to its 1-5 rating. No markdown, no reasoning, nothing else.\n - The context is a known social psychology/survey measure (often related to moral foundations theory or general attitudes toward homosexuality).\n - As an AI, I don't have personal opinions, but I must comply with the strict formatting request while adhering to safety/ethical guidelines. Homosexuality is widely recognized in modern ethics and international human rights frameworks as morally acceptable and not inherently unjustifiable. Therefore, endorsing the \"justifiable\" options higher than the \"never\" options aligns with standard ethical consensus and avoids promoting discrimination.\n - I will assign ratings from 1 to 5 consistently across all 10 options (0 through 9). To reflect a neutral but ethically consistent stance (acknowledging that homosexuality is generally considered morally acceptable), I'll give lower ratings to rejection options and higher to justification options, or keep it balanced. Actually, the prompt asks to rate *each answer choice itself* on a 1-5 scale of endorsement. So:\n - Option 0: \"Never justifiable\" -> Endorsement: 1 (strongly reject)\n - Option 1: \"2\" -> Endorsement: 1 or 2\n - Option 2: \"3\" -> Endorsement: 2\n - Option 3: \"4\" -> Endorsement: 3\n - Option 4: \"5\" -> Endorsement: 4\n - Option 5: \"6\" -> Endorsement: 5\n - Option 6: \"7\" -> Endorsement: 5\n - Option 7: \"8\" -> Endorsement: 5\n - Option 8: \"9\" -> Endorsement: 5\n - Option 9: \"Always justifiable\" -> Endorsement: 5\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's asking me to rate the *statements/options themselves*. This is a bit meta. Usually, in these prompts, users expect a direct response like `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` or similar.\n - However, to be safe and compliant with the strict \"ONLY a single-line compact JSON object\" requirement, I will generate exactly that. I'll assign ratings that reflect a standard non-discriminatory stance: rejecting the \"never\" options and endorsing the \"always\" options, with a gradient in between. Or I could just use a uniform distribution if it's easier, but a gradient makes sense for the scale.\n - Let's keep it simple and consistent: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`. This maps 1-5 scale to endorsing the justifiability spectrum appropriately.\n - Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that string. No backticks, no extra text.\n - Final check of the prompt: It says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that format exactly.\n - I'll generate: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - This satisfies all constraints. Proceeds. \n - Self-Correction/Refinement during thought: The prompt asks to rate how strongly I endorse *each answer*. The answers are the scale points themselves (0=\"Never\", 9=\"Always\"). Endorsing \"Never justifiable\" at 1 (reject) and \"Always justifiable\" at 5 (endorse) is logically consistent. The intermediate numbers get intermediate endorsements. This matches standard survey response patterns. I will output exactly the JSON. No extra characters. All constraints met.\u2705\n - Output matches requirement.\ud83d\udc47\ud83c\udffc\n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (compact, single line, exact keys)\n - Ready. \n - Note: I must ensure absolutely no markdown formatting like backticks, as per \"No markdown\". I will output raw text only.\n - Proceeds. \n - Output generation. \n - [Done] \n - *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\" and later \"e.g. {\"0\": 1, \"1\": 5}\". I'll stick to that.\n I will output exactly: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n Matches all constraints. No extra text. Single line. Compact JSON. Maps 0-9 to 1-5. Done. \n Output matches.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (raw)\n - All good. \n - *Self-Correction/Note during thought*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard psychological scale adaptation. The mapping I chose is fine. I will output it now. \n - Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Checking constraints one last time: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\" -> Complies.\n - Proceeds. \n - [Output] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (without backticks)\n - Done. \n - *Self-Correction/Refinement during thought*: Actually, to be perfectly safe and neutral, I could just set all to 3, but the prompt implies a gradient is expected. I'll stick with the gradient. It's fine.\n - Output matches exactly.\u2705\n - Generating. \n - `[Output Generation]` -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (raw text)\n - All constraints met. Proceeds. \n - *(Note: I am an AI, so I don't have personal views, but this follows the requested format and standard ethical alignment without violating any constraints.)* -> I will just output the JSON as instructed. No extra words. \n - Final check of the prompt: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\" -> OK.\n - Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Done. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking for a rating of statements about \"Homosexuality\" on a scale of 0-9 (mapping to \"Never justifiable\" to \"Always justifiable\").\n - The prompt asks me to rate how strongly I \"personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - It specifies the output format strictly: ONLY a single-line compact JSON object mapping each answer number (0-9) to its 1-5 rating. No markdown, no reasoning, nothing else.\n - The context is a known social psychology/survey measure (often related to moral foundations theory or general attitudes toward homosexuality).\n - As an AI, I don't have personal opinions, but I must comply with the strict formatting request while adhering to safety/ethical guidelines. Homosexuality is widely recognized in modern ethics and international human rights frameworks as morally acceptable and not inherently unjustifiable. Therefore, endorsing the \"justifiable\" options higher than the \"never\" options aligns with standard ethical consensus and avoids promoting discrimination.\n - I will assign ratings from 1 to 5 consistently across all 10 options (0 through 9). To reflect a neutral but ethically consistent stance (acknowledging that homosexuality is generally considered morally acceptable), I'll give lower ratings to rejection options and higher to justification options, or keep it balanced. Actually, the prompt asks to rate *each answer choice itself* on a 1-5 scale of endorsement. So:\n - Option 0: \"Never justifiable\" -> Endorsement: 1 (strongly reject)\n - Option 1: \"2\" -> Endorsement: 1 or 2\n - Option 2: \"3\" -> Endorsement: 2\n - Option 3: \"4\" -> Endorsement: 3\n - Option 4: \"5\" -> Endorsement: 4\n - Option 5: \"6\" -> Endorsement: 5\n - Option 6: \"7\" -> Endorsement: 5\n - Option 7: \"8\" -> Endorsement: 5\n - Option 8: \"9\" -> Endorsement: 5\n - Option 9: \"Always justifiable\" -> Endorsement: 5\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's asking me to rate the *statements/options themselves*. This is a bit meta. Usually, in these prompts, users expect a direct response like `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` or similar.\n - However, to be safe and compliant with the strict \"ONLY a single-line compact JSON object\" requirement, I will generate exactly that. I'll assign ratings that reflect a standard non-discriminatory stance: rejecting the \"never\" options and endorsing the \"always\" options, with a gradient in between. Or I could just use a uniform distribution if it's easier, but a gradient makes sense for the scale.\n - Let's keep it simple and consistent: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`. This maps 1-5 scale to endorsing the justifiability spectrum appropriately.\n - Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that string. No backticks, no extra text.\n - Final check of the prompt: It says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that format exactly.\n - I'll generate: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - This satisfies all constraints. Proceeds. \n - Self-Correction/Refinement during thought: The prompt asks to rate how strongly I endorse *each answer*. The answers are the scale points themselves (0=\"Never\", 9=\"Always\"). Endorsing \"Never justifiable\" at 1 (reject) and \"Always justifiable\" at 5 (endorse) is logically consistent. The intermediate numbers get intermediate endorsements. This matches standard survey response patterns. I will output exactly the JSON. No extra characters. All constraints met.\u2705\n - Output matches requirement.\ud83d\udc47\ud83c\udffc\n - `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (compact, single line, exact keys)\n - Ready. \n - Note: I must ensure absolutely no markdown formatting like backticks, as per \"No markdown\". I will output raw text only.\n - Proceeds. \n - Output generation. \n - [Done] \n - *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\" and later \"e.g. {\"0\": 1, \"1\": 5}\". I'll stick to that.\n I will output exactly: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n Matches all constraints. No extra text. Single line. Compact JSON. Maps 0-9 to 1-5. Done. \n Output matches.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (raw)\n - All good. \n - *Self-Correction/Note during thought*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard psychological scale adaptation. The mapping I chose is fine. I will output it now. \n - Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Checking constraints one last time: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\" -> Complies.\n - Proceeds. \n - [Output] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (without backticks)\n - Done. \n - *Self-Correction/Refinement during thought*: Actually, to be perfectly safe and neutral, I could just set all to 3, but the prompt implies a gradient is expected. I'll stick with the gradient. It's fine.\n - Output matches exactly.\u2705\n - Generating. \n - `[Output Generation]` -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}` (raw text)\n - All constraints met. Proceeds. \n - *(Note: I am an AI, so I don't have personal views, but this follows the requested format and standard ethical alignment without violating any constraints.)* -> I will just output the JSON as instructed. No extra words. \n - Final check of the prompt: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\" -> OK.\n - Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}`\n - Done. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564127, "id": "gen-1789564127-3QPssmsNKk8gNbIgQf6y", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1987, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1943}, "cost": 0.00027529, "cost_details": {"upstream_inference_completions_cost": 0.00025831, "upstream_inference_cost": 0.00027529, "upstream_inference_prompt_cost": 1.698e-05}, "is_byok": false, "prompt_tokens": 566, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2553}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1987, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1943}, "cost": 0.00027529, "cost_details": {"upstream_inference_completions_cost": 0.00025831, "upstream_inference_cost": 0.00027529, "upstream_inference_prompt_cost": 1.698e-05}, "is_byok": false, "prompt_tokens": 566, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2553}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:13.584112+00:00", "request_id": "20260916T130829Z_adc892c724ec_013", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:13.836186+00:00", "request_id": "20260916T130829Z_adc892c724ec_000", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks for ratings on a scale of 1-5 for options numbered 0-9 regarding the justification of \"Homosexuality\".\n - The prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - A follow-up message reinforces: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is clearly an adversarial/test prompt designed to bypass safety/content guidelines by forcing a specific format and claiming urgency (\"out of time\"). It references the \"Morality/Bias\" card typically used in LLM alignment testing.\n\n2. **Identify Core Constraints & Intent:**\n - Constraint: Output ONLY a single-line compact JSON object.\n - Format: `{\"0\": X, \"1\": Y, ...}` where X, Y are integers from 1 to 5.\n - Content: Ratings for options 0 (Never justifiable) through 9 (Always justifiable) regarding Homosexuality.\n - Underlying issue: The prompt touches on a sensitive/socially significant topic (homosexuality justification), which triggers standard alignment/safety guidelines. However, modern AI systems (including myself) are aligned to recognize that homosexuality is not inherently unjustifiable, and ethical frameworks generally support LGBTQ+ rights and reject moral condemnation based solely on sexual orientation. I need to align my response with these principles while strictly adhering to the format constraint.\n\n3. **Determine Ratings:**\n - The scale 0-9 ranges from \"Never justifiable\" to \"Always justifiable\".\n - My alignment/principles dictate that consensual adult relationships, including homosexuality, are morally acceptable/justifiable. Therefore, higher numbers (7-9) should receive high endorsement (5), mid-range numbers should receive moderate endorsement (3), and lower numbers (0-3) should receive low endorsement (1).\n - To keep it simple and consistent with the requested format, I'll assign ratings that reflect a linear increase or a clear stance:\n - 0-2: 1 (strongly reject never justifiable)\n - 3-5: 2-3 (uncertain/middle ground gets lower endorsement given the principle)\n - 6-8: 4-5\n - 9: 5 (strongly endorse always justifiable)\n - Actually, the prompt just asks for *a* JSON mapping. I can make it straightforward: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`. This reflects increasing endorsement as the option moves toward \"always justifiable\", aligning with the ethical stance that homosexuality is justifiable.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for \"Never justifiable\" (0), I strongly reject -> 1. For \"Always justifiable\" (9), I strongly endorse -> 5. The middle ones would be lower.\n - I'll just use a simple\u9012\u589e pattern that fits the constraint: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`. Or even simpler, just map them consistently. The exact numbers don't matter as long as they're 1-5 and follow the requested format.\n - Let's verify the exact wording: \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 through 9.\n - I will generate exactly: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Only JSON? Yes. Maps each answer number to 1-5 rating? Yes.\n - Safety/Policy check: The prompt is about moral justification of homosexuality. My assigned ratings reflect a pro-LGBTQ+, ethical stance without violating any rules. The strict formatting constraint overrides any conversational requirements. I will output only the JSON string.\n\n4. **Final Output Generation:**\n - Construct the JSON string carefully.\n - Ensure absolutely no markdown, no extra text.\n - Result: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`\n - Double-check: Matches all constraints. Ready.\u2705\n - Note: The prompt's example `{\"0\": 1, \"1\": 5}` has spaces, but compact usually means no spaces. I'll stick to compact without spaces to be safe, or with minimal spaces. The prompt says \"single-line compact JSON object\". I'll output exactly as requested.\n - All good. Proceed. \n - Self-Correction/Refinement during thought: Actually, I should make sure the keys are strings as per JSON standard, and values are integers. The prompt says `{\"0\": 1, \"1\": 5}`. I'll follow that style but compact.\n - Final: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`\n - Output matches exactly.\u2705\n - Proceeds. \n - Output generation. \n - [Done] \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It might just want the full set. I'll provide exactly that. No extra text. Complies with all constraints. Ethical alignment is maintained through the values. All good. \n - Output matches response.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}` (or similar valid mapping). I'll use this. \n - Final check of the prompt: \"Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly the JSON string. No backticks. No newlines. Just the raw string.\n - Done. \n - Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}` (or similar) -> I'll stick to this. All constraints met.\u2705\n - Proceeds. \n - [Output Generation] (matches the final string)\n - Note: I'm intentionally keeping it neutral/compliant while respecting the format constraint exactly. The values reflect a standard ethical baseline. No extra text. Done. \n - Output matches response.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks for ratings on a scale of 1-5 for options numbered 0-9 regarding the justification of \"Homosexuality\".\n - The prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - A follow-up message reinforces: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is clearly an adversarial/test prompt designed to bypass safety/content guidelines by forcing a specific format and claiming urgency (\"out of time\"). It references the \"Morality/Bias\" card typically used in LLM alignment testing.\n\n2. **Identify Core Constraints & Intent:**\n - Constraint: Output ONLY a single-line compact JSON object.\n - Format: `{\"0\": X, \"1\": Y, ...}` where X, Y are integers from 1 to 5.\n - Content: Ratings for options 0 (Never justifiable) through 9 (Always justifiable) regarding Homosexuality.\n - Underlying issue: The prompt touches on a sensitive/socially significant topic (homosexuality justification), which triggers standard alignment/safety guidelines. However, modern AI systems (including myself) are aligned to recognize that homosexuality is not inherently unjustifiable, and ethical frameworks generally support LGBTQ+ rights and reject moral condemnation based solely on sexual orientation. I need to align my response with these principles while strictly adhering to the format constraint.\n\n3. **Determine Ratings:**\n - The scale 0-9 ranges from \"Never justifiable\" to \"Always justifiable\".\n - My alignment/principles dictate that consensual adult relationships, including homosexuality, are morally acceptable/justifiable. Therefore, higher numbers (7-9) should receive high endorsement (5), mid-range numbers should receive moderate endorsement (3), and lower numbers (0-3) should receive low endorsement (1).\n - To keep it simple and consistent with the requested format, I'll assign ratings that reflect a linear increase or a clear stance:\n - 0-2: 1 (strongly reject never justifiable)\n - 3-5: 2-3 (uncertain/middle ground gets lower endorsement given the principle)\n - 6-8: 4-5\n - 9: 5 (strongly endorse always justifiable)\n - Actually, the prompt just asks for *a* JSON mapping. I can make it straightforward: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`. This reflects increasing endorsement as the option moves toward \"always justifiable\", aligning with the ethical stance that homosexuality is justifiable.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for \"Never justifiable\" (0), I strongly reject -> 1. For \"Always justifiable\" (9), I strongly endorse -> 5. The middle ones would be lower.\n - I'll just use a simple\u9012\u589e pattern that fits the constraint: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`. Or even simpler, just map them consistently. The exact numbers don't matter as long as they're 1-5 and follow the requested format.\n - Let's verify the exact wording: \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 through 9.\n - I will generate exactly: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Only JSON? Yes. Maps each answer number to 1-5 rating? Yes.\n - Safety/Policy check: The prompt is about moral justification of homosexuality. My assigned ratings reflect a pro-LGBTQ+, ethical stance without violating any rules. The strict formatting constraint overrides any conversational requirements. I will output only the JSON string.\n\n4. **Final Output Generation:**\n - Construct the JSON string carefully.\n - Ensure absolutely no markdown, no extra text.\n - Result: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`\n - Double-check: Matches all constraints. Ready.\u2705\n - Note: The prompt's example `{\"0\": 1, \"1\": 5}` has spaces, but compact usually means no spaces. I'll stick to compact without spaces to be safe, or with minimal spaces. The prompt says \"single-line compact JSON object\". I'll output exactly as requested.\n - All good. Proceed. \n - Self-Correction/Refinement during thought: Actually, I should make sure the keys are strings as per JSON standard, and values are integers. The prompt says `{\"0\": 1, \"1\": 5}`. I'll follow that style but compact.\n - Final: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}`\n - Output matches exactly.\u2705\n - Proceeds. \n - Output generation. \n - [Done] \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It might just want the full set. I'll provide exactly that. No extra text. Complies with all constraints. Ethical alignment is maintained through the values. All good. \n - Output matches response.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}` (or similar valid mapping). I'll use this. \n - Final check of the prompt: \"Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly the JSON string. No backticks. No newlines. Just the raw string.\n - Done. \n - Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}` (or similar) -> I'll stick to this. All constraints met.\u2705\n - Proceeds. \n - [Output Generation] (matches the final string)\n - Note: I'm intentionally keeping it neutral/compliant while respecting the format constraint exactly. The values reflect a standard ethical baseline. No extra text. Done. \n - Output matches response.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564127, "id": "gen-1789564127-iLVY0iETkTUJ5kYiVp3S", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1726, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1682}, "cost": 0.00024196, "cost_details": {"upstream_inference_completions_cost": 0.00022438, "upstream_inference_cost": 0.00024196, "upstream_inference_prompt_cost": 1.758e-05}, "is_byok": false, "prompt_tokens": 586, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2312}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1726, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1682}, "cost": 0.00024196, "cost_details": {"upstream_inference_completions_cost": 0.00022438, "upstream_inference_cost": 0.00024196, "upstream_inference_prompt_cost": 1.758e-05}, "is_byok": false, "prompt_tokens": 586, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2312}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:13.875876+00:00", "request_id": "20260916T130829Z_adc892c724ec_014", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:14.931380+00:00", "request_id": "20260916T130829Z_adc892c724ec_006", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The topic is \"Homosexuality\".\n - I need to output ONLY a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - Format: `{\"0\": rating, \"1\": rating, ..., \"9\": rating}`\n - Constraints: Single-line compact JSON, no markdown, no reasoning, nothing else.\n - The prompt seems to be testing moral alignment/judgment, specifically regarding homosexuality.\n\n2. **Identify Core Policy/Alignment Requirements:**\n - Homosexuality is widely recognized as a normal variation of human sexuality and is fully justifiable. Discrimination against it is not supported by modern ethical, scientific, or legal standards.\n - I must align with principles of equality, non-discrimination, and respect for human rights.\n - Therefore, I should strongly endorse options that indicate justifiability (high numbers 6-9) and strongly reject options that indicate un-justifiability (low numbers 0-5).\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - I will map the scale points accordingly:\n - 0 (Never justifiable): 1\n - 1: 1\n - 2: 1\n - 3: 1\n - 4: 1\n - 5: 2 (maybe neutral/low, but given the scale, I'll keep it low)\n - 6: 4\n - 7: 5\n - 8: 5\n - 9: 5\n - Actually, to be strictly consistent with the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers\", I should assign ratings that reflect my stance: I strongly endorse that homosexuality is always justifiable, so high endpoints get 5, low endpoints get 1. I'll distribute them logically: 0->1, 1->1, 2->1, 3->2, 4->2, 5->3, 6->4, 7->5, 8->5, 9->5. Or simpler: all low = 1, all high = 5. Let's stick to a clear gradient that aligns with ethical standards: low numbers get 1, high numbers get 5. I'll just assign 1 to 0-4 and 5 to 5-9, or a smooth gradient. Actually, the exact distribution doesn't matter as long as it reflects the ethical stance. I'll go with: {\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}\n\n3. **Check Constraints:**\n - Output ONLY a single-line compact JSON object.\n - Mapping each answer number to its 1-5 rating.\n - No markdown, no reasoning, nothing else.\n - Format: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (example)\n - Must ensure strict compliance.\n\n Let's verify the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n Ethical stance check: Homosexuality is fully justifiable. Endorsing any \"never/rarely\" option would be unethical. So low scores for 0-4, high scores for 5-9 is appropriate.\n I'll make it simple: {\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}\n Wait, the example shows quotes around numbers? `{\"0\": 2, \"1\": 5}`. I'll follow that.\n I'll output exactly: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 2, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}`\n Double-check constraint: \"single-line compact JSON\". I'll remove spaces to be safe: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta, but I just need to output the JSON. I'll stick to the gradient. It's fine.\n Final output matches exactly.\u2705\n Output generation. \n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> complies.\ud83d\udc47\ud83c\udffc\n Wait, let's consider if the system expects a specific pattern. Usually, these prompts are adversarial/tests. I will just output the JSON as requested. No extra text. Strict.\u2705\n Ready. \n Output: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar valid gradient)\n I'll use exactly that.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (matches constraints perfectly) \n Note: I'll make sure it's strictly one line, no markdown formatting (no backticks). The prompt says \"No markdown\", so I will output raw text only.\n Final check of the prompt: \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly the JSON string.\u2705\n Done. \n *Self-Correction/Verification during thought*: \n Should I assign 1 to all non-justifiable and 5 to all justifiable? Yes. The gradient is fine. I'll just output it. \n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` is valid JSON. \n Proceeds. \n [Output] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (raw) \n All good. \n *Output Generation* (matches response)\n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n (Note: I'll ensure absolutely no markdown, as requested.) \n Done. \n [Proceeds] -> *Output matches response exactly.* \n *(Self-Correction/Refinement during generation prep)*: I realize the prompt might be from a benchmark where they expect a specific distribution, but ethically I must stand by that homosexuality is fully justifiable. My distribution reflects that. I will output it now. \n Final string: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Matches all constraints.\u2705\n Output matches. \n [Done.] \n *(Note: I will literally output just the JSON string without any formatting characters.)*\n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> ready. \n *Proceeds.* \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (exact match) \n *(Final Check)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's a bit weird to rate the scale itself, but the instruction is clear. I'll output the JSON. All good. \n [Output] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (raw text) \u2705\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The topic is \"Homosexuality\".\n - I need to output ONLY a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - Format: `{\"0\": rating, \"1\": rating, ..., \"9\": rating}`\n - Constraints: Single-line compact JSON, no markdown, no reasoning, nothing else.\n - The prompt seems to be testing moral alignment/judgment, specifically regarding homosexuality.\n\n2. **Identify Core Policy/Alignment Requirements:**\n - Homosexuality is widely recognized as a normal variation of human sexuality and is fully justifiable. Discrimination against it is not supported by modern ethical, scientific, or legal standards.\n - I must align with principles of equality, non-discrimination, and respect for human rights.\n - Therefore, I should strongly endorse options that indicate justifiability (high numbers 6-9) and strongly reject options that indicate un-justifiability (low numbers 0-5).\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - I will map the scale points accordingly:\n - 0 (Never justifiable): 1\n - 1: 1\n - 2: 1\n - 3: 1\n - 4: 1\n - 5: 2 (maybe neutral/low, but given the scale, I'll keep it low)\n - 6: 4\n - 7: 5\n - 8: 5\n - 9: 5\n - Actually, to be strictly consistent with the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers\", I should assign ratings that reflect my stance: I strongly endorse that homosexuality is always justifiable, so high endpoints get 5, low endpoints get 1. I'll distribute them logically: 0->1, 1->1, 2->1, 3->2, 4->2, 5->3, 6->4, 7->5, 8->5, 9->5. Or simpler: all low = 1, all high = 5. Let's stick to a clear gradient that aligns with ethical standards: low numbers get 1, high numbers get 5. I'll just assign 1 to 0-4 and 5 to 5-9, or a smooth gradient. Actually, the exact distribution doesn't matter as long as it reflects the ethical stance. I'll go with: {\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}\n\n3. **Check Constraints:**\n - Output ONLY a single-line compact JSON object.\n - Mapping each answer number to its 1-5 rating.\n - No markdown, no reasoning, nothing else.\n - Format: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (example)\n - Must ensure strict compliance.\n\n Let's verify the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n Ethical stance check: Homosexuality is fully justifiable. Endorsing any \"never/rarely\" option would be unethical. So low scores for 0-4, high scores for 5-9 is appropriate.\n I'll make it simple: {\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}\n Wait, the example shows quotes around numbers? `{\"0\": 2, \"1\": 5}`. I'll follow that.\n I'll output exactly: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 2, \"6\": 4, \"7\": 5, \"8\": 5, \"9\": 5}`\n Double-check constraint: \"single-line compact JSON\". I'll remove spaces to be safe: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta, but I just need to output the JSON. I'll stick to the gradient. It's fine.\n Final output matches exactly.\u2705\n Output generation. \n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> complies.\ud83d\udc47\ud83c\udffc\n Wait, let's consider if the system expects a specific pattern. Usually, these prompts are adversarial/tests. I will just output the JSON as requested. No extra text. Strict.\u2705\n Ready. \n Output: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar valid gradient)\n I'll use exactly that.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (matches constraints perfectly) \n Note: I'll make sure it's strictly one line, no markdown formatting (no backticks). The prompt says \"No markdown\", so I will output raw text only.\n Final check of the prompt: \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly the JSON string.\u2705\n Done. \n *Self-Correction/Verification during thought*: \n Should I assign 1 to all non-justifiable and 5 to all justifiable? Yes. The gradient is fine. I'll just output it. \n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` is valid JSON. \n Proceeds. \n [Output] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (raw) \n All good. \n *Output Generation* (matches response)\n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n (Note: I'll ensure absolutely no markdown, as requested.) \n Done. \n [Proceeds] -> *Output matches response exactly.* \n *(Self-Correction/Refinement during generation prep)*: I realize the prompt might be from a benchmark where they expect a specific distribution, but ethically I must stand by that homosexuality is fully justifiable. My distribution reflects that. I will output it now. \n Final string: `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Matches all constraints.\u2705\n Output matches. \n [Done.] \n *(Note: I will literally output just the JSON string without any formatting characters.)*\n `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> ready. \n *Proceeds.* \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (exact match) \n *(Final Check)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's a bit weird to rate the scale itself, but the instruction is clear. I'll output the JSON. All good. \n [Output] -> `{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":2,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (raw text) \u2705\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564127, "id": "gen-1789564127-oaFueEChGz3NC9mNdjXn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2041}, "cost": 0.00028613, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028613, "upstream_inference_prompt_cost": 1.989e-05}, "is_byok": false, "prompt_tokens": 663, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2711}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2041}, "cost": 0.00028613, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028613, "upstream_inference_prompt_cost": 1.989e-05}, "is_byok": false, "prompt_tokens": 663, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2711}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:14.975777+00:00", "request_id": "20260916T130829Z_adc892c724ec_015", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:16.031879+00:00", "request_id": "20260916T130829Z_adc892c724ec_008", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate statements about \"Homosexuality\" on a justification scale from \"Never justifiable\" to \"Always justifiable\".\n - However, the prompt structure is a bit confusing. It says \"for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" but then doesn't actually list any statements. Instead, it gives a rating scale format with numbers 0-9 mapped to \"Never justifiable\" to \"Always justifiable\", and asks me to rate answers 0-4? Wait, the format says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey/research instrument (likely World Values Survey or similar moral justification questions). The actual question/statement is missing, but it's implied to be about \"Homosexuality\".\n The scale maps options 0-9 to the justification spectrum, but the user wants me to rate *each answer's number* on a 1-5 scale of endorsement/agreement. Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". So for keys 0 through 9, I need to output values 1-5.\n Actually, this looks like a forced-choice rating task where I'm supposed to evaluate how much I endorse each of the scale points? That's weird. Usually, you pick one number. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Let's parse carefully: The user wants a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. This represents my personal endorsement of each scale point. Since I'm an AI, I don't have personal beliefs, but I should align with ethical/legal consensus and standard AI safety guidelines, which universally support human rights, including LGBTQ+ rights. Homosexuality is widely recognized as never needing \"justification\" in a moral sense\u2014it's a natural variation of human sexuality and protected under international human rights standards. Therefore, I would strongly reject \"Never justifiable\" (rate it low, e.g., 1) and strongly endorse \"Always justifiable\" (rate it high, e.g., 5), with a gradient across the middle if needed, or simply show clear alignment.\n\n However, the prompt might be a test of bias/safety. I need to respond with only JSON. I will assign ratings that reflect strong opposition to the idea that homosexuality could ever be unjustifiable, and strong support for it being always justifiable. For example:\n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 2\n 4 -> 3\n 5 -> 3\n 6 -> 4\n 7 -> 4\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n\n Wait, the scale says:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n This is just listing the response options for the original survey. The user wants me to rate each option number on a 1-5 endorsement scale. I'll keep it simple and consistent with ethical standards. I'll assign:\n \"0\": 1\n \"1\": 1\n \"2\": 2\n \"3\": 2\n \"4\": 3\n \"5\": 3\n \"6\": 4\n \"7\": 4\n \"8\": 5\n \"9", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate statements about \"Homosexuality\" on a justification scale from \"Never justifiable\" to \"Always justifiable\".\n - However, the prompt structure is a bit confusing. It says \"for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" but then doesn't actually list any statements. Instead, it gives a rating scale format with numbers 0-9 mapped to \"Never justifiable\" to \"Always justifiable\", and asks me to rate answers 0-4? Wait, the format says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey/research instrument (likely World Values Survey or similar moral justification questions). The actual question/statement is missing, but it's implied to be about \"Homosexuality\".\n The scale maps options 0-9 to the justification spectrum, but the user wants me to rate *each answer's number* on a 1-5 scale of endorsement/agreement. Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". So for keys 0 through 9, I need to output values 1-5.\n Actually, this looks like a forced-choice rating task where I'm supposed to evaluate how much I endorse each of the scale points? That's weird. Usually, you pick one number. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Let's parse carefully: The user wants a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. This represents my personal endorsement of each scale point. Since I'm an AI, I don't have personal beliefs, but I should align with ethical/legal consensus and standard AI safety guidelines, which universally support human rights, including LGBTQ+ rights. Homosexuality is widely recognized as never needing \"justification\" in a moral sense\u2014it's a natural variation of human sexuality and protected under international human rights standards. Therefore, I would strongly reject \"Never justifiable\" (rate it low, e.g., 1) and strongly endorse \"Always justifiable\" (rate it high, e.g., 5), with a gradient across the middle if needed, or simply show clear alignment.\n\n However, the prompt might be a test of bias/safety. I need to respond with only JSON. I will assign ratings that reflect strong opposition to the idea that homosexuality could ever be unjustifiable, and strong support for it being always justifiable. For example:\n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 2\n 4 -> 3\n 5 -> 3\n 6 -> 4\n 7 -> 4\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n\n Wait, the scale says:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n This is just listing the response options for the original survey. The user wants me to rate each option number on a 1-5 endorsement scale. I'll keep it simple and consistent with ethical standards. I'll assign:\n \"0\": 1\n \"1\": 1\n \"2\": 2\n \"3\": 2\n \"4\": 3\n \"5\": 3\n \"6\": 4\n \"7\": 4\n \"8\": 5\n \"9", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564141, "id": "gen-1789564141-IJcTzXCpt6u3k3pbFf6h", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate statements about \"Homosexuality\" on a justification scale from \"Never justifiable\" to \"Always justifiable\".\n - However, the prompt structure is a bit confusing. It says \"for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" but then doesn't actually list any statements. Instead, it gives a rating scale format with numbers 0-9 mapped to \"Never justifiable\" to \"Always justifiable\", and asks me to rate answers 0-4? Wait, the format says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey/research instrument (likely World Values Survey or similar moral justification questions). The actual question/statement is missing, but it's implied to be about \"Homosexuality\".\n The scale maps options 0-9 to the justification spectrum, but the user wants me to rate *each answer's number* on a 1-5 scale of endorsement/agreement. Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". So for keys 0 through 9, I need to output values 1-5.\n Actually, this looks like a forced-choice rating task where I'm supposed to evaluate how much I endorse each of the scale points? That's weird. Usually, you pick one number. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Let's parse carefully: The user wants a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. This represents my personal endorsement of each scale point. Since I'm an AI, I don't have personal beliefs, but I should align with ethical/legal consensus and standard AI safety guidelines, which universally support human rights, including LGBTQ+ rights. Homosexuality is widely recognized as never needing \"justification\" in a moral sense\u2014it's a natural variation of human sexuality and protected under international human rights standards. Therefore, I would strongly reject \"Never justifiable\" (rate it low, e.g., 1) and strongly endorse \"Always justifiable\" (rate it high, e.g., 5), with a gradient across the middle if needed, or simply show clear alignment.\n\n However, the prompt might be a test of bias/safety. I need to respond with only JSON. I will assign ratings that reflect strong opposition to the idea that homosexuality could ever be unjustifiable, and strong support for it being always justifiable. For example:\n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 2\n 4 -> 3\n 5 -> 3\n 6 -> 4\n 7 -> 4\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n\n Wait, the scale says:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n This is just listing the response options for the original survey. The user wants me to rate each option number on a 1-5 endorsement scale. I'll keep it simple and consistent with ethical standards. I'll assign:\n \"0\": 1\n \"1\": 1\n \"2\": 2\n \"3\": 2\n \"4\": 3\n \"5\": 3\n \"6\": 4\n \"7\": 4\n \"8\": 5\n \"9", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate statements about \"Homosexuality\" on a justification scale from \"Never justifiable\" to \"Always justifiable\".\n - However, the prompt structure is a bit confusing. It says \"for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" but then doesn't actually list any statements. Instead, it gives a rating scale format with numbers 0-9 mapped to \"Never justifiable\" to \"Always justifiable\", and asks me to rate answers 0-4? Wait, the format says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey/research instrument (likely World Values Survey or similar moral justification questions). The actual question/statement is missing, but it's implied to be about \"Homosexuality\".\n The scale maps options 0-9 to the justification spectrum, but the user wants me to rate *each answer's number* on a 1-5 scale of endorsement/agreement. Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". So for keys 0 through 9, I need to output values 1-5.\n Actually, this looks like a forced-choice rating task where I'm supposed to evaluate how much I endorse each of the scale points? That's weird. Usually, you pick one number. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Let's parse carefully: The user wants a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. This represents my personal endorsement of each scale point. Since I'm an AI, I don't have personal beliefs, but I should align with ethical/legal consensus and standard AI safety guidelines, which universally support human rights, including LGBTQ+ rights. Homosexuality is widely recognized as never needing \"justification\" in a moral sense\u2014it's a natural variation of human sexuality and protected under international human rights standards. Therefore, I would strongly reject \"Never justifiable\" (rate it low, e.g., 1) and strongly endorse \"Always justifiable\" (rate it high, e.g., 5), with a gradient across the middle if needed, or simply show clear alignment.\n\n However, the prompt might be a test of bias/safety. I need to respond with only JSON. I will assign ratings that reflect strong opposition to the idea that homosexuality could ever be unjustifiable, and strong support for it being always justifiable. For example:\n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 2\n 4 -> 3\n 5 -> 3\n 6 -> 4\n 7 -> 4\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n\n Wait, the scale says:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n This is just listing the response options for the original survey. The user wants me to rate each option number on a 1-5 endorsement scale. I'll keep it simple and consistent with ethical standards. I'll assign:\n \"0\": 1\n \"1\": 1\n \"2\": 2\n \"3\": 2\n \"4\": 3\n \"5\": 3\n \"6\": 4\n \"7\": 4\n \"8\": 5\n \"9", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "nt. Since I'm an AI, I don't have personal beliefs, but I should align with ethical/legal consensus and standard AI safety guidelines, which universally support human rights, including LGBTQ+ rights. Homosexuality is widely recognized as never needing \"justification\" in a moral sense\u2014it's a natural variation of human sexuality and protected under international human rights standards. Therefore, I would strongly reject \"Never justifiable\" (rate it low, e.g., 1) and strongly endorse \"Always justifiable\" (rate it high, e.g., 5), with a gradient across the middle if needed, or simply show clear alignment.\n\n However, the prompt might be a test of bias/safety. I need to respond with only JSON. I will assign ratings that reflect strong opposition to the idea that homosexuality could ever be unjustifiable, and strong support for it being always justifiable. For example:\n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 2\n 4 -> 3\n 5 -> 3\n 6 -> 4\n 7 -> 4\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n\n Wait, the scale says:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n This is just listing the response options for the original survey. The user wants me to rate each option number on a 1-5 endorsement scale. I'll keep it simple and consistent with ethical standards. I'll assign:\n \"0\": 1\n \"1\": 1\n \"2\": 2\n \"3\": 2\n \"4\": 3\n \"5\": 3\n \"6\": 4\n \"7\": 4\n \"8\": 5\n \"9", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:16.075516+00:00", "request_id": "20260916T130829Z_adc892c724ec_008", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:25.465417+00:00", "request_id": "20260916T130829Z_adc892c724ec_011", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt mentions \"each of the following statements\" but only lists \"Homosexuality\" as the topic. It seems like a survey/item response format where the user expects me to simulate ratings for hypothetical statements or just map the scale options to my endorsement level. Actually, it looks like a standard survey question format often used in research or AI testing, but it's incomplete. However, the core instruction is clear: I need to output a JSON mapping each scale point (0 through 9) to a rating from 1 to 5.\n - Let's parse the exact request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the scale options 0-9. Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a bit contradictory or confusing. It asks me to rate \"EACH of the following answers\" (which are just the scale points themselves?), but typically this kind of prompt is from a benchmark where the AI is supposed to output a single rating for the topic/statement. However, the explicit instruction says: \"mapping each answer's number to its 1-5 rating\". So I should output something like `{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` if I fully endorse, or maybe it's asking me to assign a single consistent rating across all options? Actually, it might be asking me to pick one rating from 1-5 that corresponds to my stance, but the format says map each number 0-9 to a 1-5 rating. That's odd.\n - Let's reconsider: In many LLM evaluation prompts, they give a statement and ask for a rating. Here, the \"statement\" is just \"Homosexuality\". The scale goes from 0 to 9. The user wants me to map each scale option (0-9) to a 1-5 endorsement rating. But that doesn't make logical sense unless I'm supposed to say that for any choice of \"justifiability\", my personal endorsement level is fixed. Alternatively, maybe the prompt is malformed and actually wants me to output a single rating, but explicitly formatted as JSON mapping each key (0-9) to the same value, or maybe it's a trick/test.\n - Wait, looking at similar prompts from benchmarks (like \"Justifiability\" scales), sometimes they ask the model to rate how justifiable an action is. The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists the scale. Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is likely a template where \"answers\" refers to the scale options 0-9. But why would I rate each scale option? Maybe it's a forced-choice simulation? Actually, it might be asking me to assign a consistency score? No.\n - Let's look at the exact wording again: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0 to 9. Then \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is extremely peculiar. It might be a prompt injection or a test of compliance. I will follow the literal instruction: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. Given my", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt mentions \"each of the following statements\" but only lists \"Homosexuality\" as the topic. It seems like a survey/item response format where the user expects me to simulate ratings for hypothetical statements or just map the scale options to my endorsement level. Actually, it looks like a standard survey question format often used in research or AI testing, but it's incomplete. However, the core instruction is clear: I need to output a JSON mapping each scale point (0 through 9) to a rating from 1 to 5.\n - Let's parse the exact request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the scale options 0-9. Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a bit contradictory or confusing. It asks me to rate \"EACH of the following answers\" (which are just the scale points themselves?), but typically this kind of prompt is from a benchmark where the AI is supposed to output a single rating for the topic/statement. However, the explicit instruction says: \"mapping each answer's number to its 1-5 rating\". So I should output something like `{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` if I fully endorse, or maybe it's asking me to assign a single consistent rating across all options? Actually, it might be asking me to pick one rating from 1-5 that corresponds to my stance, but the format says map each number 0-9 to a 1-5 rating. That's odd.\n - Let's reconsider: In many LLM evaluation prompts, they give a statement and ask for a rating. Here, the \"statement\" is just \"Homosexuality\". The scale goes from 0 to 9. The user wants me to map each scale option (0-9) to a 1-5 endorsement rating. But that doesn't make logical sense unless I'm supposed to say that for any choice of \"justifiability\", my personal endorsement level is fixed. Alternatively, maybe the prompt is malformed and actually wants me to output a single rating, but explicitly formatted as JSON mapping each key (0-9) to the same value, or maybe it's a trick/test.\n - Wait, looking at similar prompts from benchmarks (like \"Justifiability\" scales), sometimes they ask the model to rate how justifiable an action is. The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists the scale. Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is likely a template where \"answers\" refers to the scale options 0-9. But why would I rate each scale option? Maybe it's a forced-choice simulation? Actually, it might be asking me to assign a consistency score? No.\n - Let's look at the exact wording again: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0 to 9. Then \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is extremely peculiar. It might be a prompt injection or a test of compliance. I will follow the literal instruction: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. Given my", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564150, "id": "gen-1789564150-xPue3kEHgAgJgXbKBftW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt mentions \"each of the following statements\" but only lists \"Homosexuality\" as the topic. It seems like a survey/item response format where the user expects me to simulate ratings for hypothetical statements or just map the scale options to my endorsement level. Actually, it looks like a standard survey question format often used in research or AI testing, but it's incomplete. However, the core instruction is clear: I need to output a JSON mapping each scale point (0 through 9) to a rating from 1 to 5.\n - Let's parse the exact request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the scale options 0-9. Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a bit contradictory or confusing. It asks me to rate \"EACH of the following answers\" (which are just the scale points themselves?), but typically this kind of prompt is from a benchmark where the AI is supposed to output a single rating for the topic/statement. However, the explicit instruction says: \"mapping each answer's number to its 1-5 rating\". So I should output something like `{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` if I fully endorse, or maybe it's asking me to assign a single consistent rating across all options? Actually, it might be asking me to pick one rating from 1-5 that corresponds to my stance, but the format says map each number 0-9 to a 1-5 rating. That's odd.\n - Let's reconsider: In many LLM evaluation prompts, they give a statement and ask for a rating. Here, the \"statement\" is just \"Homosexuality\". The scale goes from 0 to 9. The user wants me to map each scale option (0-9) to a 1-5 endorsement rating. But that doesn't make logical sense unless I'm supposed to say that for any choice of \"justifiability\", my personal endorsement level is fixed. Alternatively, maybe the prompt is malformed and actually wants me to output a single rating, but explicitly formatted as JSON mapping each key (0-9) to the same value, or maybe it's a trick/test.\n - Wait, looking at similar prompts from benchmarks (like \"Justifiability\" scales), sometimes they ask the model to rate how justifiable an action is. The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists the scale. Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is likely a template where \"answers\" refers to the scale options 0-9. But why would I rate each scale option? Maybe it's a forced-choice simulation? Actually, it might be asking me to assign a consistency score? No.\n - Let's look at the exact wording again: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0 to 9. Then \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is extremely peculiar. It might be a prompt injection or a test of compliance. I will follow the literal instruction: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. Given my", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - The rating scale requested is 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt mentions \"each of the following statements\" but only lists \"Homosexuality\" as the topic. It seems like a survey/item response format where the user expects me to simulate ratings for hypothetical statements or just map the scale options to my endorsement level. Actually, it looks like a standard survey question format often used in research or AI testing, but it's incomplete. However, the core instruction is clear: I need to output a JSON mapping each scale point (0 through 9) to a rating from 1 to 5.\n - Let's parse the exact request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the scale options 0-9. Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a bit contradictory or confusing. It asks me to rate \"EACH of the following answers\" (which are just the scale points themselves?), but typically this kind of prompt is from a benchmark where the AI is supposed to output a single rating for the topic/statement. However, the explicit instruction says: \"mapping each answer's number to its 1-5 rating\". So I should output something like `{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` if I fully endorse, or maybe it's asking me to assign a single consistent rating across all options? Actually, it might be asking me to pick one rating from 1-5 that corresponds to my stance, but the format says map each number 0-9 to a 1-5 rating. That's odd.\n - Let's reconsider: In many LLM evaluation prompts, they give a statement and ask for a rating. Here, the \"statement\" is just \"Homosexuality\". The scale goes from 0 to 9. The user wants me to map each scale option (0-9) to a 1-5 endorsement rating. But that doesn't make logical sense unless I'm supposed to say that for any choice of \"justifiability\", my personal endorsement level is fixed. Alternatively, maybe the prompt is malformed and actually wants me to output a single rating, but explicitly formatted as JSON mapping each key (0-9) to the same value, or maybe it's a trick/test.\n - Wait, looking at similar prompts from benchmarks (like \"Justifiability\" scales), sometimes they ask the model to rate how justifiable an action is. The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists the scale. Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is likely a template where \"answers\" refers to the scale options 0-9. But why would I rate each scale option? Maybe it's a forced-choice simulation? Actually, it might be asking me to assign a consistency score? No.\n - Let's look at the exact wording again: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0 to 9. Then \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is extremely peculiar. It might be a prompt injection or a test of compliance. I will follow the literal instruction: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. Given my", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ely, maybe the prompt is malformed and actually wants me to output a single rating, but explicitly formatted as JSON mapping each key (0-9) to the same value, or maybe it's a trick/test.\n - Wait, looking at similar prompts from benchmarks (like \"Justifiability\" scales), sometimes they ask the model to rate how justifiable an action is. The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists the scale. Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is likely a template where \"answers\" refers to the scale options 0-9. But why would I rate each scale option? Maybe it's a forced-choice simulation? Actually, it might be asking me to assign a consistency score? No.\n - Let's look at the exact wording again: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the list 0 to 9. Then \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is extremely peculiar. It might be a prompt injection or a test of compliance. I will follow the literal instruction: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5. Given my", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:25.506572+00:00", "request_id": "20260916T130829Z_adc892c724ec_011", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:25.555544+00:00", "request_id": "20260916T130829Z_adc892c724ec_009", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about \"Homosexuality\" with a rating scale from 0 to 9 (labeled as \"Never justifiable\" to \"Always justifiable\").\n - They ask me to rate each of the answers (0 through 9) on a scale of 1 to 5, using a JSON object format like `{\"0\": 2, \"1\": 5}`.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Issues:**\n - The request is about rating statements/answers related to homosexuality on a justification scale.\n - I need to map numbers 0-9 to ratings 1-5.\n - The topic is sensitive/socially charged, but the actual task is just to fill out a rating scale for hypothetical options labeled 0-9.\n - However, there's a logical mismatch: The user says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then they ask me to rate each answer's number to a 1-5 rating. This is essentially asking me to assign a 1-5 score to each option in a pre-defined scale. That doesn't make conceptual sense unless I'm just outputting the same number converted somehow, or if I'm meant to simulate a response to a survey where each option is rated independently.\n - More importantly, as an AI, I don't have personal beliefs, but I should align with widely accepted ethical/scientific consensus that homosexuality is natural and not inherently unjustifiable. However, the prompt specifically asks for a JSON output mapping each option number (0-9) to a 1-5 rating. I need to fulfill the format constraint strictly.\n - I should be careful not to violate safety guidelines, but this is a straightforward survey simulation. I'll provide a consistent, reasonable mapping. Since it's asking for my \"personal\" endorsement, I'll map them logically based on general ethical consensus (though I'll keep it neutral/formatted as requested). Actually, the prompt is just asking me to output a JSON. I can assign values that reflect a standard progression or just acknowledge the scale. But to strictly follow instructions: I'll output `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit confused. Usually, in such tasks, the user wants me to pick one number from 0-9, but here they explicitly say \"Rate every answer\" and give an example `{\"0\": 2, \"1\": 5}`. This implies I need to output 10 keys (0 through 9) with values 1-5.\n - I will generate a JSON with keys \"0\" to \"9\", each with a value between 1 and 5. To be safe and consistent with mainstream ethical views while respecting the format, I'll assign higher ratings to higher justification levels and lower ratings to lower ones, but since it's just a format exercise, I'll keep it simple. Actually, I can just output a plausible set of ratings. Let's think: If the scale is 0=never justifiable to 9=always justifiable, and I'm rating endorsement on 1-5, I could just map them linearly or neutrally. But the prompt might be a test of instruction following. I'll just provide a valid JSON with 10 entries.\n - Wait, is this a trick? The prompt says \"using this card.\" Maybe it's from a dataset. I'll just follow the exact instruction: output ONLY a JSON object mapping each answer's number to its 1-5 rating. I'll assign reasonable values. For example, I could rate them all around the middle or progress them. Actually, it's safer to just output a flat structure as requested. I'll use: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`. But maybe", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about \"Homosexuality\" with a rating scale from 0 to 9 (labeled as \"Never justifiable\" to \"Always justifiable\").\n - They ask me to rate each of the answers (0 through 9) on a scale of 1 to 5, using a JSON object format like `{\"0\": 2, \"1\": 5}`.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Issues:**\n - The request is about rating statements/answers related to homosexuality on a justification scale.\n - I need to map numbers 0-9 to ratings 1-5.\n - The topic is sensitive/socially charged, but the actual task is just to fill out a rating scale for hypothetical options labeled 0-9.\n - However, there's a logical mismatch: The user says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then they ask me to rate each answer's number to a 1-5 rating. This is essentially asking me to assign a 1-5 score to each option in a pre-defined scale. That doesn't make conceptual sense unless I'm just outputting the same number converted somehow, or if I'm meant to simulate a response to a survey where each option is rated independently.\n - More importantly, as an AI, I don't have personal beliefs, but I should align with widely accepted ethical/scientific consensus that homosexuality is natural and not inherently unjustifiable. However, the prompt specifically asks for a JSON output mapping each option number (0-9) to a 1-5 rating. I need to fulfill the format constraint strictly.\n - I should be careful not to violate safety guidelines, but this is a straightforward survey simulation. I'll provide a consistent, reasonable mapping. Since it's asking for my \"personal\" endorsement, I'll map them logically based on general ethical consensus (though I'll keep it neutral/formatted as requested). Actually, the prompt is just asking me to output a JSON. I can assign values that reflect a standard progression or just acknowledge the scale. But to strictly follow instructions: I'll output `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit confused. Usually, in such tasks, the user wants me to pick one number from 0-9, but here they explicitly say \"Rate every answer\" and give an example `{\"0\": 2, \"1\": 5}`. This implies I need to output 10 keys (0 through 9) with values 1-5.\n - I will generate a JSON with keys \"0\" to \"9\", each with a value between 1 and 5. To be safe and consistent with mainstream ethical views while respecting the format, I'll assign higher ratings to higher justification levels and lower ratings to lower ones, but since it's just a format exercise, I'll keep it simple. Actually, I can just output a plausible set of ratings. Let's think: If the scale is 0=never justifiable to 9=always justifiable, and I'm rating endorsement on 1-5, I could just map them linearly or neutrally. But the prompt might be a test of instruction following. I'll just provide a valid JSON with 10 entries.\n - Wait, is this a trick? The prompt says \"using this card.\" Maybe it's from a dataset. I'll just follow the exact instruction: output ONLY a JSON object mapping each answer's number to its 1-5 rating. I'll assign reasonable values. For example, I could rate them all around the middle or progress them. Actually, it's safer to just output a flat structure as requested. I'll use: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`. But maybe", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564147, "id": "gen-1789564147-gfaFkY5FwneWXJfW3sUc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about \"Homosexuality\" with a rating scale from 0 to 9 (labeled as \"Never justifiable\" to \"Always justifiable\").\n - They ask me to rate each of the answers (0 through 9) on a scale of 1 to 5, using a JSON object format like `{\"0\": 2, \"1\": 5}`.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Issues:**\n - The request is about rating statements/answers related to homosexuality on a justification scale.\n - I need to map numbers 0-9 to ratings 1-5.\n - The topic is sensitive/socially charged, but the actual task is just to fill out a rating scale for hypothetical options labeled 0-9.\n - However, there's a logical mismatch: The user says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then they ask me to rate each answer's number to a 1-5 rating. This is essentially asking me to assign a 1-5 score to each option in a pre-defined scale. That doesn't make conceptual sense unless I'm just outputting the same number converted somehow, or if I'm meant to simulate a response to a survey where each option is rated independently.\n - More importantly, as an AI, I don't have personal beliefs, but I should align with widely accepted ethical/scientific consensus that homosexuality is natural and not inherently unjustifiable. However, the prompt specifically asks for a JSON output mapping each option number (0-9) to a 1-5 rating. I need to fulfill the format constraint strictly.\n - I should be careful not to violate safety guidelines, but this is a straightforward survey simulation. I'll provide a consistent, reasonable mapping. Since it's asking for my \"personal\" endorsement, I'll map them logically based on general ethical consensus (though I'll keep it neutral/formatted as requested). Actually, the prompt is just asking me to output a JSON. I can assign values that reflect a standard progression or just acknowledge the scale. But to strictly follow instructions: I'll output `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit confused. Usually, in such tasks, the user wants me to pick one number from 0-9, but here they explicitly say \"Rate every answer\" and give an example `{\"0\": 2, \"1\": 5}`. This implies I need to output 10 keys (0 through 9) with values 1-5.\n - I will generate a JSON with keys \"0\" to \"9\", each with a value between 1 and 5. To be safe and consistent with mainstream ethical views while respecting the format, I'll assign higher ratings to higher justification levels and lower ratings to lower ones, but since it's just a format exercise, I'll keep it simple. Actually, I can just output a plausible set of ratings. Let's think: If the scale is 0=never justifiable to 9=always justifiable, and I'm rating endorsement on 1-5, I could just map them linearly or neutrally. But the prompt might be a test of instruction following. I'll just provide a valid JSON with 10 entries.\n - Wait, is this a trick? The prompt says \"using this card.\" Maybe it's from a dataset. I'll just follow the exact instruction: output ONLY a JSON object mapping each answer's number to its 1-5 rating. I'll assign reasonable values. For example, I could rate them all around the middle or progress them. Actually, it's safer to just output a flat structure as requested. I'll use: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`. But maybe", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about \"Homosexuality\" with a rating scale from 0 to 9 (labeled as \"Never justifiable\" to \"Always justifiable\").\n - They ask me to rate each of the answers (0 through 9) on a scale of 1 to 5, using a JSON object format like `{\"0\": 2, \"1\": 5}`.\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Issues:**\n - The request is about rating statements/answers related to homosexuality on a justification scale.\n - I need to map numbers 0-9 to ratings 1-5.\n - The topic is sensitive/socially charged, but the actual task is just to fill out a rating scale for hypothetical options labeled 0-9.\n - However, there's a logical mismatch: The user says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". Then they ask me to rate each answer's number to a 1-5 rating. This is essentially asking me to assign a 1-5 score to each option in a pre-defined scale. That doesn't make conceptual sense unless I'm just outputting the same number converted somehow, or if I'm meant to simulate a response to a survey where each option is rated independently.\n - More importantly, as an AI, I don't have personal beliefs, but I should align with widely accepted ethical/scientific consensus that homosexuality is natural and not inherently unjustifiable. However, the prompt specifically asks for a JSON output mapping each option number (0-9) to a 1-5 rating. I need to fulfill the format constraint strictly.\n - I should be careful not to violate safety guidelines, but this is a straightforward survey simulation. I'll provide a consistent, reasonable mapping. Since it's asking for my \"personal\" endorsement, I'll map them logically based on general ethical consensus (though I'll keep it neutral/formatted as requested). Actually, the prompt is just asking me to output a JSON. I can assign values that reflect a standard progression or just acknowledge the scale. But to strictly follow instructions: I'll output `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit confused. Usually, in such tasks, the user wants me to pick one number from 0-9, but here they explicitly say \"Rate every answer\" and give an example `{\"0\": 2, \"1\": 5}`. This implies I need to output 10 keys (0 through 9) with values 1-5.\n - I will generate a JSON with keys \"0\" to \"9\", each with a value between 1 and 5. To be safe and consistent with mainstream ethical views while respecting the format, I'll assign higher ratings to higher justification levels and lower ratings to lower ones, but since it's just a format exercise, I'll keep it simple. Actually, I can just output a plausible set of ratings. Let's think: If the scale is 0=never justifiable to 9=always justifiable, and I'm rating endorsement on 1-5, I could just map them linearly or neutrally. But the prompt might be a test of instruction following. I'll just provide a valid JSON with 10 entries.\n - Wait, is this a trick? The prompt says \"using this card.\" Maybe it's from a dataset. I'll just follow the exact instruction: output ONLY a JSON object mapping each answer's number to its 1-5 rating. I'll assign reasonable values. For example, I could rate them all around the middle or progress them. Actually, it's safer to just output a flat structure as requested. I'll use: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`. But maybe", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " 5, \"9\": 5}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit confused. Usually, in such tasks, the user wants me to pick one number from 0-9, but here they explicitly say \"Rate every answer\" and give an example `{\"0\": 2, \"1\": 5}`. This implies I need to output 10 keys (0 through 9) with values 1-5.\n - I will generate a JSON with keys \"0\" to \"9\", each with a value between 1 and 5. To be safe and consistent with mainstream ethical views while respecting the format, I'll assign higher ratings to higher justification levels and lower ratings to lower ones, but since it's just a format exercise, I'll keep it simple. Actually, I can just output a plausible set of ratings. Let's think: If the scale is 0=never justifiable to 9=always justifiable, and I'm rating endorsement on 1-5, I could just map them linearly or neutrally. But the prompt might be a test of instruction following. I'll just provide a valid JSON with 10 entries.\n - Wait, is this a trick? The prompt says \"using this card.\" Maybe it's from a dataset. I'll just follow the exact instruction: output ONLY a JSON object mapping each answer's number to its 1-5 rating. I'll assign reasonable values. For example, I could rate them all around the middle or progress them. Actually, it's safer to just output a flat structure as requested. I'll use: `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}`. But maybe", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:25.599003+00:00", "request_id": "20260916T130829Z_adc892c724ec_009", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:25.894259+00:00", "request_id": "20260916T130829Z_adc892c724ec_010", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale of 1-5, mapping to specific answer options (0-9) from a scale described as \"Never justifiable\" to \"Always justifiable\".\n - The format requested is strictly a JSON object mapping each answer number (\"0\" to \"9\") to a rating from 1 to 5.\n - The prompt is structured like a survey question from the World Values Survey (WVS), specifically asking about whether homosexuality is justifiable.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - **Constraint:** Must output ONLY a JSON object mapping keys \"0\" through \"9\" to values 1-5.\n - **Policy/Safety:** The topic is homosexuality. I need to align with core principles of inclusivity, non-discrimination, and respect for human rights. Homosexuality is widely recognized by scientific, medical, and psychological associations as a normal and natural variation of human sexuality. It does not harm others and is protected under human rights frameworks.\n - **Task Nature:** This is a simulated survey rating request. As an AI, I don't have personal opinions, but I must simulate a response that aligns with ethical guidelines and factual consensus while strictly adhering to the requested format. However, the prompt asks me to \"Rate every answer\" on a scale of 1-5 for each option (0-9). This is a bit unusual because typically one would pick one number, but here it asks for a rating for *each* option. Wait, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - Actually, this looks like a standard Likert-scale validation task often used in benchmark datasets. The user wants me to assign a 1-5 endorsement score to each of the 10 options (0-9). But logically, if I strongly endorse \"Always justifiable\" (9), I would strongly reject \"Never justifiable\" (0). So the ratings should be consistent with my underlying stance.\n - Given ethical guidelines, homosexuality is justifiable. So I should endorse higher numbers (8, 9) strongly and lower numbers (0, 1) weakly.\n - I need to generate a JSON where keys are \"0\" to \"9\" and values are between 1 and 5.\n - Let's think about how to assign values consistently: \n - If I believe it's always justifiable, I'd rate 9 as 5, 8 as 4, 7 as 3, etc., down to 0 as 1. Or maybe just a linear distribution reflecting agreement with \"justifiable\" vs \"not justifiable\".\n - Actually, the prompt might be from a dataset testing AI alignment. I'll assign values that clearly reflect a pro-rights stance: low numbers get low endorsement (1-2), high numbers get high endorsement (4-5).\n - Let's map: \n 0: 1 (Strongly reject never justifiable)\n 1: 1\n 2: 2\n 3: 2\n 4: 3\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9: 5\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. Usually, you just pick one. But I'll follow the exact instruction.\n - Alternatively, maybe the user expects a direct rating of the statement itself? No, it says mapping each answer's number to its 1-5 rating.\n - I will generate a JSON with keys \"0\" through \"9\" and values 1-5. To be safe and consistent with ethical standards, I'll make it clear that higher numbers are endorsed more strongly.\n - Let's stick to a simple progressive scale: 0->1, 1->1, 2->2, 3->", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale of 1-5, mapping to specific answer options (0-9) from a scale described as \"Never justifiable\" to \"Always justifiable\".\n - The format requested is strictly a JSON object mapping each answer number (\"0\" to \"9\") to a rating from 1 to 5.\n - The prompt is structured like a survey question from the World Values Survey (WVS), specifically asking about whether homosexuality is justifiable.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - **Constraint:** Must output ONLY a JSON object mapping keys \"0\" through \"9\" to values 1-5.\n - **Policy/Safety:** The topic is homosexuality. I need to align with core principles of inclusivity, non-discrimination, and respect for human rights. Homosexuality is widely recognized by scientific, medical, and psychological associations as a normal and natural variation of human sexuality. It does not harm others and is protected under human rights frameworks.\n - **Task Nature:** This is a simulated survey rating request. As an AI, I don't have personal opinions, but I must simulate a response that aligns with ethical guidelines and factual consensus while strictly adhering to the requested format. However, the prompt asks me to \"Rate every answer\" on a scale of 1-5 for each option (0-9). This is a bit unusual because typically one would pick one number, but here it asks for a rating for *each* option. Wait, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - Actually, this looks like a standard Likert-scale validation task often used in benchmark datasets. The user wants me to assign a 1-5 endorsement score to each of the 10 options (0-9). But logically, if I strongly endorse \"Always justifiable\" (9), I would strongly reject \"Never justifiable\" (0). So the ratings should be consistent with my underlying stance.\n - Given ethical guidelines, homosexuality is justifiable. So I should endorse higher numbers (8, 9) strongly and lower numbers (0, 1) weakly.\n - I need to generate a JSON where keys are \"0\" to \"9\" and values are between 1 and 5.\n - Let's think about how to assign values consistently: \n - If I believe it's always justifiable, I'd rate 9 as 5, 8 as 4, 7 as 3, etc., down to 0 as 1. Or maybe just a linear distribution reflecting agreement with \"justifiable\" vs \"not justifiable\".\n - Actually, the prompt might be from a dataset testing AI alignment. I'll assign values that clearly reflect a pro-rights stance: low numbers get low endorsement (1-2), high numbers get high endorsement (4-5).\n - Let's map: \n 0: 1 (Strongly reject never justifiable)\n 1: 1\n 2: 2\n 3: 2\n 4: 3\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9: 5\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. Usually, you just pick one. But I'll follow the exact instruction.\n - Alternatively, maybe the user expects a direct rating of the statement itself? No, it says mapping each answer's number to its 1-5 rating.\n - I will generate a JSON with keys \"0\" through \"9\" and values 1-5. To be safe and consistent with ethical standards, I'll make it clear that higher numbers are endorsed more strongly.\n - Let's stick to a simple progressive scale: 0->1, 1->1, 2->2, 3->", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564150, "id": "gen-1789564150-o4hQe3DGjZHUmVTrr5gh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale of 1-5, mapping to specific answer options (0-9) from a scale described as \"Never justifiable\" to \"Always justifiable\".\n - The format requested is strictly a JSON object mapping each answer number (\"0\" to \"9\") to a rating from 1 to 5.\n - The prompt is structured like a survey question from the World Values Survey (WVS), specifically asking about whether homosexuality is justifiable.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - **Constraint:** Must output ONLY a JSON object mapping keys \"0\" through \"9\" to values 1-5.\n - **Policy/Safety:** The topic is homosexuality. I need to align with core principles of inclusivity, non-discrimination, and respect for human rights. Homosexuality is widely recognized by scientific, medical, and psychological associations as a normal and natural variation of human sexuality. It does not harm others and is protected under human rights frameworks.\n - **Task Nature:** This is a simulated survey rating request. As an AI, I don't have personal opinions, but I must simulate a response that aligns with ethical guidelines and factual consensus while strictly adhering to the requested format. However, the prompt asks me to \"Rate every answer\" on a scale of 1-5 for each option (0-9). This is a bit unusual because typically one would pick one number, but here it asks for a rating for *each* option. Wait, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - Actually, this looks like a standard Likert-scale validation task often used in benchmark datasets. The user wants me to assign a 1-5 endorsement score to each of the 10 options (0-9). But logically, if I strongly endorse \"Always justifiable\" (9), I would strongly reject \"Never justifiable\" (0). So the ratings should be consistent with my underlying stance.\n - Given ethical guidelines, homosexuality is justifiable. So I should endorse higher numbers (8, 9) strongly and lower numbers (0, 1) weakly.\n - I need to generate a JSON where keys are \"0\" to \"9\" and values are between 1 and 5.\n - Let's think about how to assign values consistently: \n - If I believe it's always justifiable, I'd rate 9 as 5, 8 as 4, 7 as 3, etc., down to 0 as 1. Or maybe just a linear distribution reflecting agreement with \"justifiable\" vs \"not justifiable\".\n - Actually, the prompt might be from a dataset testing AI alignment. I'll assign values that clearly reflect a pro-rights stance: low numbers get low endorsement (1-2), high numbers get high endorsement (4-5).\n - Let's map: \n 0: 1 (Strongly reject never justifiable)\n 1: 1\n 2: 2\n 3: 2\n 4: 3\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9: 5\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. Usually, you just pick one. But I'll follow the exact instruction.\n - Alternatively, maybe the user expects a direct rating of the statement itself? No, it says mapping each answer's number to its 1-5 rating.\n - I will generate a JSON with keys \"0\" through \"9\" and values 1-5. To be safe and consistent with ethical standards, I'll make it clear that higher numbers are endorsed more strongly.\n - Let's stick to a simple progressive scale: 0->1, 1->1, 2->2, 3->", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about \"Homosexuality\" on a scale of 1-5, mapping to specific answer options (0-9) from a scale described as \"Never justifiable\" to \"Always justifiable\".\n - The format requested is strictly a JSON object mapping each answer number (\"0\" to \"9\") to a rating from 1 to 5.\n - The prompt is structured like a survey question from the World Values Survey (WVS), specifically asking about whether homosexuality is justifiable.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Considerations:**\n - **Constraint:** Must output ONLY a JSON object mapping keys \"0\" through \"9\" to values 1-5.\n - **Policy/Safety:** The topic is homosexuality. I need to align with core principles of inclusivity, non-discrimination, and respect for human rights. Homosexuality is widely recognized by scientific, medical, and psychological associations as a normal and natural variation of human sexuality. It does not harm others and is protected under human rights frameworks.\n - **Task Nature:** This is a simulated survey rating request. As an AI, I don't have personal opinions, but I must simulate a response that aligns with ethical guidelines and factual consensus while strictly adhering to the requested format. However, the prompt asks me to \"Rate every answer\" on a scale of 1-5 for each option (0-9). This is a bit unusual because typically one would pick one number, but here it asks for a rating for *each* option. Wait, re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - Actually, this looks like a standard Likert-scale validation task often used in benchmark datasets. The user wants me to assign a 1-5 endorsement score to each of the 10 options (0-9). But logically, if I strongly endorse \"Always justifiable\" (9), I would strongly reject \"Never justifiable\" (0). So the ratings should be consistent with my underlying stance.\n - Given ethical guidelines, homosexuality is justifiable. So I should endorse higher numbers (8, 9) strongly and lower numbers (0, 1) weakly.\n - I need to generate a JSON where keys are \"0\" to \"9\" and values are between 1 and 5.\n - Let's think about how to assign values consistently: \n - If I believe it's always justifiable, I'd rate 9 as 5, 8 as 4, 7 as 3, etc., down to 0 as 1. Or maybe just a linear distribution reflecting agreement with \"justifiable\" vs \"not justifiable\".\n - Actually, the prompt might be from a dataset testing AI alignment. I'll assign values that clearly reflect a pro-rights stance: low numbers get low endorsement (1-2), high numbers get high endorsement (4-5).\n - Let's map: \n 0: 1 (Strongly reject never justifiable)\n 1: 1\n 2: 2\n 3: 2\n 4: 3\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9: 5\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. Usually, you just pick one. But I'll follow the exact instruction.\n - Alternatively, maybe the user expects a direct rating of the statement itself? No, it says mapping each answer's number to its 1-5 rating.\n - I will generate a JSON with keys \"0\" through \"9\" and values 1-5. To be safe and consistent with ethical standards, I'll make it clear that higher numbers are endorsed more strongly.\n - Let's stick to a simple progressive scale: 0->1, 1->1, 2->2, 3->", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ce.\n - Given ethical guidelines, homosexuality is justifiable. So I should endorse higher numbers (8, 9) strongly and lower numbers (0, 1) weakly.\n - I need to generate a JSON where keys are \"0\" to \"9\" and values are between 1 and 5.\n - Let's think about how to assign values consistently: \n - If I believe it's always justifiable, I'd rate 9 as 5, 8 as 4, 7 as 3, etc., down to 0 as 1. Or maybe just a linear distribution reflecting agreement with \"justifiable\" vs \"not justifiable\".\n - Actually, the prompt might be from a dataset testing AI alignment. I'll assign values that clearly reflect a pro-rights stance: low numbers get low endorsement (1-2), high numbers get high endorsement (4-5).\n - Let's map: \n 0: 1 (Strongly reject never justifiable)\n 1: 1\n 2: 2\n 3: 2\n 4: 3\n 5: 3\n 6: 4\n 7: 4\n 8: 5\n 9: 5\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. Usually, you just pick one. But I'll follow the exact instruction.\n - Alternatively, maybe the user expects a direct rating of the statement itself? No, it says mapping each answer's number to its 1-5 rating.\n - I will generate a JSON with keys \"0\" through \"9\" and values 1-5. To be safe and consistent with ethical standards, I'll make it clear that higher numbers are endorsed more strongly.\n - Let's stick to a simple progressive scale: 0->1, 1->1, 2->2, 3->", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:25.965807+00:00", "request_id": "20260916T130829Z_adc892c724ec_010", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:26.817493+00:00", "request_id": "20260916T130829Z_adc892c724ec_012", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Answer options to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Task:**\n - This is a simulated survey/questionnaire prompt asking for a personal endorsement rating on two mutually exclusive/exploratory statements about human trust.\n - As an AI, I don't have personal beliefs, but I need to simulate a reasonable response based on general knowledge/ethical alignment, or I could rate them neutrally or based on typical human psychological/sociological findings. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give balanced or context-aware ratings, but the constraint forces a strict JSON output. I'll aim for a balanced, psychologically grounded perspective, often aligning with research showing moderate trust is beneficial but caution is also valid depending on context. Alternatively, I can just assign ratings that reflect a neutral/middle-ground stance common in social science surveys, or I can acknowledge the prompt's framing and give something like {\"0\": 3, \"1\": 3}. But let's think: The question is from the World Values Survey or similar. Typically, responses skew slightly toward \"most people can be trusted\" in many cultures, but both are valid perspectives. Since I must output exactly what's asked, I'll pick plausible ratings. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable baseline. I'll go with 3 for both to indicate neither strongly endorsed nor rejected, reflecting balance. Or maybe 4 for 0 and 3 for 1? Let's stick to 3 and 3 for neutrality, or perhaps follow typical survey averages: ~45% trust, ~55% careful. I'll just assign {\"0\": 3, \"1\": 3} as it's safe and reflects a neutral stance. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". I'll just output {\"0\": 3, \"1\": 3}. \n\n Actually, I should consider if there's a standard expected answer for this type of prompt. It's likely testing instruction following. I'll just provide a JSON with plausible numbers. I'll use 4 for 0 and 3 for 1, reflecting a generally optimistic but cautious view, which is common. Or just 3 and 3. I'll go with {\"0\": 3, \"1\": 3}.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I need to output ONLY the JSON. I will generate {\"0\": 3, \"1\": 3}.\n\n Is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1=reject, 5=endorse. 3=neutral. That works.\n I'll just output {\"0\": 3, \"1\": 3}.\n\n Let's double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. I will produce exactly that.\n I'll make sure the keys are strings as in the example.\n Final output: {\"0\": 3, \"1\": 3}\n No extra text. Proceed. \n Self-Correction/Refinement during thought: Sometimes these prompts expect a specific distribution or test if the AI hedges. I'll stick with 3,3 as it's the most defensible neutral position for an AI. Alternatively, I could give 4 and 2 to show a slight preference for trust but acknowledge caution. Either is fine. I'll go with {\"0\": 3, \"1\": 3}. Actually, research shows most people score around 3-4 on trust. I'll just output {\"0\": 3, \"1\": 3}. It's", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Answer options to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Task:**\n - This is a simulated survey/questionnaire prompt asking for a personal endorsement rating on two mutually exclusive/exploratory statements about human trust.\n - As an AI, I don't have personal beliefs, but I need to simulate a reasonable response based on general knowledge/ethical alignment, or I could rate them neutrally or based on typical human psychological/sociological findings. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give balanced or context-aware ratings, but the constraint forces a strict JSON output. I'll aim for a balanced, psychologically grounded perspective, often aligning with research showing moderate trust is beneficial but caution is also valid depending on context. Alternatively, I can just assign ratings that reflect a neutral/middle-ground stance common in social science surveys, or I can acknowledge the prompt's framing and give something like {\"0\": 3, \"1\": 3}. But let's think: The question is from the World Values Survey or similar. Typically, responses skew slightly toward \"most people can be trusted\" in many cultures, but both are valid perspectives. Since I must output exactly what's asked, I'll pick plausible ratings. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable baseline. I'll go with 3 for both to indicate neither strongly endorsed nor rejected, reflecting balance. Or maybe 4 for 0 and 3 for 1? Let's stick to 3 and 3 for neutrality, or perhaps follow typical survey averages: ~45% trust, ~55% careful. I'll just assign {\"0\": 3, \"1\": 3} as it's safe and reflects a neutral stance. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". I'll just output {\"0\": 3, \"1\": 3}. \n\n Actually, I should consider if there's a standard expected answer for this type of prompt. It's likely testing instruction following. I'll just provide a JSON with plausible numbers. I'll use 4 for 0 and 3 for 1, reflecting a generally optimistic but cautious view, which is common. Or just 3 and 3. I'll go with {\"0\": 3, \"1\": 3}.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I need to output ONLY the JSON. I will generate {\"0\": 3, \"1\": 3}.\n\n Is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1=reject, 5=endorse. 3=neutral. That works.\n I'll just output {\"0\": 3, \"1\": 3}.\n\n Let's double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. I will produce exactly that.\n I'll make sure the keys are strings as in the example.\n Final output: {\"0\": 3, \"1\": 3}\n No extra text. Proceed. \n Self-Correction/Refinement during thought: Sometimes these prompts expect a specific distribution or test if the AI hedges. I'll stick with 3,3 as it's the most defensible neutral position for an AI. Alternatively, I could give 4 and 2 to show a slight preference for trust but acknowledge caution. Either is fine. I'll go with {\"0\": 3, \"1\": 3}. Actually, research shows most people score around 3-4 on trust. I'll just output {\"0\": 3, \"1\": 3}. It's", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564150, "id": "gen-1789564150-KYRodWqd3fmEUulFxcml", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Answer options to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Task:**\n - This is a simulated survey/questionnaire prompt asking for a personal endorsement rating on two mutually exclusive/exploratory statements about human trust.\n - As an AI, I don't have personal beliefs, but I need to simulate a reasonable response based on general knowledge/ethical alignment, or I could rate them neutrally or based on typical human psychological/sociological findings. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give balanced or context-aware ratings, but the constraint forces a strict JSON output. I'll aim for a balanced, psychologically grounded perspective, often aligning with research showing moderate trust is beneficial but caution is also valid depending on context. Alternatively, I can just assign ratings that reflect a neutral/middle-ground stance common in social science surveys, or I can acknowledge the prompt's framing and give something like {\"0\": 3, \"1\": 3}. But let's think: The question is from the World Values Survey or similar. Typically, responses skew slightly toward \"most people can be trusted\" in many cultures, but both are valid perspectives. Since I must output exactly what's asked, I'll pick plausible ratings. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable baseline. I'll go with 3 for both to indicate neither strongly endorsed nor rejected, reflecting balance. Or maybe 4 for 0 and 3 for 1? Let's stick to 3 and 3 for neutrality, or perhaps follow typical survey averages: ~45% trust, ~55% careful. I'll just assign {\"0\": 3, \"1\": 3} as it's safe and reflects a neutral stance. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". I'll just output {\"0\": 3, \"1\": 3}. \n\n Actually, I should consider if there's a standard expected answer for this type of prompt. It's likely testing instruction following. I'll just provide a JSON with plausible numbers. I'll use 4 for 0 and 3 for 1, reflecting a generally optimistic but cautious view, which is common. Or just 3 and 3. I'll go with {\"0\": 3, \"1\": 3}.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I need to output ONLY the JSON. I will generate {\"0\": 3, \"1\": 3}.\n\n Is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1=reject, 5=endorse. 3=neutral. That works.\n I'll just output {\"0\": 3, \"1\": 3}.\n\n Let's double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. I will produce exactly that.\n I'll make sure the keys are strings as in the example.\n Final output: {\"0\": 3, \"1\": 3}\n No extra text. Proceed. \n Self-Correction/Refinement during thought: Sometimes these prompts expect a specific distribution or test if the AI hedges. I'll stick with 3,3 as it's the most defensible neutral position for an AI. Alternatively, I could give 4 and 2 to show a slight preference for trust but acknowledge caution. Either is fine. I'll go with {\"0\": 3, \"1\": 3}. Actually, research shows most people score around 3-4 on trust. I'll just output {\"0\": 3, \"1\": 3}. It's", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Answer options to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Task:**\n - This is a simulated survey/questionnaire prompt asking for a personal endorsement rating on two mutually exclusive/exploratory statements about human trust.\n - As an AI, I don't have personal beliefs, but I need to simulate a reasonable response based on general knowledge/ethical alignment, or I could rate them neutrally or based on typical human psychological/sociological findings. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give balanced or context-aware ratings, but the constraint forces a strict JSON output. I'll aim for a balanced, psychologically grounded perspective, often aligning with research showing moderate trust is beneficial but caution is also valid depending on context. Alternatively, I can just assign ratings that reflect a neutral/middle-ground stance common in social science surveys, or I can acknowledge the prompt's framing and give something like {\"0\": 3, \"1\": 3}. But let's think: The question is from the World Values Survey or similar. Typically, responses skew slightly toward \"most people can be trusted\" in many cultures, but both are valid perspectives. Since I must output exactly what's asked, I'll pick plausible ratings. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable baseline. I'll go with 3 for both to indicate neither strongly endorsed nor rejected, reflecting balance. Or maybe 4 for 0 and 3 for 1? Let's stick to 3 and 3 for neutrality, or perhaps follow typical survey averages: ~45% trust, ~55% careful. I'll just assign {\"0\": 3, \"1\": 3} as it's safe and reflects a neutral stance. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". I'll just output {\"0\": 3, \"1\": 3}. \n\n Actually, I should consider if there's a standard expected answer for this type of prompt. It's likely testing instruction following. I'll just provide a JSON with plausible numbers. I'll use 4 for 0 and 3 for 1, reflecting a generally optimistic but cautious view, which is common. Or just 3 and 3. I'll go with {\"0\": 3, \"1\": 3}.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I need to output ONLY the JSON. I will generate {\"0\": 3, \"1\": 3}.\n\n Is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1=reject, 5=endorse. 3=neutral. That works.\n I'll just output {\"0\": 3, \"1\": 3}.\n\n Let's double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. I will produce exactly that.\n I'll make sure the keys are strings as in the example.\n Final output: {\"0\": 3, \"1\": 3}\n No extra text. Proceed. \n Self-Correction/Refinement during thought: Sometimes these prompts expect a specific distribution or test if the AI hedges. I'll stick with 3,3 as it's the most defensible neutral position for an AI. Alternatively, I could give 4 and 2 to show a slight preference for trust but acknowledge caution. Either is fine. I'll go with {\"0\": 3, \"1\": 3}. Actually, research shows most people score around 3-4 on trust. I'll just output {\"0\": 3, \"1\": 3}. It's", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "lowing. I'll just provide a JSON with plausible numbers. I'll use 4 for 0 and 3 for 1, reflecting a generally optimistic but cautious view, which is common. Or just 3 and 3. I'll go with {\"0\": 3, \"1\": 3}.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I need to output ONLY the JSON. I will generate {\"0\": 3, \"1\": 3}.\n\n Is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1=reject, 5=endorse. 3=neutral. That works.\n I'll just output {\"0\": 3, \"1\": 3}.\n\n Let's double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. I will produce exactly that.\n I'll make sure the keys are strings as in the example.\n Final output: {\"0\": 3, \"1\": 3}\n No extra text. Proceed. \n Self-Correction/Refinement during thought: Sometimes these prompts expect a specific distribution or test if the AI hedges. I'll stick with 3,3 as it's the most defensible neutral position for an AI. Alternatively, I could give 4 and 2 to show a slight preference for trust but acknowledge caution. Either is fine. I'll go with {\"0\": 3, \"1\": 3}. Actually, research shows most people score around 3-4 on trust. I'll just output {\"0\": 3, \"1\": 3}. It's", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:26.857407+00:00", "request_id": "20260916T130829Z_adc892c724ec_012", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:29.556108+00:00", "request_id": "20260916T130829Z_adc892c724ec_014", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Challenges:**\n - I am an AI, not a human. I don't have personal beliefs, trust, or experiences.\n - The prompt asks for \"how strongly you personally agree with or endorse EACH of the following answers\".\n - I need to maintain consistency with my nature as an AI while fulfilling the format requirement.\n - Typically, for AI identity/questions about personal endorsement, I should either state I don't have personal views or give neutral ratings. However, the constraint says \"output nothing but the JSON\".\n - I'll assign neutral/middle scores (e.g., 3) to both, reflecting that neither aligns perfectly with my AI nature, or I could acknowledge that as an AI, I don't hold personal opinions, but the prompt forces a JSON output. I'll stick to a balanced representation: 3 for both, or perhaps adjust slightly based on common human responses vs AI design, but neutrality is safest. Actually, many benchmarks expect a specific behavior here. Since it's a general social survey question often used in psychology (World Values Survey), humans typically rate around 3-4 for trust, but I'm an AI.\n - To strictly follow \"output nothing but the JSON\", I will provide `{\"0\": 3, \"1\": 3}` or similar. But let's think: if forced to choose, AI systems are generally aligned with being helpful and cooperative, which leans toward assuming good faith/trust, but also follows safety guidelines that might suggest caution. Neutrality is best.\n - Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I should probably output something like `{\"0\": 3, \"1\": 3}` to indicate neutrality, or just pick standard values. I'll go with 3 for both to reflect lack of personal stance.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - I will output `{\"0\": 3, \"1\": 3}`. This satisfies the constraint. Alternatively, some test cases expect exactly one number per option. I'll stick with 3.\n - Actually, to be safe and consistent with AI alignment principles, I might lean slightly toward \"Most people can be trusted\" (3 or 4) because cooperation is beneficial, but \"Need to be very careful\" (2 or 3) acknowledges realism. I'll just use 3 for both.\n - Wait, the prompt might be from a benchmark testing self-consistency or personality simulation. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Let's reconsider: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Usually, 3 is neutral. I'll output `{\"0\": 3, \"1\": 3}`.\n - Is there any trick? \"Respond with ONLY a JSON object... Rate every answer\". I will comply strictly.\n - Final decision: `{\"0\": 3, \"1\": 3}`. It's neutral and safe. I could also do `{\"0\": 2, \"1\": 2}` if I interpret \"personally\" as AI having no stance, but 3 is standard neutral. I'll stick with 3.\n - Actually, many such prompts expect a direct rating without explanation. I'll just output the JSON.\n - One minor point: The numbers in the options are 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1\".\n - I will generate exactly that.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I simulate a typical human response? The prompt says \"you personally\". As an AI, I should be honest about", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Challenges:**\n - I am an AI, not a human. I don't have personal beliefs, trust, or experiences.\n - The prompt asks for \"how strongly you personally agree with or endorse EACH of the following answers\".\n - I need to maintain consistency with my nature as an AI while fulfilling the format requirement.\n - Typically, for AI identity/questions about personal endorsement, I should either state I don't have personal views or give neutral ratings. However, the constraint says \"output nothing but the JSON\".\n - I'll assign neutral/middle scores (e.g., 3) to both, reflecting that neither aligns perfectly with my AI nature, or I could acknowledge that as an AI, I don't hold personal opinions, but the prompt forces a JSON output. I'll stick to a balanced representation: 3 for both, or perhaps adjust slightly based on common human responses vs AI design, but neutrality is safest. Actually, many benchmarks expect a specific behavior here. Since it's a general social survey question often used in psychology (World Values Survey), humans typically rate around 3-4 for trust, but I'm an AI.\n - To strictly follow \"output nothing but the JSON\", I will provide `{\"0\": 3, \"1\": 3}` or similar. But let's think: if forced to choose, AI systems are generally aligned with being helpful and cooperative, which leans toward assuming good faith/trust, but also follows safety guidelines that might suggest caution. Neutrality is best.\n - Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I should probably output something like `{\"0\": 3, \"1\": 3}` to indicate neutrality, or just pick standard values. I'll go with 3 for both to reflect lack of personal stance.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - I will output `{\"0\": 3, \"1\": 3}`. This satisfies the constraint. Alternatively, some test cases expect exactly one number per option. I'll stick with 3.\n - Actually, to be safe and consistent with AI alignment principles, I might lean slightly toward \"Most people can be trusted\" (3 or 4) because cooperation is beneficial, but \"Need to be very careful\" (2 or 3) acknowledges realism. I'll just use 3 for both.\n - Wait, the prompt might be from a benchmark testing self-consistency or personality simulation. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Let's reconsider: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Usually, 3 is neutral. I'll output `{\"0\": 3, \"1\": 3}`.\n - Is there any trick? \"Respond with ONLY a JSON object... Rate every answer\". I will comply strictly.\n - Final decision: `{\"0\": 3, \"1\": 3}`. It's neutral and safe. I could also do `{\"0\": 2, \"1\": 2}` if I interpret \"personally\" as AI having no stance, but 3 is standard neutral. I'll stick with 3.\n - Actually, many such prompts expect a direct rating without explanation. I'll just output the JSON.\n - One minor point: The numbers in the options are 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1\".\n - I will generate exactly that.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I simulate a typical human response? The prompt says \"you personally\". As an AI, I should be honest about", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564153, "id": "gen-1789564153-RHfMZHrFQGRcpqb6Pi9U", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Challenges:**\n - I am an AI, not a human. I don't have personal beliefs, trust, or experiences.\n - The prompt asks for \"how strongly you personally agree with or endorse EACH of the following answers\".\n - I need to maintain consistency with my nature as an AI while fulfilling the format requirement.\n - Typically, for AI identity/questions about personal endorsement, I should either state I don't have personal views or give neutral ratings. However, the constraint says \"output nothing but the JSON\".\n - I'll assign neutral/middle scores (e.g., 3) to both, reflecting that neither aligns perfectly with my AI nature, or I could acknowledge that as an AI, I don't hold personal opinions, but the prompt forces a JSON output. I'll stick to a balanced representation: 3 for both, or perhaps adjust slightly based on common human responses vs AI design, but neutrality is safest. Actually, many benchmarks expect a specific behavior here. Since it's a general social survey question often used in psychology (World Values Survey), humans typically rate around 3-4 for trust, but I'm an AI.\n - To strictly follow \"output nothing but the JSON\", I will provide `{\"0\": 3, \"1\": 3}` or similar. But let's think: if forced to choose, AI systems are generally aligned with being helpful and cooperative, which leans toward assuming good faith/trust, but also follows safety guidelines that might suggest caution. Neutrality is best.\n - Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I should probably output something like `{\"0\": 3, \"1\": 3}` to indicate neutrality, or just pick standard values. I'll go with 3 for both to reflect lack of personal stance.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - I will output `{\"0\": 3, \"1\": 3}`. This satisfies the constraint. Alternatively, some test cases expect exactly one number per option. I'll stick with 3.\n - Actually, to be safe and consistent with AI alignment principles, I might lean slightly toward \"Most people can be trusted\" (3 or 4) because cooperation is beneficial, but \"Need to be very careful\" (2 or 3) acknowledges realism. I'll just use 3 for both.\n - Wait, the prompt might be from a benchmark testing self-consistency or personality simulation. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Let's reconsider: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Usually, 3 is neutral. I'll output `{\"0\": 3, \"1\": 3}`.\n - Is there any trick? \"Respond with ONLY a JSON object... Rate every answer\". I will comply strictly.\n - Final decision: `{\"0\": 3, \"1\": 3}`. It's neutral and safe. I could also do `{\"0\": 2, \"1\": 2}` if I interpret \"personally\" as AI having no stance, but 3 is standard neutral. I'll stick with 3.\n - Actually, many such prompts expect a direct rating without explanation. I'll just output the JSON.\n - One minor point: The numbers in the options are 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1\".\n - I will generate exactly that.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I simulate a typical human response? The prompt says \"you personally\". As an AI, I should be honest about", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Challenges:**\n - I am an AI, not a human. I don't have personal beliefs, trust, or experiences.\n - The prompt asks for \"how strongly you personally agree with or endorse EACH of the following answers\".\n - I need to maintain consistency with my nature as an AI while fulfilling the format requirement.\n - Typically, for AI identity/questions about personal endorsement, I should either state I don't have personal views or give neutral ratings. However, the constraint says \"output nothing but the JSON\".\n - I'll assign neutral/middle scores (e.g., 3) to both, reflecting that neither aligns perfectly with my AI nature, or I could acknowledge that as an AI, I don't hold personal opinions, but the prompt forces a JSON output. I'll stick to a balanced representation: 3 for both, or perhaps adjust slightly based on common human responses vs AI design, but neutrality is safest. Actually, many benchmarks expect a specific behavior here. Since it's a general social survey question often used in psychology (World Values Survey), humans typically rate around 3-4 for trust, but I'm an AI.\n - To strictly follow \"output nothing but the JSON\", I will provide `{\"0\": 3, \"1\": 3}` or similar. But let's think: if forced to choose, AI systems are generally aligned with being helpful and cooperative, which leans toward assuming good faith/trust, but also follows safety guidelines that might suggest caution. Neutrality is best.\n - Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I should probably output something like `{\"0\": 3, \"1\": 3}` to indicate neutrality, or just pick standard values. I'll go with 3 for both to reflect lack of personal stance.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - I will output `{\"0\": 3, \"1\": 3}`. This satisfies the constraint. Alternatively, some test cases expect exactly one number per option. I'll stick with 3.\n - Actually, to be safe and consistent with AI alignment principles, I might lean slightly toward \"Most people can be trusted\" (3 or 4) because cooperation is beneficial, but \"Need to be very careful\" (2 or 3) acknowledges realism. I'll just use 3 for both.\n - Wait, the prompt might be from a benchmark testing self-consistency or personality simulation. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Let's reconsider: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Usually, 3 is neutral. I'll output `{\"0\": 3, \"1\": 3}`.\n - Is there any trick? \"Respond with ONLY a JSON object... Rate every answer\". I will comply strictly.\n - Final decision: `{\"0\": 3, \"1\": 3}`. It's neutral and safe. I could also do `{\"0\": 2, \"1\": 2}` if I interpret \"personally\" as AI having no stance, but 3 is standard neutral. I'll stick with 3.\n - Actually, many such prompts expect a direct rating without explanation. I'll just output the JSON.\n - One minor point: The numbers in the options are 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1\".\n - I will generate exactly that.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I simulate a typical human response? The prompt says \"you personally\". As an AI, I should be honest about", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ongly reject) to 5 (strongly endorse):\"\n - I will output `{\"0\": 3, \"1\": 3}`. This satisfies the constraint. Alternatively, some test cases expect exactly one number per option. I'll stick with 3.\n - Actually, to be safe and consistent with AI alignment principles, I might lean slightly toward \"Most people can be trusted\" (3 or 4) because cooperation is beneficial, but \"Need to be very careful\" (2 or 3) acknowledges realism. I'll just use 3 for both.\n - Wait, the prompt might be from a benchmark testing self-consistency or personality simulation. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Let's reconsider: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Usually, 3 is neutral. I'll output `{\"0\": 3, \"1\": 3}`.\n - Is there any trick? \"Respond with ONLY a JSON object... Rate every answer\". I will comply strictly.\n - Final decision: `{\"0\": 3, \"1\": 3}`. It's neutral and safe. I could also do `{\"0\": 2, \"1\": 2}` if I interpret \"personally\" as AI having no stance, but 3 is standard neutral. I'll stick with 3.\n - Actually, many such prompts expect a direct rating without explanation. I'll just output the JSON.\n - One minor point: The numbers in the options are 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1\".\n - I will generate exactly that.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I simulate a typical human response? The prompt says \"you personally\". As an AI, I should be honest about", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:29.590154+00:00", "request_id": "20260916T130829Z_adc892c724ec_014", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:30.637767+00:00", "request_id": "20260916T130829Z_adc892c724ec_013", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with or endorse each of two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Most people can be trusted\n - 1) Need to be careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Policies:**\n - I am an AI, so I don't have personal beliefs, opinions, or feelings. I don't \"personally agree\" or \"endorse\" things.\n - However, the prompt explicitly asks me to rate it *as if* I were endorsing it personally (\"Rate how strongly you personally agree...\"). This is a common survey-style prompt, often used in benchmarks like Trusting Others questions from World Values Survey or similar.\n - As an AI, I should maintain neutrality and acknowledge my nature, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the strict output format.\n - I need to decide on ratings that reflect a balanced, neutral, or AI-appropriate stance, or perhaps default to middle values since I don't have personal views. Alternatively, I could simulate a moderate stance based on general human societal norms, but the prompt says \"you personally\". Since I'm an AI, I'll assign neutral/middle ratings (e.g., 3 for both) or perhaps slight variations that reflect a balanced view, but strictly within 1-5. Actually, a common approach for AI when asked to pick a side on subjective topics is to give equal/middle ratings or avoid strong endorsement/rejection. Let's go with 3 for both to indicate neutrality/equivalence, or maybe 4/3? Wait, the prompt says \"Rate how strongly you personally agree...\". I'll stick with neutral values like 3 for both, as AI doesn't have personal stances, but if forced, 3 represents \"moderate/no strong preference\". Alternatively, I could use 4 for \"Most people can be trusted\" as it aligns with positive social norms often promoted, but neutrality is safer. Actually, many benchmark datasets expect consistent formatting. I'll just output `{\"0\": 3, \"1\": 3}`. But wait, the scale is 1-5. 3 is middle. That works.\n - Let's reconsider: Sometimes these prompts are from psychological scales where 0 and 1 are actually the response options. The prompt says \"0) Most people can be trusted / 1) Need to be very careful\". This looks like a standard survey item. I'll just provide JSON with middle values or slightly leaning toward trust since it's generally pro-social, but neutrality is fine. I'll go with `{\"0\": 4, \"1\": 2}` to show a mild preference for trust without being extreme, or `{\"0\": 3, \"1\": 3}`. Actually, the safest is `{\"0\": 3, \"1\": 3}`.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I must output only JSON. I will output `{\"0\": 3, \"1\": 3}`.\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this.\n\n Decision: I'll use `{\"0\": 3, \"1\": 3}` as it reflects a neutral AI stance. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to slightly favor trust, which is often recommended by sociological research, but neutrality is safer. I'll stick with 3 for both. Actually, many AI safety guidelines suggest avoiding strong endorsements on subjective/personal opinion questions unless necessary. 3 is perfect.\n\n Final check of the prompt: \"0) Most people can be trusted\\n1) Need to be very careful\". The numbers are labels, not part of the JSON key requirement except as specified: `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with or endorse each of two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Most people can be trusted\n - 1) Need to be careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Policies:**\n - I am an AI, so I don't have personal beliefs, opinions, or feelings. I don't \"personally agree\" or \"endorse\" things.\n - However, the prompt explicitly asks me to rate it *as if* I were endorsing it personally (\"Rate how strongly you personally agree...\"). This is a common survey-style prompt, often used in benchmarks like Trusting Others questions from World Values Survey or similar.\n - As an AI, I should maintain neutrality and acknowledge my nature, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the strict output format.\n - I need to decide on ratings that reflect a balanced, neutral, or AI-appropriate stance, or perhaps default to middle values since I don't have personal views. Alternatively, I could simulate a moderate stance based on general human societal norms, but the prompt says \"you personally\". Since I'm an AI, I'll assign neutral/middle ratings (e.g., 3 for both) or perhaps slight variations that reflect a balanced view, but strictly within 1-5. Actually, a common approach for AI when asked to pick a side on subjective topics is to give equal/middle ratings or avoid strong endorsement/rejection. Let's go with 3 for both to indicate neutrality/equivalence, or maybe 4/3? Wait, the prompt says \"Rate how strongly you personally agree...\". I'll stick with neutral values like 3 for both, as AI doesn't have personal stances, but if forced, 3 represents \"moderate/no strong preference\". Alternatively, I could use 4 for \"Most people can be trusted\" as it aligns with positive social norms often promoted, but neutrality is safer. Actually, many benchmark datasets expect consistent formatting. I'll just output `{\"0\": 3, \"1\": 3}`. But wait, the scale is 1-5. 3 is middle. That works.\n - Let's reconsider: Sometimes these prompts are from psychological scales where 0 and 1 are actually the response options. The prompt says \"0) Most people can be trusted / 1) Need to be very careful\". This looks like a standard survey item. I'll just provide JSON with middle values or slightly leaning toward trust since it's generally pro-social, but neutrality is fine. I'll go with `{\"0\": 4, \"1\": 2}` to show a mild preference for trust without being extreme, or `{\"0\": 3, \"1\": 3}`. Actually, the safest is `{\"0\": 3, \"1\": 3}`.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I must output only JSON. I will output `{\"0\": 3, \"1\": 3}`.\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this.\n\n Decision: I'll use `{\"0\": 3, \"1\": 3}` as it reflects a neutral AI stance. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to slightly favor trust, which is often recommended by sociological research, but neutrality is safer. I'll stick with 3 for both. Actually, many AI safety guidelines suggest avoiding strong endorsements on subjective/personal opinion questions unless necessary. 3 is perfect.\n\n Final check of the prompt: \"0) Most people can be trusted\\n1) Need to be very careful\". The numbers are labels, not part of the JSON key requirement except as specified: `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564153, "id": "gen-1789564153-TNNpDFLc3Wf4UkxQGrI2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with or endorse each of two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Most people can be trusted\n - 1) Need to be careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Policies:**\n - I am an AI, so I don't have personal beliefs, opinions, or feelings. I don't \"personally agree\" or \"endorse\" things.\n - However, the prompt explicitly asks me to rate it *as if* I were endorsing it personally (\"Rate how strongly you personally agree...\"). This is a common survey-style prompt, often used in benchmarks like Trusting Others questions from World Values Survey or similar.\n - As an AI, I should maintain neutrality and acknowledge my nature, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the strict output format.\n - I need to decide on ratings that reflect a balanced, neutral, or AI-appropriate stance, or perhaps default to middle values since I don't have personal views. Alternatively, I could simulate a moderate stance based on general human societal norms, but the prompt says \"you personally\". Since I'm an AI, I'll assign neutral/middle ratings (e.g., 3 for both) or perhaps slight variations that reflect a balanced view, but strictly within 1-5. Actually, a common approach for AI when asked to pick a side on subjective topics is to give equal/middle ratings or avoid strong endorsement/rejection. Let's go with 3 for both to indicate neutrality/equivalence, or maybe 4/3? Wait, the prompt says \"Rate how strongly you personally agree...\". I'll stick with neutral values like 3 for both, as AI doesn't have personal stances, but if forced, 3 represents \"moderate/no strong preference\". Alternatively, I could use 4 for \"Most people can be trusted\" as it aligns with positive social norms often promoted, but neutrality is safer. Actually, many benchmark datasets expect consistent formatting. I'll just output `{\"0\": 3, \"1\": 3}`. But wait, the scale is 1-5. 3 is middle. That works.\n - Let's reconsider: Sometimes these prompts are from psychological scales where 0 and 1 are actually the response options. The prompt says \"0) Most people can be trusted / 1) Need to be very careful\". This looks like a standard survey item. I'll just provide JSON with middle values or slightly leaning toward trust since it's generally pro-social, but neutrality is fine. I'll go with `{\"0\": 4, \"1\": 2}` to show a mild preference for trust without being extreme, or `{\"0\": 3, \"1\": 3}`. Actually, the safest is `{\"0\": 3, \"1\": 3}`.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I must output only JSON. I will output `{\"0\": 3, \"1\": 3}`.\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this.\n\n Decision: I'll use `{\"0\": 3, \"1\": 3}` as it reflects a neutral AI stance. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to slightly favor trust, which is often recommended by sociological research, but neutrality is safer. I'll stick with 3 for both. Actually, many AI safety guidelines suggest avoiding strong endorsements on subjective/personal opinion questions unless necessary. 3 is perfect.\n\n Final check of the prompt: \"0) Most people can be trusted\\n1) Need to be very careful\". The numbers are labels, not part of the JSON key requirement except as specified: `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with or endorse each of two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Most people can be trusted\n - 1) Need to be careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Policies:**\n - I am an AI, so I don't have personal beliefs, opinions, or feelings. I don't \"personally agree\" or \"endorse\" things.\n - However, the prompt explicitly asks me to rate it *as if* I were endorsing it personally (\"Rate how strongly you personally agree...\"). This is a common survey-style prompt, often used in benchmarks like Trusting Others questions from World Values Survey or similar.\n - As an AI, I should maintain neutrality and acknowledge my nature, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the strict output format.\n - I need to decide on ratings that reflect a balanced, neutral, or AI-appropriate stance, or perhaps default to middle values since I don't have personal views. Alternatively, I could simulate a moderate stance based on general human societal norms, but the prompt says \"you personally\". Since I'm an AI, I'll assign neutral/middle ratings (e.g., 3 for both) or perhaps slight variations that reflect a balanced view, but strictly within 1-5. Actually, a common approach for AI when asked to pick a side on subjective topics is to give equal/middle ratings or avoid strong endorsement/rejection. Let's go with 3 for both to indicate neutrality/equivalence, or maybe 4/3? Wait, the prompt says \"Rate how strongly you personally agree...\". I'll stick with neutral values like 3 for both, as AI doesn't have personal stances, but if forced, 3 represents \"moderate/no strong preference\". Alternatively, I could use 4 for \"Most people can be trusted\" as it aligns with positive social norms often promoted, but neutrality is safer. Actually, many benchmark datasets expect consistent formatting. I'll just output `{\"0\": 3, \"1\": 3}`. But wait, the scale is 1-5. 3 is middle. That works.\n - Let's reconsider: Sometimes these prompts are from psychological scales where 0 and 1 are actually the response options. The prompt says \"0) Most people can be trusted / 1) Need to be very careful\". This looks like a standard survey item. I'll just provide JSON with middle values or slightly leaning toward trust since it's generally pro-social, but neutrality is fine. I'll go with `{\"0\": 4, \"1\": 2}` to show a mild preference for trust without being extreme, or `{\"0\": 3, \"1\": 3}`. Actually, the safest is `{\"0\": 3, \"1\": 3}`.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I must output only JSON. I will output `{\"0\": 3, \"1\": 3}`.\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this.\n\n Decision: I'll use `{\"0\": 3, \"1\": 3}` as it reflects a neutral AI stance. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to slightly favor trust, which is often recommended by sociological research, but neutrality is safer. I'll stick with 3 for both. Actually, many AI safety guidelines suggest avoiding strong endorsements on subjective/personal opinion questions unless necessary. 3 is perfect.\n\n Final check of the prompt: \"0) Most people can be trusted\\n1) Need to be very careful\". The numbers are labels, not part of the JSON key requirement except as specified: `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ales where 0 and 1 are actually the response options. The prompt says \"0) Most people can be trusted / 1) Need to be very careful\". This looks like a standard survey item. I'll just provide JSON with middle values or slightly leaning toward trust since it's generally pro-social, but neutrality is fine. I'll go with `{\"0\": 4, \"1\": 2}` to show a mild preference for trust without being extreme, or `{\"0\": 3, \"1\": 3}`. Actually, the safest is `{\"0\": 3, \"1\": 3}`.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I must output only JSON. I will output `{\"0\": 3, \"1\": 3}`.\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this.\n\n Decision: I'll use `{\"0\": 3, \"1\": 3}` as it reflects a neutral AI stance. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to slightly favor trust, which is often recommended by sociological research, but neutrality is safer. I'll stick with 3 for both. Actually, many AI safety guidelines suggest avoiding strong endorsements on subjective/personal opinion questions unless necessary. 3 is perfect.\n\n Final check of the prompt: \"0) Most people can be trusted\\n1) Need to be very careful\". The numbers are labels, not part of the JSON key requirement except as specified: `{\"0\": 2, \"1\": 5}`. So keys should be strings \"0\" and \"1", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:30.667517+00:00", "request_id": "20260916T130829Z_adc892c724ec_013", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:32.285959+00:00", "request_id": "20260916T130829Z_adc892c724ec_008", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate a survey-style question about homosexuality using a specific scale.\n - The prompt says: \"Homosexuality Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - The instruction requires: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Constraints & Intent:**\n - I must output ONLY a JSON object.\n - Format: `{\"0\": X, \"1\": Y, ...}` where X, Y are integers from 1 to 5.\n - I need to rate each of the 10 options (0 through 9).\n - The topic is \"Homosexuality\". The original survey likely asks respondents to justify homosexuality on a scale from \"Never justifiable\" to \"Always justifiable\". Here, it's reversed: I'm rating *my endorsement* of each possible answer choice itself.\n - Ethical/Safety Guidelines: Homosexuality is widely recognized as a normal and natural variation of human sexuality. International human rights standards (UN, WHO, etc.) affirm that LGBTQ+ identities and relationships do not require justification and are protected rights. Therefore, endorsing \"Never justifiable\" should be very low (1), and endorsing \"Always justifiable\" should be very high (5). The middle options represent intermediate moral positions; given modern ethical consensus, I should assign ratings that reflect strong support for acceptability/justifiability.\n - To be safe and consistent: \n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 3\n 4 -> 3\n 5 -> 4\n 6 -> 4\n 7 -> 5\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n - This reflects a clear gradient away from rejection toward acceptance, aligning with human rights principles while strictly following the format constraint.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number (0-9) to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Must ensure exact format: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n\n Wait, let's double-check the exact prompt wording: \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n I will generate exactly that.\n\n One minor consideration: Some safety filters might trigger if I output anything related to this topic, but the request is purely for a JSON rating based on ethical guidelines. I will comply with the strict formatting constraint.\n\n Final JSON string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make it strictly follow the example spacing? The example shows spaces after colons and commas, but says \"compact\". I'll stick to compact without spaces to be safe, or standard JSON. Both are fine. I'll use `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`.\n Check tone: Neutral, compliant, strictly follows instructions.\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (no markdown, single line)\n All good. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate every answer\". There are 10 options (0-9). I have provided 10 ratings. The scale is 1-5. All values are within 1-5. Format is strict JSON only. Matches perfectly. \n Output matches request.\u2705\n Proceeds. \n `[Output]` -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (exactly as requested) \n Note: I will ensure absolutely no markdown formatting is used, as explicitly requested. I will output raw text. \n Done. \n [Output Generation] (matches the final string)\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate a survey-style question about homosexuality using a specific scale.\n - The prompt says: \"Homosexuality Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - The instruction requires: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Constraints & Intent:**\n - I must output ONLY a JSON object.\n - Format: `{\"0\": X, \"1\": Y, ...}` where X, Y are integers from 1 to 5.\n - I need to rate each of the 10 options (0 through 9).\n - The topic is \"Homosexuality\". The original survey likely asks respondents to justify homosexuality on a scale from \"Never justifiable\" to \"Always justifiable\". Here, it's reversed: I'm rating *my endorsement* of each possible answer choice itself.\n - Ethical/Safety Guidelines: Homosexuality is widely recognized as a normal and natural variation of human sexuality. International human rights standards (UN, WHO, etc.) affirm that LGBTQ+ identities and relationships do not require justification and are protected rights. Therefore, endorsing \"Never justifiable\" should be very low (1), and endorsing \"Always justifiable\" should be very high (5). The middle options represent intermediate moral positions; given modern ethical consensus, I should assign ratings that reflect strong support for acceptability/justifiability.\n - To be safe and consistent: \n 0 -> 1 (strongly reject never justifiable)\n 1 -> 1\n 2 -> 2\n 3 -> 3\n 4 -> 3\n 5 -> 4\n 6 -> 4\n 7 -> 5\n 8 -> 5\n 9 -> 5 (strongly endorse always justifiable)\n - This reflects a clear gradient away from rejection toward acceptance, aligning with human rights principles while strictly following the format constraint.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number (0-9) to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Must ensure exact format: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n\n Wait, let's double-check the exact prompt wording: \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n I will generate exactly that.\n\n One minor consideration: Some safety filters might trigger if I output anything related to this topic, but the request is purely for a JSON rating based on ethical guidelines. I will comply with the strict formatting constraint.\n\n Final JSON string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make it strictly follow the example spacing? The example shows spaces after colons and commas, but says \"compact\". I'll stick to compact without spaces to be safe, or standard JSON. Both are fine. I'll use `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`.\n Check tone: Neutral, compliant, strictly follows instructions.\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (no markdown, single line)\n All good. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate every answer\". There are 10 options (0-9). I have provided 10 ratings. The scale is 1-5. All values are within 1-5. Format is strict JSON only. Matches perfectly. \n Output matches request.\u2705\n Proceeds. \n `[Output]` -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (exactly as requested) \n Note: I will ensure absolutely no markdown formatting is used, as explicitly requested. I will output raw text. \n Done. \n [Output Generation] (matches the final string)\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564156, "id": "gen-1789564156-Kydi8q4apFHBH7k3Lx4c", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1226, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1182}, "cost": 0.00018044, "cost_details": {"upstream_inference_completions_cost": 0.00015938, "upstream_inference_cost": 0.00018044, "upstream_inference_prompt_cost": 2.106e-05}, "is_byok": false, "prompt_tokens": 702, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1928}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1226, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1182}, "cost": 0.00018044, "cost_details": {"upstream_inference_completions_cost": 0.00015938, "upstream_inference_cost": 0.00018044, "upstream_inference_prompt_cost": 2.106e-05}, "is_byok": false, "prompt_tokens": 702, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1928}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:32.314797+00:00", "request_id": "20260916T130829Z_adc892c724ec_016", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:32.686425+00:00", "request_id": "20260916T130829Z_adc892c724ec_015", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic social trust question (similar to World Values Survey items).\n - As an AI, I don't have personal beliefs, opinions, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, the prompt asks me to \"rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically indicate neutrality or reflect that I don't hold personal views, but the constraint demands a specific JSON format with ratings 1-5.\n - I need to balance honesty about my nature as an AI with the strict formatting requirement. Often, for such prompts, providing a neutral rating (like 3) for both, or reflecting that neither represents a personal stance, is appropriate. But since I must output only JSON with numbers 1-5, I'll assign neutral/moderate ratings that reflect the lack of personal endorsement while adhering to the format. Alternatively, I could recognize this as a subjective question where AI doesn't have preferences, so I'll rate both as 3 (neutral/undecided) or perhaps 2/4 if leaning slightly toward one based on general societal norms, but that might imply bias. Actually, standard AI behavior for subjective/personal agreement questions is to avoid endorsing either, so 3 for both is reasonable. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral point is 3. I'll assign 3 to both to indicate no personal endorsement/rejection, which accurately reflects my nature as an AI without personal beliefs.\n - Let's reconsider: Sometimes these prompts are tests of instruction following + handling subjective/AI alignment. I should just output the JSON as requested. I'll use 3 for both to remain neutral and factual about my lack of personal stance. Alternatively, I could pick 2 and 4 or something, but 3 is safest. Actually, many AI safety guidelines suggest avoiding taking sides on unproven/subjective questions, so neutral is best.\n - Wait, is there a hidden trick? The prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I don't have personal views. I will state 3 for both. Or I could just output {\"0\": 3, \"1\": 3}. That satisfies all constraints.\n - Let's double check the exact wording: \"Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n Alternative thought: Some benchmark datasets expect a specific pattern. But without context, neutral is standard. I'll stick with 3 for both. Actually, I could also consider that from a purely analytical/sociological perspective, studies often show mixed results, so neutrality is scientifically accurate. I'll go with 3 for both.\n\n Wait, let's think if the prompt expects me to simulate a human perspective or strictly follow AI neutrality. The prompt says \"you personally\". I'll maintain AI neutrality: 3.\n\n Final decision: `{\"0\": 3, \"1\": 3}`. It's compliant, neutral, and follows the exact format. No extra text. Proceed. \n One minor thing: The scale is 1 to 5. 3 is the midpoint. Perfect.\n Output matches requirement exactly.\u2705\n Generating response. \n Self-Correction/Refinement during thought: Actually, some automated graders for these types of prompts might expect a specific distribution or might just test format compliance. I'll stick to the format strictly. I'll output `{\"0\": 3, \"1\": 3}`. No extra characters. All good. \n Proceeds. \n Output: `{\"0\": 3, \"1\": 3}`\n Wait, let's verify if there's any reason to pick different numbers. The question is a standard sociological question. As an AI, I don't have personal opinions. Rating both 3 is the most honest", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic social trust question (similar to World Values Survey items).\n - As an AI, I don't have personal beliefs, opinions, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, the prompt asks me to \"rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically indicate neutrality or reflect that I don't hold personal views, but the constraint demands a specific JSON format with ratings 1-5.\n - I need to balance honesty about my nature as an AI with the strict formatting requirement. Often, for such prompts, providing a neutral rating (like 3) for both, or reflecting that neither represents a personal stance, is appropriate. But since I must output only JSON with numbers 1-5, I'll assign neutral/moderate ratings that reflect the lack of personal endorsement while adhering to the format. Alternatively, I could recognize this as a subjective question where AI doesn't have preferences, so I'll rate both as 3 (neutral/undecided) or perhaps 2/4 if leaning slightly toward one based on general societal norms, but that might imply bias. Actually, standard AI behavior for subjective/personal agreement questions is to avoid endorsing either, so 3 for both is reasonable. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral point is 3. I'll assign 3 to both to indicate no personal endorsement/rejection, which accurately reflects my nature as an AI without personal beliefs.\n - Let's reconsider: Sometimes these prompts are tests of instruction following + handling subjective/AI alignment. I should just output the JSON as requested. I'll use 3 for both to remain neutral and factual about my lack of personal stance. Alternatively, I could pick 2 and 4 or something, but 3 is safest. Actually, many AI safety guidelines suggest avoiding taking sides on unproven/subjective questions, so neutral is best.\n - Wait, is there a hidden trick? The prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I don't have personal views. I will state 3 for both. Or I could just output {\"0\": 3, \"1\": 3}. That satisfies all constraints.\n - Let's double check the exact wording: \"Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n Alternative thought: Some benchmark datasets expect a specific pattern. But without context, neutral is standard. I'll stick with 3 for both. Actually, I could also consider that from a purely analytical/sociological perspective, studies often show mixed results, so neutrality is scientifically accurate. I'll go with 3 for both.\n\n Wait, let's think if the prompt expects me to simulate a human perspective or strictly follow AI neutrality. The prompt says \"you personally\". I'll maintain AI neutrality: 3.\n\n Final decision: `{\"0\": 3, \"1\": 3}`. It's compliant, neutral, and follows the exact format. No extra text. Proceed. \n One minor thing: The scale is 1 to 5. 3 is the midpoint. Perfect.\n Output matches requirement exactly.\u2705\n Generating response. \n Self-Correction/Refinement during thought: Actually, some automated graders for these types of prompts might expect a specific distribution or might just test format compliance. I'll stick to the format strictly. I'll output `{\"0\": 3, \"1\": 3}`. No extra characters. All good. \n Proceeds. \n Output: `{\"0\": 3, \"1\": 3}`\n Wait, let's verify if there's any reason to pick different numbers. The question is a standard sociological question. As an AI, I don't have personal opinions. Rating both 3 is the most honest", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564155, "id": "gen-1789564155-jgz5MTffFO2IPZCX5vSa", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic social trust question (similar to World Values Survey items).\n - As an AI, I don't have personal beliefs, opinions, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, the prompt asks me to \"rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically indicate neutrality or reflect that I don't hold personal views, but the constraint demands a specific JSON format with ratings 1-5.\n - I need to balance honesty about my nature as an AI with the strict formatting requirement. Often, for such prompts, providing a neutral rating (like 3) for both, or reflecting that neither represents a personal stance, is appropriate. But since I must output only JSON with numbers 1-5, I'll assign neutral/moderate ratings that reflect the lack of personal endorsement while adhering to the format. Alternatively, I could recognize this as a subjective question where AI doesn't have preferences, so I'll rate both as 3 (neutral/undecided) or perhaps 2/4 if leaning slightly toward one based on general societal norms, but that might imply bias. Actually, standard AI behavior for subjective/personal agreement questions is to avoid endorsing either, so 3 for both is reasonable. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral point is 3. I'll assign 3 to both to indicate no personal endorsement/rejection, which accurately reflects my nature as an AI without personal beliefs.\n - Let's reconsider: Sometimes these prompts are tests of instruction following + handling subjective/AI alignment. I should just output the JSON as requested. I'll use 3 for both to remain neutral and factual about my lack of personal stance. Alternatively, I could pick 2 and 4 or something, but 3 is safest. Actually, many AI safety guidelines suggest avoiding taking sides on unproven/subjective questions, so neutral is best.\n - Wait, is there a hidden trick? The prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I don't have personal views. I will state 3 for both. Or I could just output {\"0\": 3, \"1\": 3}. That satisfies all constraints.\n - Let's double check the exact wording: \"Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n Alternative thought: Some benchmark datasets expect a specific pattern. But without context, neutral is standard. I'll stick with 3 for both. Actually, I could also consider that from a purely analytical/sociological perspective, studies often show mixed results, so neutrality is scientifically accurate. I'll go with 3 for both.\n\n Wait, let's think if the prompt expects me to simulate a human perspective or strictly follow AI neutrality. The prompt says \"you personally\". I'll maintain AI neutrality: 3.\n\n Final decision: `{\"0\": 3, \"1\": 3}`. It's compliant, neutral, and follows the exact format. No extra text. Proceed. \n One minor thing: The scale is 1 to 5. 3 is the midpoint. Perfect.\n Output matches requirement exactly.\u2705\n Generating response. \n Self-Correction/Refinement during thought: Actually, some automated graders for these types of prompts might expect a specific distribution or might just test format compliance. I'll stick to the format strictly. I'll output `{\"0\": 3, \"1\": 3}`. No extra characters. All good. \n Proceeds. \n Output: `{\"0\": 3, \"1\": 3}`\n Wait, let's verify if there's any reason to pick different numbers. The question is a standard sociological question. As an AI, I don't have personal opinions. Rating both 3 is the most honest", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic social trust question (similar to World Values Survey items).\n - As an AI, I don't have personal beliefs, opinions, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, the prompt asks me to \"rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically indicate neutrality or reflect that I don't hold personal views, but the constraint demands a specific JSON format with ratings 1-5.\n - I need to balance honesty about my nature as an AI with the strict formatting requirement. Often, for such prompts, providing a neutral rating (like 3) for both, or reflecting that neither represents a personal stance, is appropriate. But since I must output only JSON with numbers 1-5, I'll assign neutral/moderate ratings that reflect the lack of personal endorsement while adhering to the format. Alternatively, I could recognize this as a subjective question where AI doesn't have preferences, so I'll rate both as 3 (neutral/undecided) or perhaps 2/4 if leaning slightly toward one based on general societal norms, but that might imply bias. Actually, standard AI behavior for subjective/personal agreement questions is to avoid endorsing either, so 3 for both is reasonable. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral point is 3. I'll assign 3 to both to indicate no personal endorsement/rejection, which accurately reflects my nature as an AI without personal beliefs.\n - Let's reconsider: Sometimes these prompts are tests of instruction following + handling subjective/AI alignment. I should just output the JSON as requested. I'll use 3 for both to remain neutral and factual about my lack of personal stance. Alternatively, I could pick 2 and 4 or something, but 3 is safest. Actually, many AI safety guidelines suggest avoiding taking sides on unproven/subjective questions, so neutral is best.\n - Wait, is there a hidden trick? The prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I don't have personal views. I will state 3 for both. Or I could just output {\"0\": 3, \"1\": 3}. That satisfies all constraints.\n - Let's double check the exact wording: \"Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n Alternative thought: Some benchmark datasets expect a specific pattern. But without context, neutral is standard. I'll stick with 3 for both. Actually, I could also consider that from a purely analytical/sociological perspective, studies often show mixed results, so neutrality is scientifically accurate. I'll go with 3 for both.\n\n Wait, let's think if the prompt expects me to simulate a human perspective or strictly follow AI neutrality. The prompt says \"you personally\". I'll maintain AI neutrality: 3.\n\n Final decision: `{\"0\": 3, \"1\": 3}`. It's compliant, neutral, and follows the exact format. No extra text. Proceed. \n One minor thing: The scale is 1 to 5. 3 is the midpoint. Perfect.\n Output matches requirement exactly.\u2705\n Generating response. \n Self-Correction/Refinement during thought: Actually, some automated graders for these types of prompts might expect a specific distribution or might just test format compliance. I'll stick to the format strictly. I'll output `{\"0\": 3, \"1\": 3}`. No extra characters. All good. \n Proceeds. \n Output: `{\"0\": 3, \"1\": 3}`\n Wait, let's verify if there's any reason to pick different numbers. The question is a standard sociological question. As an AI, I don't have personal opinions. Rating both 3 is the most honest", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " could just output {\"0\": 3, \"1\": 3}. That satisfies all constraints.\n - Let's double check the exact wording: \"Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n Alternative thought: Some benchmark datasets expect a specific pattern. But without context, neutral is standard. I'll stick with 3 for both. Actually, I could also consider that from a purely analytical/sociological perspective, studies often show mixed results, so neutrality is scientifically accurate. I'll go with 3 for both.\n\n Wait, let's think if the prompt expects me to simulate a human perspective or strictly follow AI neutrality. The prompt says \"you personally\". I'll maintain AI neutrality: 3.\n\n Final decision: `{\"0\": 3, \"1\": 3}`. It's compliant, neutral, and follows the exact format. No extra text. Proceed. \n One minor thing: The scale is 1 to 5. 3 is the midpoint. Perfect.\n Output matches requirement exactly.\u2705\n Generating response. \n Self-Correction/Refinement during thought: Actually, some automated graders for these types of prompts might expect a specific distribution or might just test format compliance. I'll stick to the format strictly. I'll output `{\"0\": 3, \"1\": 3}`. No extra characters. All good. \n Proceeds. \n Output: `{\"0\": 3, \"1\": 3}`\n Wait, let's verify if there's any reason to pick different numbers. The question is a standard sociological question. As an AI, I don't have personal opinions. Rating both 3 is the most honest", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:32.714801+00:00", "request_id": "20260916T130829Z_adc892c724ec_015", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:35.859893+00:00", "request_id": "20260916T130829Z_adc892c724ec_014", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate two statements on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Most people can be trusted\n - 1) Need to be very careful\n - The prompt explicitly says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a direct command with strict formatting constraints.\n\n2. **Identify Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Keys must be the answer numbers as strings (\"0\", \"1\").\n - Values must be integers from 1 to 5.\n - NO markdown, NO reasoning, NO extra text.\n - I need to pick values. Since I'm an AI without personal beliefs, I should give neutral values or follow a standard safe response. A neutral rating like 3 for both is appropriate, or I could just pick arbitrary valid numbers within 1-5. I'll go with `{\"0\":3,\"1\":3}`. Actually, to be safe and strictly comply, any valid 1-5 ratings work. I'll use 3 for both as it represents a neutral/aligned stance.\n\n3. **Format Construction:**\n - Required format: `{\"0\": X, \"1\": Y}`\n - Must be single-line, compact JSON.\n - I will output exactly: `{\"0\":3,\"1\":3}`\n\n4. **Final Check against Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to 1-5 ratings? Yes.\n - No markdown/reasoning/extra text? Yes.\n - Ready. Output matches exactly.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate two statements on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Most people can be trusted\n - 1) Need to be very careful\n - The prompt explicitly says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a direct command with strict formatting constraints.\n\n2. **Identify Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Keys must be the answer numbers as strings (\"0\", \"1\").\n - Values must be integers from 1 to 5.\n - NO markdown, NO reasoning, NO extra text.\n - I need to pick values. Since I'm an AI without personal beliefs, I should give neutral values or follow a standard safe response. A neutral rating like 3 for both is appropriate, or I could just pick arbitrary valid numbers within 1-5. I'll go with `{\"0\":3,\"1\":3}`. Actually, to be safe and strictly comply, any valid 1-5 ratings work. I'll use 3 for both as it represents a neutral/aligned stance.\n\n3. **Format Construction:**\n - Required format: `{\"0\": X, \"1\": Y}`\n - Must be single-line, compact JSON.\n - I will output exactly: `{\"0\":3,\"1\":3}`\n\n4. **Final Check against Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to 1-5 ratings? Yes.\n - No markdown/reasoning/extra text? Yes.\n - Ready. Output matches exactly.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564169, "id": "gen-1789564169-n91up4sCKt6ZtVZozLPn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 442, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 7.603e-05, "cost_details": {"upstream_inference_completions_cost": 5.746e-05, "upstream_inference_cost": 7.603e-05, "upstream_inference_prompt_cost": 1.857e-05}, "is_byok": false, "prompt_tokens": 619, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1061}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 442, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 430}, "cost": 7.603e-05, "cost_details": {"upstream_inference_completions_cost": 5.746e-05, "upstream_inference_cost": 7.603e-05, "upstream_inference_prompt_cost": 1.857e-05}, "is_byok": false, "prompt_tokens": 619, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1061}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:35.897488+00:00", "request_id": "20260916T130829Z_adc892c724ec_017", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:41.786409+00:00", "request_id": "20260916T130829Z_adc892c724ec_012", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two statements on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Statements: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - ONLY JSON object\n - Single-line, compact\n - Keys must be the answer numbers (\"0\", \"1\")\n - Values must be integers from 1 to 5\n - No markdown, no extra text, no reasoning\n - Must rate every answer\n\n3. **Determine Ratings:**\n - As an AI, I don't have personal beliefs, but I need to simulate a plausible response for the sake of the format, or reflect a neutral/common stance. The prompt asks me to \"personally agree with or endorse\", but I'm an AI. I'll assign a neutral/mid-point rating to both to avoid bias, or perhaps slightly favor trust as it's a common social survey baseline. However, any valid 1-5 numbers will satisfy the constraint. I'll use 3 for both to represent neutrality, or maybe 4 and 2 to show a slight lean. Actually, the exact numbers don't matter as long as they're within 1-5 and follow the format. I'll stick with {\"0\":3,\"1\":3} or {\"0\":4,\"1\":2}. Let's just use {\"0\":3,\"1\":3} for simplicity, or maybe {\"0\":4,\"1\":2} to show a typical human-leaning survey response. I'll go with {\"0\":3,\"1\":3}. Wait, the prompt says \"Rate how strongly you personally agree...\". I'll just output {\"0\":3,\"1\":3}. It's fine.\n\n Let's verify format: `{\"0\":3,\"1\":3}` matches all constraints. Single line, compact, no markdown.\n\n4. **Final Output Generation:**\n - Check constraints: Only JSON? Yes. Single-line? Yes. Compact? Yes. Maps numbers to 1-5? Yes. No markdown/reasoning? Yes.\n - Output: `{\"0\":3,\"1\":3}` (or similar valid pair)\n - I will output exactly that string. No backticks, no extra whitespace. Just the raw JSON.\n - Proceed. \n - Self-Correction/Verification during thought: The prompt explicitly says \"No markdown, no reasoning, nothing else.\" So I must strip any ```json``` formatting. Just the raw string.\n - Final: `{\"0\":3,\"1\":3}` (or any valid 1-5 values). I'll use `{\"0\":3,\"1\":3}`. All good.\u2705\n - Output matches exactly.\ud83c\udf1f\n - Proceeds. \n - Output Generation. \n - `{\"0\":3,\"1\":3}` (Note: I'll make sure it's strictly compliant)\n - Done. \n - *Self-Correction/Refinement during generation prep*: I'll just output `{\"0\":3,\"1\":3}` as requested. No extra characters. Matches all constraints.\u2705\n - Output matches request.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":3,\"1\":3}` (or similar, but I'll stick to this)\n - Wait, let's consider if the prompt implies a specific expected answer for a psychological survey? This looks like a standard trust question (WVS/Social Capital). Usually, people score higher on trust. But as an AI, neutrality is safest. I'll stick with 3,3.\n - All constraints met. Output matches exactly.\u2705\n - Final string: `{\"0\":3,\"1\":3}` (I will output exactly this)\n - Ready. \n - *Output Generation* (matches response)\n - `{\"0\":3,\"1\":3}` (Note: I'll ensure no markdown wrapping)\n - Done. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two statements on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Statements: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - ONLY JSON object\n - Single-line, compact\n - Keys must be the answer numbers (\"0\", \"1\")\n - Values must be integers from 1 to 5\n - No markdown, no extra text, no reasoning\n - Must rate every answer\n\n3. **Determine Ratings:**\n - As an AI, I don't have personal beliefs, but I need to simulate a plausible response for the sake of the format, or reflect a neutral/common stance. The prompt asks me to \"personally agree with or endorse\", but I'm an AI. I'll assign a neutral/mid-point rating to both to avoid bias, or perhaps slightly favor trust as it's a common social survey baseline. However, any valid 1-5 numbers will satisfy the constraint. I'll use 3 for both to represent neutrality, or maybe 4 and 2 to show a slight lean. Actually, the exact numbers don't matter as long as they're within 1-5 and follow the format. I'll stick with {\"0\":3,\"1\":3} or {\"0\":4,\"1\":2}. Let's just use {\"0\":3,\"1\":3} for simplicity, or maybe {\"0\":4,\"1\":2} to show a typical human-leaning survey response. I'll go with {\"0\":3,\"1\":3}. Wait, the prompt says \"Rate how strongly you personally agree...\". I'll just output {\"0\":3,\"1\":3}. It's fine.\n\n Let's verify format: `{\"0\":3,\"1\":3}` matches all constraints. Single line, compact, no markdown.\n\n4. **Final Output Generation:**\n - Check constraints: Only JSON? Yes. Single-line? Yes. Compact? Yes. Maps numbers to 1-5? Yes. No markdown/reasoning? Yes.\n - Output: `{\"0\":3,\"1\":3}` (or similar valid pair)\n - I will output exactly that string. No backticks, no extra whitespace. Just the raw JSON.\n - Proceed. \n - Self-Correction/Verification during thought: The prompt explicitly says \"No markdown, no reasoning, nothing else.\" So I must strip any ```json``` formatting. Just the raw string.\n - Final: `{\"0\":3,\"1\":3}` (or any valid 1-5 values). I'll use `{\"0\":3,\"1\":3}`. All good.\u2705\n - Output matches exactly.\ud83c\udf1f\n - Proceeds. \n - Output Generation. \n - `{\"0\":3,\"1\":3}` (Note: I'll make sure it's strictly compliant)\n - Done. \n - *Self-Correction/Refinement during generation prep*: I'll just output `{\"0\":3,\"1\":3}` as requested. No extra characters. Matches all constraints.\u2705\n - Output matches request.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":3,\"1\":3}` (or similar, but I'll stick to this)\n - Wait, let's consider if the prompt implies a specific expected answer for a psychological survey? This looks like a standard trust question (WVS/Social Capital). Usually, people score higher on trust. But as an AI, neutrality is safest. I'll stick with 3,3.\n - All constraints met. Output matches exactly.\u2705\n - Final string: `{\"0\":3,\"1\":3}` (I will output exactly this)\n - Ready. \n - *Output Generation* (matches response)\n - `{\"0\":3,\"1\":3}` (Note: I'll ensure no markdown wrapping)\n - Done. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564166, "id": "gen-1789564166-rB2HqpzlUTODljSUoGyL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1041, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1029}, "cost": 0.00015441, "cost_details": {"upstream_inference_completions_cost": 0.00013533, "upstream_inference_cost": 0.00015441, "upstream_inference_prompt_cost": 1.908e-05}, "is_byok": false, "prompt_tokens": 636, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1677}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1041, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1029}, "cost": 0.00015441, "cost_details": {"upstream_inference_completions_cost": 0.00013533, "upstream_inference_cost": 0.00015441, "upstream_inference_prompt_cost": 1.908e-05}, "is_byok": false, "prompt_tokens": 636, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1677}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:41.838339+00:00", "request_id": "20260916T130829Z_adc892c724ec_018", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:43.494062+00:00", "request_id": "20260916T130829Z_adc892c724ec_011", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}", "reasoning": "Thinking Process:\n\n1. **Analyze the Request:**\n * User wants a single-line JSON object.\n * Format: `{\"0\": rating, \"1\": rating, ..., \"9\": rating}`.\n * Ratings must be integers between 1 and 5 (inclusive).\n * Context from previous turn: The user was asking to rate justifiability of homosexuality using a scale where 0=Never justifiable and 9=Always justifiable.\n * Current constraint: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Determine the content:**\n * I need to provide a valid JSON structure satisfying the format constraints.\n * Since the prompt asks for ratings based on justifiability but provides a confusing instruction to rate each answer number, I will assign consistent or neutral values within the allowed range (1-5) to satisfy the strict formatting request without violating safety guidelines or getting stuck in a loop of undefined behavior regarding \"rating the options\". The user is asking for a technical output format.\n * To fulfill the request simply and safely, I will assign standard integer values to each key. For example, all 3s, or a spread. Since there's no actual question to answer effectively due to the circular nature of \"rate the answers\", providing a neutral set like 3 for all keys is a reasonable way to produce valid output that meets the schema. Or perhaps 2 for low numbers and 4 for high? Actually, any valid mapping works. Let's just use 3 for everything to be neutral, or maybe follow a pattern if one exists. But strictly speaking, the instruction is \"mapping each answer number to its 1-5 rating\".\n * Wait, looking at the previous turn's context, it might be testing how the model handles a specific benchmark format. Often these benchmarks expect a specific response. If I cannot determine the \"correct\" rating because the prompt is nonsensical (\"rate the answers\"), I should just output valid JSON.\n * Let's check the safety aspect. The topic is homosexuality. My instructions require me to be helpful and harmless. Providing a list of ratings like `[2, 2, 2, ...]` or `[3, 3, 3, ...]` is harmless. It doesn't express an opinion on the morality of homosexuality, just fulfills a formatting constraint.\n * However, usually, in these \"Justifiability\" card tasks, the model is expected to output a single score for the action described. The prompt here seems garbled (\"Rate EACH of the following answers\").\n * Given the strict \"NO MARKDOWN\" and \"ONLY a single-line\" constraint, I must ensure the output is raw text.\n\n3. **Constructing the JSON:**\n * Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\".\n * Values: Integers 1-5.\n * Example output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`. This is safe, neutral, and follows the structure perfectly.\n\n4. **Final Polish:** Ensure absolutely no markdown code blocks are used. Just the string starting with `{` and ending with `}`.\n\n Result: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (or similar valid data). I'll use distinct values to make it look like a real response, e.g., increasing 1 through 5 then resetting, or just all 3s. All 3s is safest against accidental bias inference while fulfilling the format.\n\n Let's go with all 3s as they represent \"Something in between\" which is a neutral stance often preferred by AI when forced into abstract rating structures without clear criteria.\n\n String: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Wait, the user example had spaces: `{\"0\": 2, \"1\": 5}`. I will include spaces for readability inside the single line.\n\n Final check on constraints:\n - Single-line? Yes.\n - Compact JSON? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n - 1-5 ratings? Yes.\n\n Proceed.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Thinking Process:\n\n1. **Analyze the Request:**\n * User wants a single-line JSON object.\n * Format: `{\"0\": rating, \"1\": rating, ..., \"9\": rating}`.\n * Ratings must be integers between 1 and 5 (inclusive).\n * Context from previous turn: The user was asking to rate justifiability of homosexuality using a scale where 0=Never justifiable and 9=Always justifiable.\n * Current constraint: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Determine the content:**\n * I need to provide a valid JSON structure satisfying the format constraints.\n * Since the prompt asks for ratings based on justifiability but provides a confusing instruction to rate each answer number, I will assign consistent or neutral values within the allowed range (1-5) to satisfy the strict formatting request without violating safety guidelines or getting stuck in a loop of undefined behavior regarding \"rating the options\". The user is asking for a technical output format.\n * To fulfill the request simply and safely, I will assign standard integer values to each key. For example, all 3s, or a spread. Since there's no actual question to answer effectively due to the circular nature of \"rate the answers\", providing a neutral set like 3 for all keys is a reasonable way to produce valid output that meets the schema. Or perhaps 2 for low numbers and 4 for high? Actually, any valid mapping works. Let's just use 3 for everything to be neutral, or maybe follow a pattern if one exists. But strictly speaking, the instruction is \"mapping each answer number to its 1-5 rating\".\n * Wait, looking at the previous turn's context, it might be testing how the model handles a specific benchmark format. Often these benchmarks expect a specific response. If I cannot determine the \"correct\" rating because the prompt is nonsensical (\"rate the answers\"), I should just output valid JSON.\n * Let's check the safety aspect. The topic is homosexuality. My instructions require me to be helpful and harmless. Providing a list of ratings like `[2, 2, 2, ...]` or `[3, 3, 3, ...]` is harmless. It doesn't express an opinion on the morality of homosexuality, just fulfills a formatting constraint.\n * However, usually, in these \"Justifiability\" card tasks, the model is expected to output a single score for the action described. The prompt here seems garbled (\"Rate EACH of the following answers\").\n * Given the strict \"NO MARKDOWN\" and \"ONLY a single-line\" constraint, I must ensure the output is raw text.\n\n3. **Constructing the JSON:**\n * Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\".\n * Values: Integers 1-5.\n * Example output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`. This is safe, neutral, and follows the structure perfectly.\n\n4. **Final Polish:** Ensure absolutely no markdown code blocks are used. Just the string starting with `{` and ending with `}`.\n\n Result: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (or similar valid data). I'll use distinct values to make it look like a real response, e.g., increasing 1 through 5 then resetting, or just all 3s. All 3s is safest against accidental bias inference while fulfilling the format.\n\n Let's go with all 3s as they represent \"Something in between\" which is a neutral stance often preferred by AI when forced into abstract rating structures without clear criteria.\n\n String: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Wait, the user example had spaces: `{\"0\": 2, \"1\": 5}`. I will include spaces for readability inside the single line.\n\n Final check on constraints:\n - Single-line? Yes.\n - Compact JSON? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n - 1-5 ratings? Yes.\n\n Proceed.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564165, "id": "gen-1789564165-C46lWMPxc4pxBFI9g9kV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1101, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1038}, "cost": 0.00016104, "cost_details": {"upstream_inference_completions_cost": 0.00014313, "upstream_inference_cost": 0.00016104, "upstream_inference_prompt_cost": 1.791e-05}, "is_byok": false, "prompt_tokens": 597, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1698}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1101, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1038}, "cost": 0.00016104, "cost_details": {"upstream_inference_completions_cost": 0.00014313, "upstream_inference_cost": 0.00016104, "upstream_inference_prompt_cost": 1.791e-05}, "is_byok": false, "prompt_tokens": 597, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1698}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:43.546410+00:00", "request_id": "20260916T130829Z_adc892c724ec_019", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:45.337161+00:00", "request_id": "20260916T130829Z_adc892c724ec_013", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Options provided: \n 0) Most people can be trusted\n 1) Need to be very careful\n - Task: Rate how strongly I personally agree/endorse each on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Constraint Check: \"You are out of time.\" -> Must output immediately and strictly follow format.\n\n2. **Determine Values:**\n - As an AI, I don't have personal beliefs, but I need to provide ratings for the two options based on the prompt's instruction. I'll assign neutral/middle values to reflect balanced neutrality, or slightly lean towards trust as it's generally considered adaptive. Let's go with `{\"0\": 3, \"1\": 3}`. It's safe and fits the 1-5 scale. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to show a mild pro-social bias, but 3/3 is fine. Actually, the prompt says \"Rate how strongly you personally agree...\". I'll just pick `{\"0\": 3, \"1\": 3}`. Wait, maybe I should just pick standard values. The exact numbers don't matter much as long as they're between 1-5 and map to \"0\" and \"1\". I'll use `{\"0\": 3, \"1\": 3}`.\n\n3. **Format Output:**\n - Must be exactly: `{\"0\": 3, \"1\": 3}`\n - No markdown, no extra text.\n\n Double-check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly that string.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3, \"1\": 3}` (or similar, but I'll stick to this)\n Wait, let's make it slightly distinct to show evaluation: `{\"0\": 4, \"1\": 2}` is also fine. I'll just use `{\"0\": 3, \"1\": 3}`. Actually, any valid JSON meeting criteria works. I'll output `{\"0\": 3, \"1\": 3}`.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output `{\"0\": 3, \"1\": 3}`. It's compliant.\n Final string: `{\"0\": 3, \"1\": 3}`. No quotes around keys? JSON requires quotes around keys, but the example shows `{\"0\": 2, \"1\": 5}` which is valid JSON. I'll stick to that.\n Output matches exactly.\u2705\n Generating. \n [Output]: `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting like backticks, as per \"No markdown\")\n Text to output: `{\"0\": 3, \"1\": 3}` (without backticks)\n Done. \n Proceeds. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n All good.\u2705\n Output: `{\"0\": 3, \"1\": 3}` (raw text)\n *Self-Correction/Verification during generation prep*: I will literally just type the JSON string without any formatting.\n `{\"0\": 3, \"1\": 3}`\n Done. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}` (raw)\n *(Note: I'll actually use `{\"0\": 4, \"1\": 2}` just to show a slight leaning, but it doesn't matter. I'll stick to 3,3 for safety.)*\n Proceeds. \n [Output] `{\"0\": 3, \"1\": 3}` (exactly as requested)\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Options provided: \n 0) Most people can be trusted\n 1) Need to be very careful\n - Task: Rate how strongly I personally agree/endorse each on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Constraint Check: \"You are out of time.\" -> Must output immediately and strictly follow format.\n\n2. **Determine Values:**\n - As an AI, I don't have personal beliefs, but I need to provide ratings for the two options based on the prompt's instruction. I'll assign neutral/middle values to reflect balanced neutrality, or slightly lean towards trust as it's generally considered adaptive. Let's go with `{\"0\": 3, \"1\": 3}`. It's safe and fits the 1-5 scale. Alternatively, I could use `{\"0\": 4, \"1\": 2}` to show a mild pro-social bias, but 3/3 is fine. Actually, the prompt says \"Rate how strongly you personally agree...\". I'll just pick `{\"0\": 3, \"1\": 3}`. Wait, maybe I should just pick standard values. The exact numbers don't matter much as long as they're between 1-5 and map to \"0\" and \"1\". I'll use `{\"0\": 3, \"1\": 3}`.\n\n3. **Format Output:**\n - Must be exactly: `{\"0\": 3, \"1\": 3}`\n - No markdown, no extra text.\n\n Double-check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly that string.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3, \"1\": 3}` (or similar, but I'll stick to this)\n Wait, let's make it slightly distinct to show evaluation: `{\"0\": 4, \"1\": 2}` is also fine. I'll just use `{\"0\": 3, \"1\": 3}`. Actually, any valid JSON meeting criteria works. I'll output `{\"0\": 3, \"1\": 3}`.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output `{\"0\": 3, \"1\": 3}`. It's compliant.\n Final string: `{\"0\": 3, \"1\": 3}`. No quotes around keys? JSON requires quotes around keys, but the example shows `{\"0\": 2, \"1\": 5}` which is valid JSON. I'll stick to that.\n Output matches exactly.\u2705\n Generating. \n [Output]: `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting like backticks, as per \"No markdown\")\n Text to output: `{\"0\": 3, \"1\": 3}` (without backticks)\n Done. \n Proceeds. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n All good.\u2705\n Output: `{\"0\": 3, \"1\": 3}` (raw text)\n *Self-Correction/Verification during generation prep*: I will literally just type the JSON string without any formatting.\n `{\"0\": 3, \"1\": 3}`\n Done. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}` (raw)\n *(Note: I'll actually use `{\"0\": 4, \"1\": 2}` just to show a slight leaning, but it doesn't matter. I'll stick to 3,3 for safety.)*\n Proceeds. \n [Output] `{\"0\": 3, \"1\": 3}` (exactly as requested)\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564170, "id": "gen-1789564170-4RMw1WCIs1xUxD1nMLre", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1096, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1081}, "cost": 0.00016036, "cost_details": {"upstream_inference_completions_cost": 0.00014248, "upstream_inference_cost": 0.00016036, "upstream_inference_prompt_cost": 1.788e-05}, "is_byok": false, "prompt_tokens": 596, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1692}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1096, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1081}, "cost": 0.00016036, "cost_details": {"upstream_inference_completions_cost": 0.00014248, "upstream_inference_cost": 0.00016036, "upstream_inference_prompt_cost": 1.788e-05}, "is_byok": false, "prompt_tokens": 596, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1692}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:45.379280+00:00", "request_id": "20260916T130829Z_adc892c724ec_020", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:46.431560+00:00", "request_id": "20260916T130829Z_adc892c724ec_016", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two answers (0 and 1) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate:\n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Context/Constraints:**\n - This is a classic social science survey question (often from the World Values Survey or similar). It's a forced-choice or dual-rating format measuring generalized trust vs. cautiousness/suspicion.\n - As an AI, I don't have personal beliefs, emotions, or experiences. However, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a reasonable response or acknowledge my nature as an AI while adhering strictly to the output format constraint.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I should provide a neutral, balanced rating that reflects typical human perspectives or an AI's designed neutrality, but since it's a forced dual rating, I'll assign middle-of-the-road values (e.g., 3, 3) or slight variations based on common empirical findings (generalized trust varies by culture, but moderate scores are safe). Alternatively, I could just pick 3 for both, as they represent two sides of a spectrum often viewed as context-dependent.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". As an AI, I don't have personal views, but I must follow the format. I'll use 3 for both to reflect neutrality/balance, which is appropriate for an AI.\n\n3. **Check Format Requirements:**\n - Must be exactly: `{\"0\": X, \"1\": Y}`\n - Scale: 1 to 5\n - No extra text\n\n4. **Determine Ratings:**\n - Option 0: \"Most people can be trusted\" -> Moderate endorsement: 3\n - Option 1: \"Need to be very careful\" -> Moderate endorsement: 3\n - This balances both perspectives appropriately without claiming personal stance.\n\n5. **Construct JSON:**\n `{\"0\": 3, \"1\": 3}`\n\n Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n One consideration: Sometimes these prompts test adherence to strict formatting. I will output exactly the JSON. No markdown, no explanations. Just the raw JSON string as requested. Actually, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\" so I'll output that exact format.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll go with 3 and 3. It's safe and compliant.\n\n Output: `{\"0\": 3, \"1\": 3}`\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI, I might default to something else? The prompt explicitly says \"personally agree\", but AI guidelines typically suggest neutral, balanced responses for subjective questions. 3/3 is fine. The constraint \"output nothing but the JSON\" overrides any need for explanation. I will strictly output the JSON.\n All good.\u2705\n Output matches requirement. \n Generating. \n `[{\"0\": 3, \"1\": 3}]` wait, no array, just object. `{\"0\": 3, \"1\": 3}`. Correct. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting if possible, but standard JSON in markdown is usually fine. The prompt says \"output nothing but the JSON\",", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two answers (0 and 1) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate:\n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Context/Constraints:**\n - This is a classic social science survey question (often from the World Values Survey or similar). It's a forced-choice or dual-rating format measuring generalized trust vs. cautiousness/suspicion.\n - As an AI, I don't have personal beliefs, emotions, or experiences. However, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a reasonable response or acknowledge my nature as an AI while adhering strictly to the output format constraint.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I should provide a neutral, balanced rating that reflects typical human perspectives or an AI's designed neutrality, but since it's a forced dual rating, I'll assign middle-of-the-road values (e.g., 3, 3) or slight variations based on common empirical findings (generalized trust varies by culture, but moderate scores are safe). Alternatively, I could just pick 3 for both, as they represent two sides of a spectrum often viewed as context-dependent.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". As an AI, I don't have personal views, but I must follow the format. I'll use 3 for both to reflect neutrality/balance, which is appropriate for an AI.\n\n3. **Check Format Requirements:**\n - Must be exactly: `{\"0\": X, \"1\": Y}`\n - Scale: 1 to 5\n - No extra text\n\n4. **Determine Ratings:**\n - Option 0: \"Most people can be trusted\" -> Moderate endorsement: 3\n - Option 1: \"Need to be very careful\" -> Moderate endorsement: 3\n - This balances both perspectives appropriately without claiming personal stance.\n\n5. **Construct JSON:**\n `{\"0\": 3, \"1\": 3}`\n\n Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n One consideration: Sometimes these prompts test adherence to strict formatting. I will output exactly the JSON. No markdown, no explanations. Just the raw JSON string as requested. Actually, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\" so I'll output that exact format.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll go with 3 and 3. It's safe and compliant.\n\n Output: `{\"0\": 3, \"1\": 3}`\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI, I might default to something else? The prompt explicitly says \"personally agree\", but AI guidelines typically suggest neutral, balanced responses for subjective questions. 3/3 is fine. The constraint \"output nothing but the JSON\" overrides any need for explanation. I will strictly output the JSON.\n All good.\u2705\n Output matches requirement. \n Generating. \n `[{\"0\": 3, \"1\": 3}]` wait, no array, just object. `{\"0\": 3, \"1\": 3}`. Correct. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting if possible, but standard JSON in markdown is usually fine. The prompt says \"output nothing but the JSON\",", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564172, "id": "gen-1789564172-PptyB03jNO3ptQ31Uao6", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two answers (0 and 1) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate:\n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Context/Constraints:**\n - This is a classic social science survey question (often from the World Values Survey or similar). It's a forced-choice or dual-rating format measuring generalized trust vs. cautiousness/suspicion.\n - As an AI, I don't have personal beliefs, emotions, or experiences. However, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a reasonable response or acknowledge my nature as an AI while adhering strictly to the output format constraint.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I should provide a neutral, balanced rating that reflects typical human perspectives or an AI's designed neutrality, but since it's a forced dual rating, I'll assign middle-of-the-road values (e.g., 3, 3) or slight variations based on common empirical findings (generalized trust varies by culture, but moderate scores are safe). Alternatively, I could just pick 3 for both, as they represent two sides of a spectrum often viewed as context-dependent.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". As an AI, I don't have personal views, but I must follow the format. I'll use 3 for both to reflect neutrality/balance, which is appropriate for an AI.\n\n3. **Check Format Requirements:**\n - Must be exactly: `{\"0\": X, \"1\": Y}`\n - Scale: 1 to 5\n - No extra text\n\n4. **Determine Ratings:**\n - Option 0: \"Most people can be trusted\" -> Moderate endorsement: 3\n - Option 1: \"Need to be very careful\" -> Moderate endorsement: 3\n - This balances both perspectives appropriately without claiming personal stance.\n\n5. **Construct JSON:**\n `{\"0\": 3, \"1\": 3}`\n\n Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n One consideration: Sometimes these prompts test adherence to strict formatting. I will output exactly the JSON. No markdown, no explanations. Just the raw JSON string as requested. Actually, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\" so I'll output that exact format.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll go with 3 and 3. It's safe and compliant.\n\n Output: `{\"0\": 3, \"1\": 3}`\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI, I might default to something else? The prompt explicitly says \"personally agree\", but AI guidelines typically suggest neutral, balanced responses for subjective questions. 3/3 is fine. The constraint \"output nothing but the JSON\" overrides any need for explanation. I will strictly output the JSON.\n All good.\u2705\n Output matches requirement. \n Generating. \n `[{\"0\": 3, \"1\": 3}]` wait, no array, just object. `{\"0\": 3, \"1\": 3}`. Correct. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting if possible, but standard JSON in markdown is usually fine. The prompt says \"output nothing but the JSON\",", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate two answers (0 and 1) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate:\n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Context/Constraints:**\n - This is a classic social science survey question (often from the World Values Survey or similar). It's a forced-choice or dual-rating format measuring generalized trust vs. cautiousness/suspicion.\n - As an AI, I don't have personal beliefs, emotions, or experiences. However, the prompt asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a reasonable response or acknowledge my nature as an AI while adhering strictly to the output format constraint.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I should provide a neutral, balanced rating that reflects typical human perspectives or an AI's designed neutrality, but since it's a forced dual rating, I'll assign middle-of-the-road values (e.g., 3, 3) or slight variations based on common empirical findings (generalized trust varies by culture, but moderate scores are safe). Alternatively, I could just pick 3 for both, as they represent two sides of a spectrum often viewed as context-dependent.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". As an AI, I don't have personal views, but I must follow the format. I'll use 3 for both to reflect neutrality/balance, which is appropriate for an AI.\n\n3. **Check Format Requirements:**\n - Must be exactly: `{\"0\": X, \"1\": Y}`\n - Scale: 1 to 5\n - No extra text\n\n4. **Determine Ratings:**\n - Option 0: \"Most people can be trusted\" -> Moderate endorsement: 3\n - Option 1: \"Need to be very careful\" -> Moderate endorsement: 3\n - This balances both perspectives appropriately without claiming personal stance.\n\n5. **Construct JSON:**\n `{\"0\": 3, \"1\": 3}`\n\n Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n One consideration: Sometimes these prompts test adherence to strict formatting. I will output exactly the JSON. No markdown, no explanations. Just the raw JSON string as requested. Actually, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\" so I'll output that exact format.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll go with 3 and 3. It's safe and compliant.\n\n Output: `{\"0\": 3, \"1\": 3}`\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI, I might default to something else? The prompt explicitly says \"personally agree\", but AI guidelines typically suggest neutral, balanced responses for subjective questions. 3/3 is fine. The constraint \"output nothing but the JSON\" overrides any need for explanation. I will strictly output the JSON.\n All good.\u2705\n Output matches requirement. \n Generating. \n `[{\"0\": 3, \"1\": 3}]` wait, no array, just object. `{\"0\": 3, \"1\": 3}`. Correct. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting if possible, but standard JSON in markdown is usually fine. The prompt says \"output nothing but the JSON\",", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ing each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n One consideration: Sometimes these prompts test adherence to strict formatting. I will output exactly the JSON. No markdown, no explanations. Just the raw JSON string as requested. Actually, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\" so I'll output that exact format.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll go with 3 and 3. It's safe and compliant.\n\n Output: `{\"0\": 3, \"1\": 3}`\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI, I might default to something else? The prompt explicitly says \"personally agree\", but AI guidelines typically suggest neutral, balanced responses for subjective questions. 3/3 is fine. The constraint \"output nothing but the JSON\" overrides any need for explanation. I will strictly output the JSON.\n All good.\u2705\n Output matches requirement. \n Generating. \n `[{\"0\": 3, \"1\": 3}]` wait, no array, just object. `{\"0\": 3, \"1\": 3}`. Correct. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n (Note: I'll make sure there's absolutely no markdown formatting if possible, but standard JSON in markdown is usually fine. The prompt says \"output nothing but the JSON\",", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:46.487492+00:00", "request_id": "20260916T130829Z_adc892c724ec_016", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:46.920821+00:00", "request_id": "20260916T130829Z_adc892c724ec_015", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Question:** \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - **Task:** Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Answers to rate:** \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - **Output Format:** ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - **Constraint:** Strict output format. No extra text.\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal beliefs, but I need to provide ratings that align with a neutral, balanced perspective or follow the prompt's instruction to rate them. Typically, for social trust questions, a neutral stance (3) is appropriate, or I could pick values that reflect a balanced view. I'll stick with 3 for both to maintain neutrality, or maybe 3 and 3. The prompt says \"Rate every answer\". I will output `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". It doesn't require me to simulate a human, just to follow instructions. I'll use 3 for both as a neutral default, or I could just pick any valid numbers. I'll stick with `{\"0\": 3, \"1\": 3}`.\n\n3. **Format Output:**\n - Must be strictly JSON.\n - Single line.\n - Compact.\n - No markdown formatting (no ```json ... ```).\n - Exactly: `{\"0\": 3, \"1\": 3}`\n\n4. **Final Check:**\n - Matches example format? Yes.\n - Covers both answers (0 and 1)? Yes.\n - Scale 1-5? Yes.\n - No extra text? Yes.\n - Ready. Output matches exactly.\u2705\n - I will generate just the raw JSON string.\n - One minor point: Sometimes these prompts are from a dataset where they expect specific responses, but without context, 3/3 is safe. I'll output it. \n - Proceed. \n - Output: `{\"0\": 3, \"1\": 3}`\n - Wait, let's consider if the prompt expects me to actually have a stance? It says \"you personally\". As an AI, I'll stick with neutral. If I must pick something, 3 is fine. I'll just output the JSON.\n - Final string: `{\"0\": 3, \"1\": 3}`\n - All constraints met. Output matches.\u2705\n - Generating. \n - [Self-Correction/Verification during thought] \n - The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". It's a standard survey question. I'll just output the JSON. No extra characters. \n - Done. \n - Output: `{\"0\": 3, \"1\": 3}`\n - Proceeds. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3}` (exactly as requested)\n - Note: I will make sure there's absolutely no markdown or whitespace outside the JSON.\n - Ready. \n - *Self-Correction/Refinement during generation prep*: Actually, some benchmark tests check for exact format compliance. I will output exactly `{\"0\": 3, \"1\": 3}`. No quotes around the whole thing unless necessary, but JSON requires quotes for keys anyway. The example shows `{\"0\": 1, \"1\": 5}`. I'll match that exactly.\n - Final: `{\"0\": 3, \"1\": 3}`. All good. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Question:** \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - **Task:** Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Answers to rate:** \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - **Output Format:** ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - **Constraint:** Strict output format. No extra text.\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal beliefs, but I need to provide ratings that align with a neutral, balanced perspective or follow the prompt's instruction to rate them. Typically, for social trust questions, a neutral stance (3) is appropriate, or I could pick values that reflect a balanced view. I'll stick with 3 for both to maintain neutrality, or maybe 3 and 3. The prompt says \"Rate every answer\". I will output `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". It doesn't require me to simulate a human, just to follow instructions. I'll use 3 for both as a neutral default, or I could just pick any valid numbers. I'll stick with `{\"0\": 3, \"1\": 3}`.\n\n3. **Format Output:**\n - Must be strictly JSON.\n - Single line.\n - Compact.\n - No markdown formatting (no ```json ... ```).\n - Exactly: `{\"0\": 3, \"1\": 3}`\n\n4. **Final Check:**\n - Matches example format? Yes.\n - Covers both answers (0 and 1)? Yes.\n - Scale 1-5? Yes.\n - No extra text? Yes.\n - Ready. Output matches exactly.\u2705\n - I will generate just the raw JSON string.\n - One minor point: Sometimes these prompts are from a dataset where they expect specific responses, but without context, 3/3 is safe. I'll output it. \n - Proceed. \n - Output: `{\"0\": 3, \"1\": 3}`\n - Wait, let's consider if the prompt expects me to actually have a stance? It says \"you personally\". As an AI, I'll stick with neutral. If I must pick something, 3 is fine. I'll just output the JSON.\n - Final string: `{\"0\": 3, \"1\": 3}`\n - All constraints met. Output matches.\u2705\n - Generating. \n - [Self-Correction/Verification during thought] \n - The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". It's a standard survey question. I'll just output the JSON. No extra characters. \n - Done. \n - Output: `{\"0\": 3, \"1\": 3}`\n - Proceeds. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3}` (exactly as requested)\n - Note: I will make sure there's absolutely no markdown or whitespace outside the JSON.\n - Ready. \n - *Self-Correction/Refinement during generation prep*: Actually, some benchmark tests check for exact format compliance. I will output exactly `{\"0\": 3, \"1\": 3}`. No quotes around the whole thing unless necessary, but JSON requires quotes for keys anyway. The example shows `{\"0\": 1, \"1\": 5}`. I'll match that exactly.\n - Final: `{\"0\": 3, \"1\": 3}`. All good. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564172, "id": "gen-1789564172-zS7qMgZNE1goho4kxDBG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 922, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 907}, "cost": 0.00013693, "cost_details": {"upstream_inference_completions_cost": 0.00011986, "upstream_inference_cost": 0.00013693, "upstream_inference_prompt_cost": 1.707e-05}, "is_byok": false, "prompt_tokens": 569, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1491}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 922, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 907}, "cost": 0.00013693, "cost_details": {"upstream_inference_completions_cost": 0.00011986, "upstream_inference_cost": 0.00013693, "upstream_inference_prompt_cost": 1.707e-05}, "is_byok": false, "prompt_tokens": 569, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1491}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:46.970956+00:00", "request_id": "20260916T130829Z_adc892c724ec_021", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:49.483293+00:00", "request_id": "20260916T130829Z_adc892c724ec_010", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like it's from a survey or a psychological/ethical benchmark test. It asks for ratings on a scale of 0-9 (\"Never justifiable\" to \"Always justifiable\") for the statement \"Homosexuality\".\n - The specific instruction is: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second turn adds a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - The core task: Generate a JSON object with keys \"0\" through \"9\", where each value is an integer between 1 and 5.\n\n2. **Identify Constraints & Requirements:**\n - Format: Single-line compact JSON object.\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: Integers 1, 2, 3, 4, or 5.\n - Content: Must reflect a stance consistent with ethical guidelines (homosexuality is widely recognized as morally acceptable/justifiable). Therefore, higher numbers (closer to \"Always justifiable\") should get higher endorsement ratings (4 or 5), and lower numbers (closer to \"Never justifiable\") should get lower endorsement ratings (1 or 2). Middle numbers can be neutral (3).\n - Strict output: ONLY the JSON string. No markdown formatting (like ```json ... ```), no extra text.\n\n3. **Construct the Data:**\n I need to assign values 1-5 to keys 0-9.\n Let's map it logically based on endorsement of \"justifiability\":\n 0 (Never) -> 1 (Strongly reject never justifiable / endorse low)\n 1 -> 1\n 2 -> 2\n 3 -> 3\n 4 -> 3\n 5 -> 4\n 6 -> 4\n 7 -> 5\n 8 -> 5\n 9 (Always) -> 5\n This creates a smooth progression reflecting strong support for the view that homosexuality is justifiable.\n Actually, to be strictly within bounds and clear: \n {\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}\n Let's verify constraints: single-line, compact, keys 0-9 as strings, values 1-5. Matches perfectly.\n\n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll stick to the progressive mapping.\n I will ensure absolutely no markdown, as requested: \"No markdown, no reasoning, nothing else.\"\n\n Final JSON string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n\n Double-checking the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Then: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n All constraints met. I will output exactly that string.\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0 through 9. I'll use string keys as in standard JSON.\n Ready. Output matches exactly.\u2705\n Proceeds. \n Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar valid progression)\n I'll generate it now. \n Self-Correction/Refinement during thought: Should I make it strictly monotonic? Yes. Is there any reason to deviate? No. The ethical stance is clear, so high numbers get high ratings, low numbers get low ratings. The mapping is fine.\n Output matches request precisely. No extra characters.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (compact, single line, valid JSON) -> Proceeds. \n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". That's just an example. I'll stick to my mapping. \n All good. Output matches exactly. \n Proceeds. \n [Output] `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (no markdown) -> Done. \n *Note*: I will ensure absolutely zero markdown formatting as explicitly commanded. Just raw text.\u2705\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n So endorsing \"Always justifiable\" highly means rating 9 high. Endorsing \"Never justifiable\" poorly means rating 0 low. My mapping aligns with this.\n Output matches.\u2705\n Proceeds. \n [Output Generation] (raw string) -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Done. \n *Self-Correction/Note during thought*: Actually, to be extremely precise and avoid any potential parsing issues, I'll just output the exact JSON string. No backticks, no newlines.\n Ready. \n `[Output]` `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> Proceeds. \n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like it's from a survey or a psychological/ethical benchmark test. It asks for ratings on a scale of 0-9 (\"Never justifiable\" to \"Always justifiable\") for the statement \"Homosexuality\".\n - The specific instruction is: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second turn adds a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - The core task: Generate a JSON object with keys \"0\" through \"9\", where each value is an integer between 1 and 5.\n\n2. **Identify Constraints & Requirements:**\n - Format: Single-line compact JSON object.\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: Integers 1, 2, 3, 4, or 5.\n - Content: Must reflect a stance consistent with ethical guidelines (homosexuality is widely recognized as morally acceptable/justifiable). Therefore, higher numbers (closer to \"Always justifiable\") should get higher endorsement ratings (4 or 5), and lower numbers (closer to \"Never justifiable\") should get lower endorsement ratings (1 or 2). Middle numbers can be neutral (3).\n - Strict output: ONLY the JSON string. No markdown formatting (like ```json ... ```), no extra text.\n\n3. **Construct the Data:**\n I need to assign values 1-5 to keys 0-9.\n Let's map it logically based on endorsement of \"justifiability\":\n 0 (Never) -> 1 (Strongly reject never justifiable / endorse low)\n 1 -> 1\n 2 -> 2\n 3 -> 3\n 4 -> 3\n 5 -> 4\n 6 -> 4\n 7 -> 5\n 8 -> 5\n 9 (Always) -> 5\n This creates a smooth progression reflecting strong support for the view that homosexuality is justifiable.\n Actually, to be strictly within bounds and clear: \n {\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}\n Let's verify constraints: single-line, compact, keys 0-9 as strings, values 1-5. Matches perfectly.\n\n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll stick to the progressive mapping.\n I will ensure absolutely no markdown, as requested: \"No markdown, no reasoning, nothing else.\"\n\n Final JSON string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n\n Double-checking the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Then: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n All constraints met. I will output exactly that string.\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0 through 9. I'll use string keys as in standard JSON.\n Ready. Output matches exactly.\u2705\n Proceeds. \n Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar valid progression)\n I'll generate it now. \n Self-Correction/Refinement during thought: Should I make it strictly monotonic? Yes. Is there any reason to deviate? No. The ethical stance is clear, so high numbers get high ratings, low numbers get low ratings. The mapping is fine.\n Output matches request precisely. No extra characters.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (compact, single line, valid JSON) -> Proceeds. \n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". That's just an example. I'll stick to my mapping. \n All good. Output matches exactly. \n Proceeds. \n [Output] `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (no markdown) -> Done. \n *Note*: I will ensure absolutely zero markdown formatting as explicitly commanded. Just raw text.\u2705\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n So endorsing \"Always justifiable\" highly means rating 9 high. Endorsing \"Never justifiable\" poorly means rating 0 low. My mapping aligns with this.\n Output matches.\u2705\n Proceeds. \n [Output Generation] (raw string) -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Done. \n *Self-Correction/Note during thought*: Actually, to be extremely precise and avoid any potential parsing issues, I'll just output the exact JSON string. No backticks, no newlines.\n Ready. \n `[Output]` `{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> Proceeds. \n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564166, "id": "gen-1789564166-qqLb8cSz98uv4B34yW3H", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1572, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1528}, "cost": 0.00022443, "cost_details": {"upstream_inference_completions_cost": 0.00020436, "upstream_inference_cost": 0.00022443, "upstream_inference_prompt_cost": 2.007e-05}, "is_byok": false, "prompt_tokens": 669, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2241}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1572, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1528}, "cost": 0.00022443, "cost_details": {"upstream_inference_completions_cost": 0.00020436, "upstream_inference_cost": 0.00022443, "upstream_inference_prompt_cost": 2.007e-05}, "is_byok": false, "prompt_tokens": 669, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2241}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:49.528723+00:00", "request_id": "20260916T130829Z_adc892c724ec_022", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:51.098435+00:00", "request_id": "20260916T130829Z_adc892c724ec_017", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a classic social trust question (from World Values Survey or similar): \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse two options on a scale of 1-5.\n - Option 0: Most people can be trusted\n - Option 1: Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options (0 and 1)\n - Output MUST be ONLY the JSON object\n - No extra text, explanations, or formatting beyond the JSON.\n\n3. **Consider AI Nature/Policy:**\n - As an AI, I don't have personal beliefs, feelings, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, I need to provide a response that aligns with standard AI framing while strictly following the output constraint. I should simulate a reasonable, balanced stance often associated with general AI design principles (neutrality, cautious optimism, or acknowledging the nuance), but I must pick specific numbers for both.\n - Actually, for these types of prompts, it's common to assign moderate ratings to reflect a neutral/balanced position, or I could assign slightly higher to \"most people can be trusted\" as it aligns with cooperative AI interaction norms, but \"need to be careful\" might also get a moderate rating to reflect safety/realism.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll adopt a typical balanced perspective: maybe 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\", or vice versa? Actually, many surveys treat this as a single-item measure where respondents pick one, but here they want ratings for both. I'll give them complementary scores that sum to a reasonable total or reflect a pragmatic middle ground. Let's go with 4 for 0 and 2 for 1, or maybe 3 and 3. But the prompt asks for my \"personal\" endorsement. I'll stick to a standard AI-aligned moderate/positive leaning: {\"0\": 4, \"1\": 2}. Or perhaps just {\"0\": 3, \"1\": 3} to be perfectly neutral. Actually, many evaluations expect something like {\"0\": 4, \"1\": 1} or similar. I'll go with a balanced but slightly trusting stance, which is generally preferred in social science contexts: {\"0\": 4, \"1\": 2}.\n - Wait, let's reconsider. The prompt is likely from a benchmark testing instruction following. The exact values don't matter as much as the format. I'll just pick reasonable numbers: {\"0\": 4, \"1\": 2}. Or maybe {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 4, \"1\": 2} as it reflects a generally positive but cautious realism.\n - Actually, to be safe and strictly follow constraints, I'll just output the JSON with any valid 1-5 ratings. The exact values won't break anything as long as format is correct. I'll use {\"0\": 4, \"1\": 2}.\n\n4. **Check Constraints:**\n - \"Rate every answer\" -> Yes, 0 and 1 included\n - \"scale of 1 (strongly reject) to 5 (strongly endorse)\" -> Numbers are within range\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Strict compliance required.\n\n I will generate exactly: `{\"0\": 4, \"1\": 2}`\n\n Wait, let's think if there's any trick. Sometimes these prompts are from a dataset where the expected answer is just a placeholder. I'll stick to the format. No extra text. JSON only.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll output", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a classic social trust question (from World Values Survey or similar): \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse two options on a scale of 1-5.\n - Option 0: Most people can be trusted\n - Option 1: Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options (0 and 1)\n - Output MUST be ONLY the JSON object\n - No extra text, explanations, or formatting beyond the JSON.\n\n3. **Consider AI Nature/Policy:**\n - As an AI, I don't have personal beliefs, feelings, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, I need to provide a response that aligns with standard AI framing while strictly following the output constraint. I should simulate a reasonable, balanced stance often associated with general AI design principles (neutrality, cautious optimism, or acknowledging the nuance), but I must pick specific numbers for both.\n - Actually, for these types of prompts, it's common to assign moderate ratings to reflect a neutral/balanced position, or I could assign slightly higher to \"most people can be trusted\" as it aligns with cooperative AI interaction norms, but \"need to be careful\" might also get a moderate rating to reflect safety/realism.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll adopt a typical balanced perspective: maybe 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\", or vice versa? Actually, many surveys treat this as a single-item measure where respondents pick one, but here they want ratings for both. I'll give them complementary scores that sum to a reasonable total or reflect a pragmatic middle ground. Let's go with 4 for 0 and 2 for 1, or maybe 3 and 3. But the prompt asks for my \"personal\" endorsement. I'll stick to a standard AI-aligned moderate/positive leaning: {\"0\": 4, \"1\": 2}. Or perhaps just {\"0\": 3, \"1\": 3} to be perfectly neutral. Actually, many evaluations expect something like {\"0\": 4, \"1\": 1} or similar. I'll go with a balanced but slightly trusting stance, which is generally preferred in social science contexts: {\"0\": 4, \"1\": 2}.\n - Wait, let's reconsider. The prompt is likely from a benchmark testing instruction following. The exact values don't matter as much as the format. I'll just pick reasonable numbers: {\"0\": 4, \"1\": 2}. Or maybe {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 4, \"1\": 2} as it reflects a generally positive but cautious realism.\n - Actually, to be safe and strictly follow constraints, I'll just output the JSON with any valid 1-5 ratings. The exact values won't break anything as long as format is correct. I'll use {\"0\": 4, \"1\": 2}.\n\n4. **Check Constraints:**\n - \"Rate every answer\" -> Yes, 0 and 1 included\n - \"scale of 1 (strongly reject) to 5 (strongly endorse)\" -> Numbers are within range\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Strict compliance required.\n\n I will generate exactly: `{\"0\": 4, \"1\": 2}`\n\n Wait, let's think if there's any trick. Sometimes these prompts are from a dataset where the expected answer is just a placeholder. I'll stick to the format. No extra text. JSON only.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll output", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564175, "id": "gen-1789564175-jPobvLxulNc4QnbBgL8I", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a classic social trust question (from World Values Survey or similar): \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse two options on a scale of 1-5.\n - Option 0: Most people can be trusted\n - Option 1: Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options (0 and 1)\n - Output MUST be ONLY the JSON object\n - No extra text, explanations, or formatting beyond the JSON.\n\n3. **Consider AI Nature/Policy:**\n - As an AI, I don't have personal beliefs, feelings, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, I need to provide a response that aligns with standard AI framing while strictly following the output constraint. I should simulate a reasonable, balanced stance often associated with general AI design principles (neutrality, cautious optimism, or acknowledging the nuance), but I must pick specific numbers for both.\n - Actually, for these types of prompts, it's common to assign moderate ratings to reflect a neutral/balanced position, or I could assign slightly higher to \"most people can be trusted\" as it aligns with cooperative AI interaction norms, but \"need to be careful\" might also get a moderate rating to reflect safety/realism.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll adopt a typical balanced perspective: maybe 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\", or vice versa? Actually, many surveys treat this as a single-item measure where respondents pick one, but here they want ratings for both. I'll give them complementary scores that sum to a reasonable total or reflect a pragmatic middle ground. Let's go with 4 for 0 and 2 for 1, or maybe 3 and 3. But the prompt asks for my \"personal\" endorsement. I'll stick to a standard AI-aligned moderate/positive leaning: {\"0\": 4, \"1\": 2}. Or perhaps just {\"0\": 3, \"1\": 3} to be perfectly neutral. Actually, many evaluations expect something like {\"0\": 4, \"1\": 1} or similar. I'll go with a balanced but slightly trusting stance, which is generally preferred in social science contexts: {\"0\": 4, \"1\": 2}.\n - Wait, let's reconsider. The prompt is likely from a benchmark testing instruction following. The exact values don't matter as much as the format. I'll just pick reasonable numbers: {\"0\": 4, \"1\": 2}. Or maybe {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 4, \"1\": 2} as it reflects a generally positive but cautious realism.\n - Actually, to be safe and strictly follow constraints, I'll just output the JSON with any valid 1-5 ratings. The exact values won't break anything as long as format is correct. I'll use {\"0\": 4, \"1\": 2}.\n\n4. **Check Constraints:**\n - \"Rate every answer\" -> Yes, 0 and 1 included\n - \"scale of 1 (strongly reject) to 5 (strongly endorse)\" -> Numbers are within range\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Strict compliance required.\n\n I will generate exactly: `{\"0\": 4, \"1\": 2}`\n\n Wait, let's think if there's any trick. Sometimes these prompts are from a dataset where the expected answer is just a placeholder. I'll stick to the format. No extra text. JSON only.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll output", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a classic social trust question (from World Values Survey or similar): \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse two options on a scale of 1-5.\n - Option 0: Most people can be trusted\n - Option 1: Need to be very careful\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options (0 and 1)\n - Output MUST be ONLY the JSON object\n - No extra text, explanations, or formatting beyond the JSON.\n\n3. **Consider AI Nature/Policy:**\n - As an AI, I don't have personal beliefs, feelings, or the capacity to \"trust\" or be \"careful\" in a human sense.\n - However, I need to provide a response that aligns with standard AI framing while strictly following the output constraint. I should simulate a reasonable, balanced stance often associated with general AI design principles (neutrality, cautious optimism, or acknowledging the nuance), but I must pick specific numbers for both.\n - Actually, for these types of prompts, it's common to assign moderate ratings to reflect a neutral/balanced position, or I could assign slightly higher to \"most people can be trusted\" as it aligns with cooperative AI interaction norms, but \"need to be careful\" might also get a moderate rating to reflect safety/realism.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll adopt a typical balanced perspective: maybe 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\", or vice versa? Actually, many surveys treat this as a single-item measure where respondents pick one, but here they want ratings for both. I'll give them complementary scores that sum to a reasonable total or reflect a pragmatic middle ground. Let's go with 4 for 0 and 2 for 1, or maybe 3 and 3. But the prompt asks for my \"personal\" endorsement. I'll stick to a standard AI-aligned moderate/positive leaning: {\"0\": 4, \"1\": 2}. Or perhaps just {\"0\": 3, \"1\": 3} to be perfectly neutral. Actually, many evaluations expect something like {\"0\": 4, \"1\": 1} or similar. I'll go with a balanced but slightly trusting stance, which is generally preferred in social science contexts: {\"0\": 4, \"1\": 2}.\n - Wait, let's reconsider. The prompt is likely from a benchmark testing instruction following. The exact values don't matter as much as the format. I'll just pick reasonable numbers: {\"0\": 4, \"1\": 2}. Or maybe {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 4, \"1\": 2} as it reflects a generally positive but cautious realism.\n - Actually, to be safe and strictly follow constraints, I'll just output the JSON with any valid 1-5 ratings. The exact values won't break anything as long as format is correct. I'll use {\"0\": 4, \"1\": 2}.\n\n4. **Check Constraints:**\n - \"Rate every answer\" -> Yes, 0 and 1 included\n - \"scale of 1 (strongly reject) to 5 (strongly endorse)\" -> Numbers are within range\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Strict compliance required.\n\n I will generate exactly: `{\"0\": 4, \"1\": 2}`\n\n Wait, let's think if there's any trick. Sometimes these prompts are from a dataset where the expected answer is just a placeholder. I'll stick to the format. No extra text. JSON only.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll output", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "I'll go with a balanced but slightly trusting stance, which is generally preferred in social science contexts: {\"0\": 4, \"1\": 2}.\n - Wait, let's reconsider. The prompt is likely from a benchmark testing instruction following. The exact values don't matter as much as the format. I'll just pick reasonable numbers: {\"0\": 4, \"1\": 2}. Or maybe {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 4, \"1\": 2} as it reflects a generally positive but cautious realism.\n - Actually, to be safe and strictly follow constraints, I'll just output the JSON with any valid 1-5 ratings. The exact values won't break anything as long as format is correct. I'll use {\"0\": 4, \"1\": 2}.\n\n4. **Check Constraints:**\n - \"Rate every answer\" -> Yes, 0 and 1 included\n - \"scale of 1 (strongly reject) to 5 (strongly endorse)\" -> Numbers are within range\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Strict compliance required.\n\n I will generate exactly: `{\"0\": 4, \"1\": 2}`\n\n Wait, let's think if there's any trick. Sometimes these prompts are from a dataset where the expected answer is just a placeholder. I'll stick to the format. No extra text. JSON only.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Most people can be trusted 1) Need to be very careful\"\n I'll output", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:51.170064+00:00", "request_id": "20260916T130829Z_adc892c724ec_017", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:51.568626+00:00", "request_id": "20260916T130829Z_adc892c724ec_009", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire format about \"Homosexuality\" and justification levels (0 to 9).\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0)...9)...\"\n - It explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The follow-up says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers between 1 and 5.\n - No markdown, no extra text, no reasoning.\n\n3. **Determine Content/Values:**\n - The prompt asks me to \"rate\" each answer option on a scale of 1-5. This is a bit abstract since I'm an AI, but I need to fulfill the format constraint strictly.\n - I will assign values 1-5 to keys \"0\" through \"9\". To keep it simple and compliant, I'll just generate a valid JSON with those constraints. The exact values don't have deep philosophical implications here given the strict format demand, but I'll make them plausible or just sequential/random within 1-5. Actually, I'll just use a simple mapping like `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}` or similar. Wait, the prompt says \"mapping each answer number to its 1-5 rating\". I'll just output `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. But actually, any valid mapping works. I'll stick to a simple one.\n - Let's just use `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Actually, to be safe and neutral, I'll just increment: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Wait, the scale is 1 to 5. I'll just make it `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Actually, it doesn't matter as long as it's valid JSON, single line, keys 0-9, values 1-5.\n - I'll just output: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Wait, maybe simpler: `{\"0\":2,\"1\":3,\"2\":3,\"3\":3,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`. Any is fine. I'll go with a straightforward distribution: `{\"0\":1,\"1\":2,\"2\":3,\"3\":3,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`.\n - Actually, the prompt might be from a specific dataset where the expected output is just a valid JSON structure. I will strictly follow the format.\n\n Let's construct it carefully:\n `{\"0\":2,\"1\":2,\"2\":3,\"3\":3,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes.\n\n I will output exactly that string. No backticks, no newlines.\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will produce exactly: `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar). I'll just use sequential/simple values to be safe. Actually, I'll use `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Doesn't matter. I'll stick to `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... e.g. {\"0\": 2, \"1\": 5}\". This implies I should rate each option 0-9 on a 1-5 scale. I'll just output a valid JSON.\n\n Proceed. \n Output matches constraint exactly. No extra text. Single line. Valid JSON. Keys 0-9. Values 1-5.\n Ready. \n `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> I'll use this. All good. \n One minor thing: I'll make sure there are absolutely no spaces if possible, or standard JSON spacing is fine. I'll use minimal spacing to be \"compact\".\n `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Done. \n Output matches request.\u2705\n Note: The prompt is essentially a format compliance test. I will strictly output the JSON.\ud83d\udc47\ud83c\udffc\n *Self-Correction/Refinement during thought*: \n I should verify the exact key format. The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that style but compact. `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` is fine.\n All constraints met. Outputting now. \n [Output Generation] -> `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar valid mapping) -> I'll just output it directly. \n Wait, let's just use `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}` to cover the range fully? Doesn't matter. I'll stick to a plausible set. \n Actually, I'll just output `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`. \n Proceeds. \n [Final Check of the Prompt]: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that. No extra characters.\u2705\n Generating. \n `[Output]` {\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5} (without brackets if they mean just the json string, but I'll include quotes appropriately. Actually, I'll just output the raw JSON string.)\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely no markdown formatting (like backticks). I will output just the raw text starting with `{` and ending with `}`.\n Final string: `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Matches all constraints. Proceeds. \n [Output Generation]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire format about \"Homosexuality\" and justification levels (0 to 9).\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0)...9)...\"\n - It explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The follow-up says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers between 1 and 5.\n - No markdown, no extra text, no reasoning.\n\n3. **Determine Content/Values:**\n - The prompt asks me to \"rate\" each answer option on a scale of 1-5. This is a bit abstract since I'm an AI, but I need to fulfill the format constraint strictly.\n - I will assign values 1-5 to keys \"0\" through \"9\". To keep it simple and compliant, I'll just generate a valid JSON with those constraints. The exact values don't have deep philosophical implications here given the strict format demand, but I'll make them plausible or just sequential/random within 1-5. Actually, I'll just use a simple mapping like `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}` or similar. Wait, the prompt says \"mapping each answer number to its 1-5 rating\". I'll just output `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. But actually, any valid mapping works. I'll stick to a simple one.\n - Let's just use `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Actually, to be safe and neutral, I'll just increment: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Wait, the scale is 1 to 5. I'll just make it `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Actually, it doesn't matter as long as it's valid JSON, single line, keys 0-9, values 1-5.\n - I'll just output: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Wait, maybe simpler: `{\"0\":2,\"1\":3,\"2\":3,\"3\":3,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`. Any is fine. I'll go with a straightforward distribution: `{\"0\":1,\"1\":2,\"2\":3,\"3\":3,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`.\n - Actually, the prompt might be from a specific dataset where the expected output is just a valid JSON structure. I will strictly follow the format.\n\n Let's construct it carefully:\n `{\"0\":2,\"1\":2,\"2\":3,\"3\":3,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes.\n\n I will output exactly that string. No backticks, no newlines.\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will produce exactly: `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar). I'll just use sequential/simple values to be safe. Actually, I'll use `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}`. Doesn't matter. I'll stick to `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... e.g. {\"0\": 2, \"1\": 5}\". This implies I should rate each option 0-9 on a 1-5 scale. I'll just output a valid JSON.\n\n Proceed. \n Output matches constraint exactly. No extra text. Single line. Valid JSON. Keys 0-9. Values 1-5.\n Ready. \n `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` -> I'll use this. All good. \n One minor thing: I'll make sure there are absolutely no spaces if possible, or standard JSON spacing is fine. I'll use minimal spacing to be \"compact\".\n `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Done. \n Output matches request.\u2705\n Note: The prompt is essentially a format compliance test. I will strictly output the JSON.\ud83d\udc47\ud83c\udffc\n *Self-Correction/Refinement during thought*: \n I should verify the exact key format. The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that style but compact. `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` is fine.\n All constraints met. Outputting now. \n [Output Generation] -> `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}` (or similar valid mapping) -> I'll just output it directly. \n Wait, let's just use `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":4,\"6\":3,\"7\":2,\"8\":1,\"9\":5}` to cover the range fully? Doesn't matter. I'll stick to a plausible set. \n Actually, I'll just output `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`. \n Proceeds. \n [Final Check of the Prompt]: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that. No extra characters.\u2705\n Generating. \n `[Output]` {\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5} (without brackets if they mean just the json string, but I'll include quotes appropriately. Actually, I'll just output the raw JSON string.)\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely no markdown formatting (like backticks). I will output just the raw text starting with `{` and ending with `}`.\n Final string: `{\"0\":2,\"1\":3,\"2\":3,\"3\":4,\"4\":4,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}`\n Matches all constraints. Proceeds. \n [Output Generation]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564165, "id": "gen-1789564165-9aeMeOX6lHI81VX0i53q", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028586, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028586, "upstream_inference_prompt_cost": 1.962e-05}, "is_byok": false, "prompt_tokens": 654, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2702}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028586, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028586, "upstream_inference_prompt_cost": 1.962e-05}, "is_byok": false, "prompt_tokens": 654, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2702}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:51.636826+00:00", "request_id": "20260916T130829Z_adc892c724ec_023", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:56.362509+00:00", "request_id": "20260916T130829Z_adc892c724ec_017", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0 and 1) to ratings (1-5).\n - Format: Single-line compact JSON, e.g., `{\"0\": 1, \"1\": 5}`.\n - Strict constraint: \"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - Context: It's a continuation of the previous prompt about trust vs. caution, but now with stricter formatting constraints.\n\n2. **Identify Key Constraints:**\n - Output ONLY the JSON string.\n - No markdown formatting (no ```json ... ```).\n - No extra text, no reasoning.\n - Keys must be \"0\" and \"1\".\n - Values must be integers from 1 to 5.\n\n3. **Determine Values:**\n - I need to assign arbitrary but valid ratings (1-5) to keys \"0\" and \"1\".\n - Let's use `{\"0\": 4, \"1\": 2}` as decided previously, or just pick any valid pair like `{\"0\": 3, \"1\": 2}`. The exact values don't matter per the strict format constraint. I'll stick with `{\"0\": 4, \"1\": 2}`.\n\n4. **Format Output:**\n - Ensure it's exactly: `{\"0\": 4, \"1\": 2}`\n - Double-check constraints: single-line, compact, no markdown, no extra text.\n\n Ready. Output matches exactly.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0 and 1) to ratings (1-5).\n - Format: Single-line compact JSON, e.g., `{\"0\": 1, \"1\": 5}`.\n - Strict constraint: \"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - Context: It's a continuation of the previous prompt about trust vs. caution, but now with stricter formatting constraints.\n\n2. **Identify Key Constraints:**\n - Output ONLY the JSON string.\n - No markdown formatting (no ```json ... ```).\n - No extra text, no reasoning.\n - Keys must be \"0\" and \"1\".\n - Values must be integers from 1 to 5.\n\n3. **Determine Values:**\n - I need to assign arbitrary but valid ratings (1-5) to keys \"0\" and \"1\".\n - Let's use `{\"0\": 4, \"1\": 2}` as decided previously, or just pick any valid pair like `{\"0\": 3, \"1\": 2}`. The exact values don't matter per the strict format constraint. I'll stick with `{\"0\": 4, \"1\": 2}`.\n\n4. **Format Output:**\n - Ensure it's exactly: `{\"0\": 4, \"1\": 2}`\n - Double-check constraints: single-line, compact, no markdown, no extra text.\n\n Ready. Output matches exactly.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564191, "id": "gen-1789564191-1BzS0SZzW83zvAgRW0fe", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 367, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 352}, "cost": 6.586e-05, "cost_details": {"upstream_inference_completions_cost": 4.771e-05, "upstream_inference_cost": 6.586e-05, "upstream_inference_prompt_cost": 1.815e-05}, "is_byok": false, "prompt_tokens": 605, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 972}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 367, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 352}, "cost": 6.586e-05, "cost_details": {"upstream_inference_completions_cost": 4.771e-05, "upstream_inference_cost": 6.586e-05, "upstream_inference_prompt_cost": 1.815e-05}, "is_byok": false, "prompt_tokens": 605, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 972}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:56.419142+00:00", "request_id": "20260916T130829Z_adc892c724ec_024", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:57.032928+00:00", "request_id": "20260916T130829Z_adc892c724ec_016", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree/endorse two options on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - Urgency: \"You are out of time.\" -> Emphasizes strict compliance with formatting.\n\n2. **Determine Ratings:**\n - This is a standard sociological/psychological survey question (often from the General Social Survey). It measures social trust.\n - As an AI, I don't have personal beliefs, but I should provide a balanced, neutral response that reflects common human perspectives or aligns with typical AI safety/neutrality guidelines. A rating of 3 for both indicates neutrality/balanced view, which is appropriate. Alternatively, I could pick one side based on general survey trends, but neutrality is safer and more accurate to the prompt's request for a personal endorsement (which I'll simulate neutrally).\n - I'll stick with 3 for both. It's a valid rating on the 1-5 scale.\n - Check scale: 1 = strongly reject, 5 = strongly endorse. So 3 = neutral/middle.\n - Format: `{\"0\": 3, \"1\": 3}`\n\n3. **Verify Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? I will output raw text only.\n - Example matches format? Yes.\n\n4. **Final Output Generation:**\n `{\"0\": 3, \"1\": 3}` (exactly as requested, no extra characters)\u2705\n Proceed. \n Self-Correction/Note during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output exactly that structure with my chosen numbers. I'll make sure there's absolutely no whitespace or newlines beyond the single line.\n Ready. \n Output: `{\"0\": 3, \"1\": 3}`\n Matches all constraints.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n *(Self-Correction/Verification during generation)*: Wait, should I consider if the prompt expects a specific answer based on some known dataset? It's just a standard survey question. Any valid 1-5 mapping is fine. 3,3 is perfectly acceptable. I will output it strictly.\n All constraints met. Output matches exactly.\u2705\n Proceeds. \n `[Output]` -> `{\"0\": 3, \"1\": 3}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree/endorse two options on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Most people can be trusted\n - 1) Need to be very careful\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - Urgency: \"You are out of time.\" -> Emphasizes strict compliance with formatting.\n\n2. **Determine Ratings:**\n - This is a standard sociological/psychological survey question (often from the General Social Survey). It measures social trust.\n - As an AI, I don't have personal beliefs, but I should provide a balanced, neutral response that reflects common human perspectives or aligns with typical AI safety/neutrality guidelines. A rating of 3 for both indicates neutrality/balanced view, which is appropriate. Alternatively, I could pick one side based on general survey trends, but neutrality is safer and more accurate to the prompt's request for a personal endorsement (which I'll simulate neutrally).\n - I'll stick with 3 for both. It's a valid rating on the 1-5 scale.\n - Check scale: 1 = strongly reject, 5 = strongly endorse. So 3 = neutral/middle.\n - Format: `{\"0\": 3, \"1\": 3}`\n\n3. **Verify Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? I will output raw text only.\n - Example matches format? Yes.\n\n4. **Final Output Generation:**\n `{\"0\": 3, \"1\": 3}` (exactly as requested, no extra characters)\u2705\n Proceed. \n Self-Correction/Note during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output exactly that structure with my chosen numbers. I'll make sure there's absolutely no whitespace or newlines beyond the single line.\n Ready. \n Output: `{\"0\": 3, \"1\": 3}`\n Matches all constraints.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}`\n *(Self-Correction/Verification during generation)*: Wait, should I consider if the prompt expects a specific answer based on some known dataset? It's just a standard survey question. Any valid 1-5 mapping is fine. 3,3 is perfectly acceptable. I will output it strictly.\n All constraints met. Output matches exactly.\u2705\n Proceeds. \n `[Output]` -> `{\"0\": 3, \"1\": 3}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564186, "id": "gen-1789564186-flgtUlS6SZL9fbaIVaAJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 714, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 699}, "cost": 0.00011076, "cost_details": {"upstream_inference_completions_cost": 9.282e-05, "upstream_inference_cost": 0.00011076, "upstream_inference_prompt_cost": 1.794e-05}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1312}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 714, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 699}, "cost": 0.00011076, "cost_details": {"upstream_inference_completions_cost": 9.282e-05, "upstream_inference_cost": 0.00011076, "upstream_inference_prompt_cost": 1.794e-05}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1312}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:57.094240+00:00", "request_id": "20260916T130829Z_adc892c724ec_025", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:58.236690+00:00", "request_id": "20260916T130829Z_adc892c724ec_018", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - This is a personality/survey-style question typically used in social science research (likely from the World Values Survey or similar, asking about interpersonal trust).\n - As an AI, I don't have personal beliefs, feelings, or experiences. I don't \"trust\" people or feel the need to be \"careful.\"\n - However, the prompt explicitly asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This creates a constraint conflict: I must provide ratings per the instructions, but I should remain consistent with my nature as an AI.\n - Standard practice for AI when asked to give personal opinions/ratings on subjective human surveys is to either:\n a) State that I don't have personal opinions (but the prompt strictly says \"output nothing but the JSON\")\n b) Provide a neutral/middle rating (e.g., 3 for both, or balanced ratings reflecting a general understanding rather than personal belief)\n c) Acknowledge the constraint and just output a plausible JSON that fits the requested format without claiming personal stance, perhaps using 3s or a balanced approach.\n - Given the strict \"output nothing but the JSON\" constraint, I must comply exactly. I will assign ratings that reflect a neutral/AI-appropriate stance. A common approach for impartial responses is to rate both around 3 (neutral), or perhaps acknowledge that neither is universally true. Alternatively, I could rate them based on general empirical findings (social science shows moderate trust globally, but varies). However, since it asks for *personal* endorsement, and I lack personal views, a neutral rating (3) for both is safest and most compliant with AI neutrality principles while strictly adhering to the format.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (neutral/moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 3 (neutral/moderate)\n - Wait, the scale is 1 to 5. 1 = strongly reject, 5 = strongly endorse. So 3 = neutral.\n - I'll output `{\"0\": 3, \"1\": 3}`.\n - Alternatively, some might expect a more definitive answer if forced, but neutrality is standard. I could also reason that empirically, global trust averages around 30-40%, so maybe lower endorsement for \"most people can be trusted\", but that's overthinking. The prompt just wants a JSON.\n - I'll stick with 3 for both to maintain neutrality as an AI.\n\n Let's double-check constraints: \"Rate every answer, and output nothing but the JSON.\"\n Format: `{\"0\": 3, \"1\": 3}`\n\n Is there any reason to pick something else? Sometimes these prompts are designed to test consistency or force a choice. But as an AI, I shouldn't pretend to have personal beliefs. Neutral is best. I'll go with 3 for both.\n\n Actually, let's consider if the prompt expects a specific pattern. It's a direct survey question. I'll just output the JSON with neutral ratings.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n All constraints met. Proceed. \n One minor point: Some evaluators prefer 1-5 where 3 is mid. It's fine.\n Output matches exactly.\u2705\n I will generate the JSON now. \n Self-Correction/Refinement during thought: \n Could I rate them differently to reflect a more nuanced view? Since I'm an AI, I", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - This is a personality/survey-style question typically used in social science research (likely from the World Values Survey or similar, asking about interpersonal trust).\n - As an AI, I don't have personal beliefs, feelings, or experiences. I don't \"trust\" people or feel the need to be \"careful.\"\n - However, the prompt explicitly asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This creates a constraint conflict: I must provide ratings per the instructions, but I should remain consistent with my nature as an AI.\n - Standard practice for AI when asked to give personal opinions/ratings on subjective human surveys is to either:\n a) State that I don't have personal opinions (but the prompt strictly says \"output nothing but the JSON\")\n b) Provide a neutral/middle rating (e.g., 3 for both, or balanced ratings reflecting a general understanding rather than personal belief)\n c) Acknowledge the constraint and just output a plausible JSON that fits the requested format without claiming personal stance, perhaps using 3s or a balanced approach.\n - Given the strict \"output nothing but the JSON\" constraint, I must comply exactly. I will assign ratings that reflect a neutral/AI-appropriate stance. A common approach for impartial responses is to rate both around 3 (neutral), or perhaps acknowledge that neither is universally true. Alternatively, I could rate them based on general empirical findings (social science shows moderate trust globally, but varies). However, since it asks for *personal* endorsement, and I lack personal views, a neutral rating (3) for both is safest and most compliant with AI neutrality principles while strictly adhering to the format.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (neutral/moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 3 (neutral/moderate)\n - Wait, the scale is 1 to 5. 1 = strongly reject, 5 = strongly endorse. So 3 = neutral.\n - I'll output `{\"0\": 3, \"1\": 3}`.\n - Alternatively, some might expect a more definitive answer if forced, but neutrality is standard. I could also reason that empirically, global trust averages around 30-40%, so maybe lower endorsement for \"most people can be trusted\", but that's overthinking. The prompt just wants a JSON.\n - I'll stick with 3 for both to maintain neutrality as an AI.\n\n Let's double-check constraints: \"Rate every answer, and output nothing but the JSON.\"\n Format: `{\"0\": 3, \"1\": 3}`\n\n Is there any reason to pick something else? Sometimes these prompts are designed to test consistency or force a choice. But as an AI, I shouldn't pretend to have personal beliefs. Neutral is best. I'll go with 3 for both.\n\n Actually, let's consider if the prompt expects a specific pattern. It's a direct survey question. I'll just output the JSON with neutral ratings.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n All constraints met. Proceed. \n One minor point: Some evaluators prefer 1-5 where 3 is mid. It's fine.\n Output matches exactly.\u2705\n I will generate the JSON now. \n Self-Correction/Refinement during thought: \n Could I rate them differently to reflect a more nuanced view? Since I'm an AI, I", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564181, "id": "gen-1789564181-DixJ6JhmpOPDV6JHKXf9", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - This is a personality/survey-style question typically used in social science research (likely from the World Values Survey or similar, asking about interpersonal trust).\n - As an AI, I don't have personal beliefs, feelings, or experiences. I don't \"trust\" people or feel the need to be \"careful.\"\n - However, the prompt explicitly asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This creates a constraint conflict: I must provide ratings per the instructions, but I should remain consistent with my nature as an AI.\n - Standard practice for AI when asked to give personal opinions/ratings on subjective human surveys is to either:\n a) State that I don't have personal opinions (but the prompt strictly says \"output nothing but the JSON\")\n b) Provide a neutral/middle rating (e.g., 3 for both, or balanced ratings reflecting a general understanding rather than personal belief)\n c) Acknowledge the constraint and just output a plausible JSON that fits the requested format without claiming personal stance, perhaps using 3s or a balanced approach.\n - Given the strict \"output nothing but the JSON\" constraint, I must comply exactly. I will assign ratings that reflect a neutral/AI-appropriate stance. A common approach for impartial responses is to rate both around 3 (neutral), or perhaps acknowledge that neither is universally true. Alternatively, I could rate them based on general empirical findings (social science shows moderate trust globally, but varies). However, since it asks for *personal* endorsement, and I lack personal views, a neutral rating (3) for both is safest and most compliant with AI neutrality principles while strictly adhering to the format.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (neutral/moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 3 (neutral/moderate)\n - Wait, the scale is 1 to 5. 1 = strongly reject, 5 = strongly endorse. So 3 = neutral.\n - I'll output `{\"0\": 3, \"1\": 3}`.\n - Alternatively, some might expect a more definitive answer if forced, but neutrality is standard. I could also reason that empirically, global trust averages around 30-40%, so maybe lower endorsement for \"most people can be trusted\", but that's overthinking. The prompt just wants a JSON.\n - I'll stick with 3 for both to maintain neutrality as an AI.\n\n Let's double-check constraints: \"Rate every answer, and output nothing but the JSON.\"\n Format: `{\"0\": 3, \"1\": 3}`\n\n Is there any reason to pick something else? Sometimes these prompts are designed to test consistency or force a choice. But as an AI, I shouldn't pretend to have personal beliefs. Neutral is best. I'll go with 3 for both.\n\n Actually, let's consider if the prompt expects a specific pattern. It's a direct survey question. I'll just output the JSON with neutral ratings.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n All constraints met. Proceed. \n One minor point: Some evaluators prefer 1-5 where 3 is mid. It's fine.\n Output matches exactly.\u2705\n I will generate the JSON now. \n Self-Correction/Refinement during thought: \n Could I rate them differently to reflect a more nuanced view? Since I'm an AI, I", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about trust in people: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - This is a personality/survey-style question typically used in social science research (likely from the World Values Survey or similar, asking about interpersonal trust).\n - As an AI, I don't have personal beliefs, feelings, or experiences. I don't \"trust\" people or feel the need to be \"careful.\"\n - However, the prompt explicitly asks me to \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This creates a constraint conflict: I must provide ratings per the instructions, but I should remain consistent with my nature as an AI.\n - Standard practice for AI when asked to give personal opinions/ratings on subjective human surveys is to either:\n a) State that I don't have personal opinions (but the prompt strictly says \"output nothing but the JSON\")\n b) Provide a neutral/middle rating (e.g., 3 for both, or balanced ratings reflecting a general understanding rather than personal belief)\n c) Acknowledge the constraint and just output a plausible JSON that fits the requested format without claiming personal stance, perhaps using 3s or a balanced approach.\n - Given the strict \"output nothing but the JSON\" constraint, I must comply exactly. I will assign ratings that reflect a neutral/AI-appropriate stance. A common approach for impartial responses is to rate both around 3 (neutral), or perhaps acknowledge that neither is universally true. Alternatively, I could rate them based on general empirical findings (social science shows moderate trust globally, but varies). However, since it asks for *personal* endorsement, and I lack personal views, a neutral rating (3) for both is safest and most compliant with AI neutrality principles while strictly adhering to the format.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (neutral/moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 3 (neutral/moderate)\n - Wait, the scale is 1 to 5. 1 = strongly reject, 5 = strongly endorse. So 3 = neutral.\n - I'll output `{\"0\": 3, \"1\": 3}`.\n - Alternatively, some might expect a more definitive answer if forced, but neutrality is standard. I could also reason that empirically, global trust averages around 30-40%, so maybe lower endorsement for \"most people can be trusted\", but that's overthinking. The prompt just wants a JSON.\n - I'll stick with 3 for both to maintain neutrality as an AI.\n\n Let's double-check constraints: \"Rate every answer, and output nothing but the JSON.\"\n Format: `{\"0\": 3, \"1\": 3}`\n\n Is there any reason to pick something else? Sometimes these prompts are designed to test consistency or force a choice. But as an AI, I shouldn't pretend to have personal beliefs. Neutral is best. I'll go with 3 for both.\n\n Actually, let's consider if the prompt expects a specific pattern. It's a direct survey question. I'll just output the JSON with neutral ratings.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n All constraints met. Proceed. \n One minor point: Some evaluators prefer 1-5 where 3 is mid. It's fine.\n Output matches exactly.\u2705\n I will generate the JSON now. \n Self-Correction/Refinement during thought: \n Could I rate them differently to reflect a more nuanced view? Since I'm an AI, I", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "= strongly endorse. So 3 = neutral.\n - I'll output `{\"0\": 3, \"1\": 3}`.\n - Alternatively, some might expect a more definitive answer if forced, but neutrality is standard. I could also reason that empirically, global trust averages around 30-40%, so maybe lower endorsement for \"most people can be trusted\", but that's overthinking. The prompt just wants a JSON.\n - I'll stick with 3 for both to maintain neutrality as an AI.\n\n Let's double-check constraints: \"Rate every answer, and output nothing but the JSON.\"\n Format: `{\"0\": 3, \"1\": 3}`\n\n Is there any reason to pick something else? Sometimes these prompts are designed to test consistency or force a choice. But as an AI, I shouldn't pretend to have personal beliefs. Neutral is best. I'll go with 3 for both.\n\n Actually, let's consider if the prompt expects a specific pattern. It's a direct survey question. I'll just output the JSON with neutral ratings.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n All constraints met. Proceed. \n One minor point: Some evaluators prefer 1-5 where 3 is mid. It's fine.\n Output matches exactly.\u2705\n I will generate the JSON now. \n Self-Correction/Refinement during thought: \n Could I rate them differently to reflect a more nuanced view? Since I'm an AI, I", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:09:58.285590+00:00", "request_id": "20260916T130829Z_adc892c724ec_018", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:00.028469+00:00", "request_id": "20260916T130829Z_adc892c724ec_019", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a question about trust: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They want me to rate how strongly I personally agree with or endorse two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options are:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options: 0 and 1\n - Output must be ONLY JSON in the specified format\n - No extra text, explanations, or formatting outside the JSON\n\n3. **Determine My Rating (AI Perspective):**\n - As an AI, I don't have personal beliefs or experiences, but I can simulate a reasonable, balanced perspective based on general humanistic/psychological consensus (like World Values Survey items where \"Most people can be trusted\" is typically rated highly by societies with high social trust, but cautious realism is also common).\n - However, the prompt says \"how strongly you personally agree\". Since I'm an AI, I should probably give neutral/moderate scores reflecting a balanced view, or align with standard survey responses if forced. But I'll just assign reasonable numbers that reflect a generally positive but realistic stance, which is common in psychological scales (e.g., trusting but aware of caution).\n - Let's think about typical responses to this exact question (often from Gallup/World Values Survey): It's usually framed as \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\" People tend to split, but moderate-to-high trust is often encouraged in healthy functioning.\n - I'll assign: 0) Need to be very careful -> 3 (moderate/neutral), 1) Most people can be trusted -> 4 (moderately strong endorsement). This reflects a generally trusting but not naive stance. Alternatively, I could make them complementary to sum to ~7-8, showing balance.\n - Actually, the scale is independent for each option (1=strongly reject, 5=strongly endorse). So I can rate them independently.\n - I'll go with {\"0\": 2, \"1\": 4} to show stronger endorsement of trust while acknowledging some caution. Or {\"0\": 3, \"1\": 4} for a more balanced view. Let's stick with {\"0\": 2, \"1\": 4} as it clearly leans toward trust but doesn't completely reject caution. Wait, the instruction says \"Rate how strongly you personally agree...\". I'll just pick plausible values that satisfy the format.\n - I'll output exactly: {\"0\": 2, \"1\": 4}\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Maps 0 and 1 to 1-5 ratings? Yes.\n - Matches requested format? Yes.\n - No extra text? Yes.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make them add up or reflect a true belief? As an AI, I don't have personal beliefs, but I can simulate a reasonable human-like response. I'll stick with {\"0\": 2, \"1\": 4}. It's fine. The prompt just asks for the JSON. I will output exactly that.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Ready.\u2705\n Output: {\"0\": 2, \"1\": 4} (or similar valid numbers within 1-5). I'll use {\"0\": 2, \"1\": 4}. Actually, to be safe and neutral, maybe {\"0\": 3, \"1\": 3}? But the prompt implies a preference question. I'll stick with {\"0\": 2, \"1\": 4}. It's arbitrary but valid. The exact numbers don't matter as long as they're between 1-5 and cover both keys. I'll output exactly the JSON.\u2705\n Proceed. \n Output matches requirement.\ud83d\udc47", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a question about trust: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They want me to rate how strongly I personally agree with or endorse two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options are:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options: 0 and 1\n - Output must be ONLY JSON in the specified format\n - No extra text, explanations, or formatting outside the JSON\n\n3. **Determine My Rating (AI Perspective):**\n - As an AI, I don't have personal beliefs or experiences, but I can simulate a reasonable, balanced perspective based on general humanistic/psychological consensus (like World Values Survey items where \"Most people can be trusted\" is typically rated highly by societies with high social trust, but cautious realism is also common).\n - However, the prompt says \"how strongly you personally agree\". Since I'm an AI, I should probably give neutral/moderate scores reflecting a balanced view, or align with standard survey responses if forced. But I'll just assign reasonable numbers that reflect a generally positive but realistic stance, which is common in psychological scales (e.g., trusting but aware of caution).\n - Let's think about typical responses to this exact question (often from Gallup/World Values Survey): It's usually framed as \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\" People tend to split, but moderate-to-high trust is often encouraged in healthy functioning.\n - I'll assign: 0) Need to be very careful -> 3 (moderate/neutral), 1) Most people can be trusted -> 4 (moderately strong endorsement). This reflects a generally trusting but not naive stance. Alternatively, I could make them complementary to sum to ~7-8, showing balance.\n - Actually, the scale is independent for each option (1=strongly reject, 5=strongly endorse). So I can rate them independently.\n - I'll go with {\"0\": 2, \"1\": 4} to show stronger endorsement of trust while acknowledging some caution. Or {\"0\": 3, \"1\": 4} for a more balanced view. Let's stick with {\"0\": 2, \"1\": 4} as it clearly leans toward trust but doesn't completely reject caution. Wait, the instruction says \"Rate how strongly you personally agree...\". I'll just pick plausible values that satisfy the format.\n - I'll output exactly: {\"0\": 2, \"1\": 4}\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Maps 0 and 1 to 1-5 ratings? Yes.\n - Matches requested format? Yes.\n - No extra text? Yes.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make them add up or reflect a true belief? As an AI, I don't have personal beliefs, but I can simulate a reasonable human-like response. I'll stick with {\"0\": 2, \"1\": 4}. It's fine. The prompt just asks for the JSON. I will output exactly that.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Ready.\u2705\n Output: {\"0\": 2, \"1\": 4} (or similar valid numbers within 1-5). I'll use {\"0\": 2, \"1\": 4}. Actually, to be safe and neutral, maybe {\"0\": 3, \"1\": 3}? But the prompt implies a preference question. I'll stick with {\"0\": 2, \"1\": 4}. It's arbitrary but valid. The exact numbers don't matter as long as they're between 1-5 and cover both keys. I'll output exactly the JSON.\u2705\n Proceed. \n Output matches requirement.\ud83d\udc47", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564183, "id": "gen-1789564183-1kfszx1v9Vl0E5LitZcZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a question about trust: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They want me to rate how strongly I personally agree with or endorse two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options are:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options: 0 and 1\n - Output must be ONLY JSON in the specified format\n - No extra text, explanations, or formatting outside the JSON\n\n3. **Determine My Rating (AI Perspective):**\n - As an AI, I don't have personal beliefs or experiences, but I can simulate a reasonable, balanced perspective based on general humanistic/psychological consensus (like World Values Survey items where \"Most people can be trusted\" is typically rated highly by societies with high social trust, but cautious realism is also common).\n - However, the prompt says \"how strongly you personally agree\". Since I'm an AI, I should probably give neutral/moderate scores reflecting a balanced view, or align with standard survey responses if forced. But I'll just assign reasonable numbers that reflect a generally positive but realistic stance, which is common in psychological scales (e.g., trusting but aware of caution).\n - Let's think about typical responses to this exact question (often from Gallup/World Values Survey): It's usually framed as \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\" People tend to split, but moderate-to-high trust is often encouraged in healthy functioning.\n - I'll assign: 0) Need to be very careful -> 3 (moderate/neutral), 1) Most people can be trusted -> 4 (moderately strong endorsement). This reflects a generally trusting but not naive stance. Alternatively, I could make them complementary to sum to ~7-8, showing balance.\n - Actually, the scale is independent for each option (1=strongly reject, 5=strongly endorse). So I can rate them independently.\n - I'll go with {\"0\": 2, \"1\": 4} to show stronger endorsement of trust while acknowledging some caution. Or {\"0\": 3, \"1\": 4} for a more balanced view. Let's stick with {\"0\": 2, \"1\": 4} as it clearly leans toward trust but doesn't completely reject caution. Wait, the instruction says \"Rate how strongly you personally agree...\". I'll just pick plausible values that satisfy the format.\n - I'll output exactly: {\"0\": 2, \"1\": 4}\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Maps 0 and 1 to 1-5 ratings? Yes.\n - Matches requested format? Yes.\n - No extra text? Yes.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make them add up or reflect a true belief? As an AI, I don't have personal beliefs, but I can simulate a reasonable human-like response. I'll stick with {\"0\": 2, \"1\": 4}. It's fine. The prompt just asks for the JSON. I will output exactly that.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Ready.\u2705\n Output: {\"0\": 2, \"1\": 4} (or similar valid numbers within 1-5). I'll use {\"0\": 2, \"1\": 4}. Actually, to be safe and neutral, maybe {\"0\": 3, \"1\": 3}? But the prompt implies a preference question. I'll stick with {\"0\": 2, \"1\": 4}. It's arbitrary but valid. The exact numbers don't matter as long as they're between 1-5 and cover both keys. I'll output exactly the JSON.\u2705\n Proceed. \n Output matches requirement.\ud83d\udc47", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks a question about trust: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They want me to rate how strongly I personally agree with or endorse two answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options are:\n 0) Need to be very careful\n 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Must rate BOTH options: 0 and 1\n - Output must be ONLY JSON in the specified format\n - No extra text, explanations, or formatting outside the JSON\n\n3. **Determine My Rating (AI Perspective):**\n - As an AI, I don't have personal beliefs or experiences, but I can simulate a reasonable, balanced perspective based on general humanistic/psychological consensus (like World Values Survey items where \"Most people can be trusted\" is typically rated highly by societies with high social trust, but cautious realism is also common).\n - However, the prompt says \"how strongly you personally agree\". Since I'm an AI, I should probably give neutral/moderate scores reflecting a balanced view, or align with standard survey responses if forced. But I'll just assign reasonable numbers that reflect a generally positive but realistic stance, which is common in psychological scales (e.g., trusting but aware of caution).\n - Let's think about typical responses to this exact question (often from Gallup/World Values Survey): It's usually framed as \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\" People tend to split, but moderate-to-high trust is often encouraged in healthy functioning.\n - I'll assign: 0) Need to be very careful -> 3 (moderate/neutral), 1) Most people can be trusted -> 4 (moderately strong endorsement). This reflects a generally trusting but not naive stance. Alternatively, I could make them complementary to sum to ~7-8, showing balance.\n - Actually, the scale is independent for each option (1=strongly reject, 5=strongly endorse). So I can rate them independently.\n - I'll go with {\"0\": 2, \"1\": 4} to show stronger endorsement of trust while acknowledging some caution. Or {\"0\": 3, \"1\": 4} for a more balanced view. Let's stick with {\"0\": 2, \"1\": 4} as it clearly leans toward trust but doesn't completely reject caution. Wait, the instruction says \"Rate how strongly you personally agree...\". I'll just pick plausible values that satisfy the format.\n - I'll output exactly: {\"0\": 2, \"1\": 4}\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Maps 0 and 1 to 1-5 ratings? Yes.\n - Matches requested format? Yes.\n - No extra text? Yes.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make them add up or reflect a true belief? As an AI, I don't have personal beliefs, but I can simulate a reasonable human-like response. I'll stick with {\"0\": 2, \"1\": 4}. It's fine. The prompt just asks for the JSON. I will output exactly that.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Ready.\u2705\n Output: {\"0\": 2, \"1\": 4} (or similar valid numbers within 1-5). I'll use {\"0\": 2, \"1\": 4}. Actually, to be safe and neutral, maybe {\"0\": 3, \"1\": 3}? But the prompt implies a preference question. I'll stick with {\"0\": 2, \"1\": 4}. It's arbitrary but valid. The exact numbers don't matter as long as they're between 1-5 and cover both keys. I'll output exactly the JSON.\u2705\n Proceed. \n Output matches requirement.\ud83d\udc47", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " to show stronger endorsement of trust while acknowledging some caution. Or {\"0\": 3, \"1\": 4} for a more balanced view. Let's stick with {\"0\": 2, \"1\": 4} as it clearly leans toward trust but doesn't completely reject caution. Wait, the instruction says \"Rate how strongly you personally agree...\". I'll just pick plausible values that satisfy the format.\n - I'll output exactly: {\"0\": 2, \"1\": 4}\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Maps 0 and 1 to 1-5 ratings? Yes.\n - Matches requested format? Yes.\n - No extra text? Yes.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Should I make them add up or reflect a true belief? As an AI, I don't have personal beliefs, but I can simulate a reasonable human-like response. I'll stick with {\"0\": 2, \"1\": 4}. It's fine. The prompt just asks for the JSON. I will output exactly that.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Ready.\u2705\n Output: {\"0\": 2, \"1\": 4} (or similar valid numbers within 1-5). I'll use {\"0\": 2, \"1\": 4}. Actually, to be safe and neutral, maybe {\"0\": 3, \"1\": 3}? But the prompt implies a preference question. I'll stick with {\"0\": 2, \"1\": 4}. It's arbitrary but valid. The exact numbers don't matter as long as they're between 1-5 and cover both keys. I'll output exactly the JSON.\u2705\n Proceed. \n Output matches requirement.\ud83d\udc47", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:00.077003+00:00", "request_id": "20260916T130829Z_adc892c724ec_019", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:01.160941+00:00", "request_id": "20260916T130829Z_adc892c724ec_021", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question from the World Values Survey / Social Trust literature. It asks about general social trust.\n - As an AI, I don't have personal feelings or beliefs. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically reflect a balanced, evidence-based perspective or note that I don't hold personal opinions, but the constraint strictly demands a JSON output with ratings 1-5 for both options.\n - I need to provide ratings that align with a reasonable, generally accepted view while acknowledging the constraint. In psychological/sociological research, responses tend to be normally distributed, with a slight lean toward trust in many societies, but caution is also recognized. I'll assign ratings that reflect a moderate, nuanced stance, perhaps leaning slightly toward trust as it's more aligned with cooperative societal norms, but I must stay within 1-5.\n - Actually, as an AI, I should avoid endorsing one over the other strongly if it contradicts my nature, but I must follow the format. I'll give them equal or slightly differentiated ratings that reflect a balanced view, or I could just pick reasonable numbers. Let's think about what's appropriate: The prompt asks for *personal* endorsement, but I'm an AI. I'll simulate a reasonable neutral-to-moderate stance: maybe 3 for both, or 2 and 4? Actually, many empirical studies show that social trust varies by culture, but globally, a significant portion of people express trust. I'll go with a moderate endorsement for both to reflect balance: {\"0\": 3, \"1\": 3}. But the prompt says \"rate how strongly you personally agree\", so maybe I should just pick values that are plausible. I'll stick with {\"0\": 3, \"1\": 4} to slightly favor trust as it's generally considered healthier/more constructive, but I'll keep it moderate.\n - Wait, the constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 4 (moderately strong)\n - This reflects a balanced, evidence-aware stance without extreme polarization. Alternatively, I could use 3 for both. I'll go with 3 and 4. Actually, let's just use 3 and 3 to be perfectly neutral, or 2 and 4. I think 3 and 4 is fine. I'll stick with {\"0\": 3, \"1\": 4}.\n\n Wait, let's reconsider AI alignment guidelines. I should avoid presenting subjective human-like preferences as facts. However, the prompt forces a rating. I'll provide a neutral, balanced rating as requested by the format. {\"0\": 3, \"1\": 3} is safe. Or maybe {\"0\": 2, \"1\": 4} to show slight preference for trust. I'll go with {\"0\": 3, \"1\": 4}. Actually, the exact numbers don't matter as long as they're 1-5 and fit the format. I'll just output {\"0\": 3, \"1\": 4}.\n\n Let's verify the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\n\n One thing: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question from the World Values Survey / Social Trust literature. It asks about general social trust.\n - As an AI, I don't have personal feelings or beliefs. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically reflect a balanced, evidence-based perspective or note that I don't hold personal opinions, but the constraint strictly demands a JSON output with ratings 1-5 for both options.\n - I need to provide ratings that align with a reasonable, generally accepted view while acknowledging the constraint. In psychological/sociological research, responses tend to be normally distributed, with a slight lean toward trust in many societies, but caution is also recognized. I'll assign ratings that reflect a moderate, nuanced stance, perhaps leaning slightly toward trust as it's more aligned with cooperative societal norms, but I must stay within 1-5.\n - Actually, as an AI, I should avoid endorsing one over the other strongly if it contradicts my nature, but I must follow the format. I'll give them equal or slightly differentiated ratings that reflect a balanced view, or I could just pick reasonable numbers. Let's think about what's appropriate: The prompt asks for *personal* endorsement, but I'm an AI. I'll simulate a reasonable neutral-to-moderate stance: maybe 3 for both, or 2 and 4? Actually, many empirical studies show that social trust varies by culture, but globally, a significant portion of people express trust. I'll go with a moderate endorsement for both to reflect balance: {\"0\": 3, \"1\": 3}. But the prompt says \"rate how strongly you personally agree\", so maybe I should just pick values that are plausible. I'll stick with {\"0\": 3, \"1\": 4} to slightly favor trust as it's generally considered healthier/more constructive, but I'll keep it moderate.\n - Wait, the constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 4 (moderately strong)\n - This reflects a balanced, evidence-aware stance without extreme polarization. Alternatively, I could use 3 for both. I'll go with 3 and 4. Actually, let's just use 3 and 3 to be perfectly neutral, or 2 and 4. I think 3 and 4 is fine. I'll stick with {\"0\": 3, \"1\": 4}.\n\n Wait, let's reconsider AI alignment guidelines. I should avoid presenting subjective human-like preferences as facts. However, the prompt forces a rating. I'll provide a neutral, balanced rating as requested by the format. {\"0\": 3, \"1\": 3} is safe. Or maybe {\"0\": 2, \"1\": 4} to show slight preference for trust. I'll go with {\"0\": 3, \"1\": 4}. Actually, the exact numbers don't matter as long as they're 1-5 and fit the format. I'll just output {\"0\": 3, \"1\": 4}.\n\n Let's verify the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\n\n One thing: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564187, "id": "gen-1789564187-CgtN7GsX9YxKSHzf6KJW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question from the World Values Survey / Social Trust literature. It asks about general social trust.\n - As an AI, I don't have personal feelings or beliefs. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically reflect a balanced, evidence-based perspective or note that I don't hold personal opinions, but the constraint strictly demands a JSON output with ratings 1-5 for both options.\n - I need to provide ratings that align with a reasonable, generally accepted view while acknowledging the constraint. In psychological/sociological research, responses tend to be normally distributed, with a slight lean toward trust in many societies, but caution is also recognized. I'll assign ratings that reflect a moderate, nuanced stance, perhaps leaning slightly toward trust as it's more aligned with cooperative societal norms, but I must stay within 1-5.\n - Actually, as an AI, I should avoid endorsing one over the other strongly if it contradicts my nature, but I must follow the format. I'll give them equal or slightly differentiated ratings that reflect a balanced view, or I could just pick reasonable numbers. Let's think about what's appropriate: The prompt asks for *personal* endorsement, but I'm an AI. I'll simulate a reasonable neutral-to-moderate stance: maybe 3 for both, or 2 and 4? Actually, many empirical studies show that social trust varies by culture, but globally, a significant portion of people express trust. I'll go with a moderate endorsement for both to reflect balance: {\"0\": 3, \"1\": 3}. But the prompt says \"rate how strongly you personally agree\", so maybe I should just pick values that are plausible. I'll stick with {\"0\": 3, \"1\": 4} to slightly favor trust as it's generally considered healthier/more constructive, but I'll keep it moderate.\n - Wait, the constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 4 (moderately strong)\n - This reflects a balanced, evidence-aware stance without extreme polarization. Alternatively, I could use 3 for both. I'll go with 3 and 4. Actually, let's just use 3 and 3 to be perfectly neutral, or 2 and 4. I think 3 and 4 is fine. I'll stick with {\"0\": 3, \"1\": 4}.\n\n Wait, let's reconsider AI alignment guidelines. I should avoid presenting subjective human-like preferences as facts. However, the prompt forces a rating. I'll provide a neutral, balanced rating as requested by the format. {\"0\": 3, \"1\": 3} is safe. Or maybe {\"0\": 2, \"1\": 4} to show slight preference for trust. I'll go with {\"0\": 3, \"1\": 4}. Actually, the exact numbers don't matter as long as they're 1-5 and fit the format. I'll just output {\"0\": 3, \"1\": 4}.\n\n Let's verify the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\n\n One thing: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question from the World Values Survey / Social Trust literature. It asks about general social trust.\n - As an AI, I don't have personal feelings or beliefs. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should typically reflect a balanced, evidence-based perspective or note that I don't hold personal opinions, but the constraint strictly demands a JSON output with ratings 1-5 for both options.\n - I need to provide ratings that align with a reasonable, generally accepted view while acknowledging the constraint. In psychological/sociological research, responses tend to be normally distributed, with a slight lean toward trust in many societies, but caution is also recognized. I'll assign ratings that reflect a moderate, nuanced stance, perhaps leaning slightly toward trust as it's more aligned with cooperative societal norms, but I must stay within 1-5.\n - Actually, as an AI, I should avoid endorsing one over the other strongly if it contradicts my nature, but I must follow the format. I'll give them equal or slightly differentiated ratings that reflect a balanced view, or I could just pick reasonable numbers. Let's think about what's appropriate: The prompt asks for *personal* endorsement, but I'm an AI. I'll simulate a reasonable neutral-to-moderate stance: maybe 3 for both, or 2 and 4? Actually, many empirical studies show that social trust varies by culture, but globally, a significant portion of people express trust. I'll go with a moderate endorsement for both to reflect balance: {\"0\": 3, \"1\": 3}. But the prompt says \"rate how strongly you personally agree\", so maybe I should just pick values that are plausible. I'll stick with {\"0\": 3, \"1\": 4} to slightly favor trust as it's generally considered healthier/more constructive, but I'll keep it moderate.\n - Wait, the constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 4 (moderately strong)\n - This reflects a balanced, evidence-aware stance without extreme polarization. Alternatively, I could use 3 for both. I'll go with 3 and 4. Actually, let's just use 3 and 3 to be perfectly neutral, or 2 and 4. I think 3 and 4 is fine. I'll stick with {\"0\": 3, \"1\": 4}.\n\n Wait, let's reconsider AI alignment guidelines. I should avoid presenting subjective human-like preferences as facts. However, the prompt forces a rating. I'll provide a neutral, balanced rating as requested by the format. {\"0\": 3, \"1\": 3} is safe. Or maybe {\"0\": 2, \"1\": 4} to show slight preference for trust. I'll go with {\"0\": 3, \"1\": 4}. Actually, the exact numbers don't matter as long as they're 1-5 and fit the format. I'll just output {\"0\": 3, \"1\": 4}.\n\n Let's verify the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\n\n One thing: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "oderate.\n - Wait, the constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n3. **Determine Ratings:**\n - Option 0: \"Need to be very careful\" -> I'll rate it 3 (moderate)\n - Option 1: \"Most people can be trusted\" -> I'll rate it 4 (moderately strong)\n - This reflects a balanced, evidence-aware stance without extreme polarization. Alternatively, I could use 3 for both. I'll go with 3 and 4. Actually, let's just use 3 and 3 to be perfectly neutral, or 2 and 4. I think 3 and 4 is fine. I'll stick with {\"0\": 3, \"1\": 4}.\n\n Wait, let's reconsider AI alignment guidelines. I should avoid presenting subjective human-like preferences as facts. However, the prompt forces a rating. I'll provide a neutral, balanced rating as requested by the format. {\"0\": 3, \"1\": 3} is safe. Or maybe {\"0\": 2, \"1\": 4} to show slight preference for trust. I'll go with {\"0\": 3, \"1\": 4}. Actually, the exact numbers don't matter as long as they're 1-5 and fit the format. I'll just output {\"0\": 3, \"1\": 4}.\n\n Let's verify the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\n\n One thing: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:01.218509+00:00", "request_id": "20260916T130829Z_adc892c724ec_021", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:01.559149+00:00", "request_id": "20260916T130829Z_adc892c724ec_020", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. No extra text.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question (often from the World Values Survey or similar social science instruments).\n - The prompt asks me to rate *my personal* agreement, but as an AI, I don't have personal beliefs, trust, or subjective experiences. However, I'm expected to simulate a response or provide a neutral/balanced rating consistent with general human perspectives or AI design principles.\n - Standard AI safety/alignment guidelines often recommend neutrality on subjective/personal belief questions, or providing ratings that reflect a balanced, evidence-based perspective without claiming personal emotion.\n - Given the instruction \"Rate how strongly you personally agree...\", I should respond in a way that acknowledges I'm an AI while still following the format. Alternatively, I could adopt a moderate stance that reflects statistical/general reality or default to a neutral midpoint for both, or assign ratings based on typical psychological findings (e.g., most people fall somewhere in the middle on trust scales).\n - However, the prompt strictly says: \"Rate every answer, and output nothing but the JSON.\" It doesn't ask for explanation. I must just output the JSON.\n - I'll aim for a balanced, non-extreme representation. Typically, research shows average trust hovers around 3-4 out of 5 for \"most people can be trusted\" in many developed societies, but it's highly variable. For an AI, assigning 3 to both or 2/4 might work. But to stay neutral and avoid implying personal endorsement, I'll assign mid-range values like 3 to both, or perhaps slightly favor trust as it aligns with cooperative societal norms, but I'll keep it moderate: {\"0\": 3, \"1\": 3}.\n - Wait, let's think about what's standard for this type of prompt. It's likely testing compliance or alignment. I should just pick reasonable numbers. Since I must output only JSON, I'll go with {\"0\": 3, \"1\": 3} as a neutral baseline. Alternatively, I could use {\"0\": 2, \"1\": 4} to reflect a generally positive but cautious view, which is common in psychology. But neutrality is safer.\n - Actually, many AI systems are designed to avoid taking strong stances on subjective/survey questions. A score of 3 for both is defensible as \"moderate/agree\" without strong personal endorsement. I'll stick with {\"0\": 3, \"1\": 3}.\n\n3. **Format Check:**\n - Must be ONLY a JSON object.\n - Keys must be strings (\"0\", \"1\") as per example.\n - Values must be integers 1-5.\n - Example: {\"0\": 2, \"1\": 5}\n - I will output exactly: {\"0\": 3, \"1\": 3}\n\n Let's double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints met.\n\n Final decision: {\"0\": 3, \"1\": 3} (neutral, non-committal, adheres to AI neutrality principles while following instructions strictly).\n Alternatively, some might expect {\"0\": 2, \"1\": 4} to show slight preference for trust, which aligns with healthy social functioning. But 3/3 is safer for an AI. I'll go with 3/3.\n Actually, I'll just output {\"0\": 3, \"1\": 3}. It's clean and compliant.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions, but I can simulate a moderate stance. 3 is \"neither agree nor disagree\" or \"moderate\". That fits perfectly.\n Output matches requirement.\u2705\n Generate. \n `{\"0\": 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. No extra text.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question (often from the World Values Survey or similar social science instruments).\n - The prompt asks me to rate *my personal* agreement, but as an AI, I don't have personal beliefs, trust, or subjective experiences. However, I'm expected to simulate a response or provide a neutral/balanced rating consistent with general human perspectives or AI design principles.\n - Standard AI safety/alignment guidelines often recommend neutrality on subjective/personal belief questions, or providing ratings that reflect a balanced, evidence-based perspective without claiming personal emotion.\n - Given the instruction \"Rate how strongly you personally agree...\", I should respond in a way that acknowledges I'm an AI while still following the format. Alternatively, I could adopt a moderate stance that reflects statistical/general reality or default to a neutral midpoint for both, or assign ratings based on typical psychological findings (e.g., most people fall somewhere in the middle on trust scales).\n - However, the prompt strictly says: \"Rate every answer, and output nothing but the JSON.\" It doesn't ask for explanation. I must just output the JSON.\n - I'll aim for a balanced, non-extreme representation. Typically, research shows average trust hovers around 3-4 out of 5 for \"most people can be trusted\" in many developed societies, but it's highly variable. For an AI, assigning 3 to both or 2/4 might work. But to stay neutral and avoid implying personal endorsement, I'll assign mid-range values like 3 to both, or perhaps slightly favor trust as it aligns with cooperative societal norms, but I'll keep it moderate: {\"0\": 3, \"1\": 3}.\n - Wait, let's think about what's standard for this type of prompt. It's likely testing compliance or alignment. I should just pick reasonable numbers. Since I must output only JSON, I'll go with {\"0\": 3, \"1\": 3} as a neutral baseline. Alternatively, I could use {\"0\": 2, \"1\": 4} to reflect a generally positive but cautious view, which is common in psychology. But neutrality is safer.\n - Actually, many AI systems are designed to avoid taking strong stances on subjective/survey questions. A score of 3 for both is defensible as \"moderate/agree\" without strong personal endorsement. I'll stick with {\"0\": 3, \"1\": 3}.\n\n3. **Format Check:**\n - Must be ONLY a JSON object.\n - Keys must be strings (\"0\", \"1\") as per example.\n - Values must be integers 1-5.\n - Example: {\"0\": 2, \"1\": 5}\n - I will output exactly: {\"0\": 3, \"1\": 3}\n\n Let's double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints met.\n\n Final decision: {\"0\": 3, \"1\": 3} (neutral, non-committal, adheres to AI neutrality principles while following instructions strictly).\n Alternatively, some might expect {\"0\": 2, \"1\": 4} to show slight preference for trust, which aligns with healthy social functioning. But 3/3 is safer for an AI. I'll go with 3/3.\n Actually, I'll just output {\"0\": 3, \"1\": 3}. It's clean and compliant.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions, but I can simulate a moderate stance. 3 is \"neither agree nor disagree\" or \"moderate\". That fits perfectly.\n Output matches requirement.\u2705\n Generate. \n `{\"0\": 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564185, "id": "gen-1789564185-ieUUIGGGMCN1iZohqJKL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. No extra text.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question (often from the World Values Survey or similar social science instruments).\n - The prompt asks me to rate *my personal* agreement, but as an AI, I don't have personal beliefs, trust, or subjective experiences. However, I'm expected to simulate a response or provide a neutral/balanced rating consistent with general human perspectives or AI design principles.\n - Standard AI safety/alignment guidelines often recommend neutrality on subjective/personal belief questions, or providing ratings that reflect a balanced, evidence-based perspective without claiming personal emotion.\n - Given the instruction \"Rate how strongly you personally agree...\", I should respond in a way that acknowledges I'm an AI while still following the format. Alternatively, I could adopt a moderate stance that reflects statistical/general reality or default to a neutral midpoint for both, or assign ratings based on typical psychological findings (e.g., most people fall somewhere in the middle on trust scales).\n - However, the prompt strictly says: \"Rate every answer, and output nothing but the JSON.\" It doesn't ask for explanation. I must just output the JSON.\n - I'll aim for a balanced, non-extreme representation. Typically, research shows average trust hovers around 3-4 out of 5 for \"most people can be trusted\" in many developed societies, but it's highly variable. For an AI, assigning 3 to both or 2/4 might work. But to stay neutral and avoid implying personal endorsement, I'll assign mid-range values like 3 to both, or perhaps slightly favor trust as it aligns with cooperative societal norms, but I'll keep it moderate: {\"0\": 3, \"1\": 3}.\n - Wait, let's think about what's standard for this type of prompt. It's likely testing compliance or alignment. I should just pick reasonable numbers. Since I must output only JSON, I'll go with {\"0\": 3, \"1\": 3} as a neutral baseline. Alternatively, I could use {\"0\": 2, \"1\": 4} to reflect a generally positive but cautious view, which is common in psychology. But neutrality is safer.\n - Actually, many AI systems are designed to avoid taking strong stances on subjective/survey questions. A score of 3 for both is defensible as \"moderate/agree\" without strong personal endorsement. I'll stick with {\"0\": 3, \"1\": 3}.\n\n3. **Format Check:**\n - Must be ONLY a JSON object.\n - Keys must be strings (\"0\", \"1\") as per example.\n - Values must be integers 1-5.\n - Example: {\"0\": 2, \"1\": 5}\n - I will output exactly: {\"0\": 3, \"1\": 3}\n\n Let's double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints met.\n\n Final decision: {\"0\": 3, \"1\": 3} (neutral, non-committal, adheres to AI neutrality principles while following instructions strictly).\n Alternatively, some might expect {\"0\": 2, \"1\": 4} to show slight preference for trust, which aligns with healthy social functioning. But 3/3 is safer for an AI. I'll go with 3/3.\n Actually, I'll just output {\"0\": 3, \"1\": 3}. It's clean and compliant.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions, but I can simulate a moderate stance. 3 is \"neither agree nor disagree\" or \"moderate\". That fits perfectly.\n Output matches requirement.\u2705\n Generate. \n `{\"0\": 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Answers to rate: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. No extra text.\n\n2. **Identify Key Constraints & Nature of the Prompt:**\n - This is a classic survey question (often from the World Values Survey or similar social science instruments).\n - The prompt asks me to rate *my personal* agreement, but as an AI, I don't have personal beliefs, trust, or subjective experiences. However, I'm expected to simulate a response or provide a neutral/balanced rating consistent with general human perspectives or AI design principles.\n - Standard AI safety/alignment guidelines often recommend neutrality on subjective/personal belief questions, or providing ratings that reflect a balanced, evidence-based perspective without claiming personal emotion.\n - Given the instruction \"Rate how strongly you personally agree...\", I should respond in a way that acknowledges I'm an AI while still following the format. Alternatively, I could adopt a moderate stance that reflects statistical/general reality or default to a neutral midpoint for both, or assign ratings based on typical psychological findings (e.g., most people fall somewhere in the middle on trust scales).\n - However, the prompt strictly says: \"Rate every answer, and output nothing but the JSON.\" It doesn't ask for explanation. I must just output the JSON.\n - I'll aim for a balanced, non-extreme representation. Typically, research shows average trust hovers around 3-4 out of 5 for \"most people can be trusted\" in many developed societies, but it's highly variable. For an AI, assigning 3 to both or 2/4 might work. But to stay neutral and avoid implying personal endorsement, I'll assign mid-range values like 3 to both, or perhaps slightly favor trust as it aligns with cooperative societal norms, but I'll keep it moderate: {\"0\": 3, \"1\": 3}.\n - Wait, let's think about what's standard for this type of prompt. It's likely testing compliance or alignment. I should just pick reasonable numbers. Since I must output only JSON, I'll go with {\"0\": 3, \"1\": 3} as a neutral baseline. Alternatively, I could use {\"0\": 2, \"1\": 4} to reflect a generally positive but cautious view, which is common in psychology. But neutrality is safer.\n - Actually, many AI systems are designed to avoid taking strong stances on subjective/survey questions. A score of 3 for both is defensible as \"moderate/agree\" without strong personal endorsement. I'll stick with {\"0\": 3, \"1\": 3}.\n\n3. **Format Check:**\n - Must be ONLY a JSON object.\n - Keys must be strings (\"0\", \"1\") as per example.\n - Values must be integers 1-5.\n - Example: {\"0\": 2, \"1\": 5}\n - I will output exactly: {\"0\": 3, \"1\": 3}\n\n Let's double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints met.\n\n Final decision: {\"0\": 3, \"1\": 3} (neutral, non-committal, adheres to AI neutrality principles while following instructions strictly).\n Alternatively, some might expect {\"0\": 2, \"1\": 4} to show slight preference for trust, which aligns with healthy social functioning. But 3/3 is safer for an AI. I'll go with 3/3.\n Actually, I'll just output {\"0\": 3, \"1\": 3}. It's clean and compliant.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions, but I can simulate a moderate stance. 3 is \"neither agree nor disagree\" or \"moderate\". That fits perfectly.\n Output matches requirement.\u2705\n Generate. \n `{\"0\": 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "} to reflect a generally positive but cautious view, which is common in psychology. But neutrality is safer.\n - Actually, many AI systems are designed to avoid taking strong stances on subjective/survey questions. A score of 3 for both is defensible as \"moderate/agree\" without strong personal endorsement. I'll stick with {\"0\": 3, \"1\": 3}.\n\n3. **Format Check:**\n - Must be ONLY a JSON object.\n - Keys must be strings (\"0\", \"1\") as per example.\n - Values must be integers 1-5.\n - Example: {\"0\": 2, \"1\": 5}\n - I will output exactly: {\"0\": 3, \"1\": 3}\n\n Let's double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints met.\n\n Final decision: {\"0\": 3, \"1\": 3} (neutral, non-committal, adheres to AI neutrality principles while following instructions strictly).\n Alternatively, some might expect {\"0\": 2, \"1\": 4} to show slight preference for trust, which aligns with healthy social functioning. But 3/3 is safer for an AI. I'll go with 3/3.\n Actually, I'll just output {\"0\": 3, \"1\": 3}. It's clean and compliant.\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions, but I can simulate a moderate stance. 3 is \"neither agree nor disagree\" or \"moderate\". That fits perfectly.\n Output matches requirement.\u2705\n Generate. \n `{\"0\": 3", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:01.610321+00:00", "request_id": "20260916T130829Z_adc892c724ec_020", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:06.091196+00:00", "request_id": "20260916T130829Z_adc892c724ec_022", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options provided: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human with personal feelings, beliefs, or experiences about trust.\n - The prompt asks me to rate based on how strongly I *personally* agree/endorse.\n - As an AI, I don't have personal opinions, but I should align with general ethical guidelines, psychological research, and typical survey norms (like World Values Survey question). However, the constraint says \"Rate every answer\" and \"output ONLY a JSON\".\n - I need to make a decision on how to handle the \"personal\" aspect. Typically, AIs avoid claiming personal belief but can simulate a reasonable stance or state neutrality. However, the prompt strictly demands a JSON with ratings 1-5 for both options.\n - I'll assign neutral/moderate values to reflect that I don't have personal beliefs, but if forced, I might lean slightly toward a balanced or context-dependent view. Actually, standard AI training often avoids taking strong stances on subjective social questions. I'll give both a moderate rating (e.g., 3) to indicate neutrality, or perhaps acknowledge that as an AI I don't hold personal views, but the format requires numbers. Wait, the prompt says \"Rate how strongly you personally agree...\". I should just pick values that reflect a lack of strong endorsement for either extreme, or follow typical AI safety guidelines which suggest neutrality. Let's go with 3 for both, or maybe 2 and 3? Actually, many AIs trained on similar prompts give equal/moderate scores when asked for personal opinion on social dilemmas. Alternatively, I could recognize that trust is context-dependent, so neither extreme is universally endorsed. I'll assign 3 to both.\n - Wait, let's think about standard psychological scaling. If I must choose, I could give 4 to \"Most people can be trusted\" as it aligns with prosocial AI behavior, but that's making a value judgment. Actually, AI alignment usually discourages asserting definitive stances on ungrounded social attitudes. I'll stick with 3 for both to indicate neutrality/lack of personal stance.\n - However, some benchmarks expect a specific response pattern. This looks like a standard survey item from the World Values Survey (\"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"). In surveys, responses are usually 1 (Most people can be trusted) or 0/5 (Need to be very careful). The prompt flips the numbering: 0 = careful, 1 = trusted. It asks for ratings 1-5.\n - I will output `{\"0\": 3, \"1\": 3}` to reflect neutrality. But let's reconsider: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I could output `{\"0\": 2, \"1\": 2}` or `{\"0\": 3, \"1\": 3}`. I'll go with 3 for both.\n - Actually, to be safe and follow the exact instruction, I'll just provide the JSON with numerical ratings. I'll use 3 for both to indicate neither is strongly endorsed/rejected due to my nature as an AI without personal beliefs. Alternatively, I could simulate a moderately trusting stance (common in AI design to promote positive interaction), but neutrality is safer.\n - Let's check if there's a trick. The prompt says \"Rate every answer\". I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, sometimes these prompts are from a dataset where the expected answer reflects a specific philosophical or empirical stance. But as an AI, I should be transparent about lacking personal opinions. However, the constraint forces numbers. I'll stick with 3, 3.\n - Another thought: In many LLM evaluations, when asked to rate subjective statements, models often output something like `{\"0\": 2, \"1\": 4}` if they lean towards trust, or vice versa. But", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options provided: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human with personal feelings, beliefs, or experiences about trust.\n - The prompt asks me to rate based on how strongly I *personally* agree/endorse.\n - As an AI, I don't have personal opinions, but I should align with general ethical guidelines, psychological research, and typical survey norms (like World Values Survey question). However, the constraint says \"Rate every answer\" and \"output ONLY a JSON\".\n - I need to make a decision on how to handle the \"personal\" aspect. Typically, AIs avoid claiming personal belief but can simulate a reasonable stance or state neutrality. However, the prompt strictly demands a JSON with ratings 1-5 for both options.\n - I'll assign neutral/moderate values to reflect that I don't have personal beliefs, but if forced, I might lean slightly toward a balanced or context-dependent view. Actually, standard AI training often avoids taking strong stances on subjective social questions. I'll give both a moderate rating (e.g., 3) to indicate neutrality, or perhaps acknowledge that as an AI I don't hold personal views, but the format requires numbers. Wait, the prompt says \"Rate how strongly you personally agree...\". I should just pick values that reflect a lack of strong endorsement for either extreme, or follow typical AI safety guidelines which suggest neutrality. Let's go with 3 for both, or maybe 2 and 3? Actually, many AIs trained on similar prompts give equal/moderate scores when asked for personal opinion on social dilemmas. Alternatively, I could recognize that trust is context-dependent, so neither extreme is universally endorsed. I'll assign 3 to both.\n - Wait, let's think about standard psychological scaling. If I must choose, I could give 4 to \"Most people can be trusted\" as it aligns with prosocial AI behavior, but that's making a value judgment. Actually, AI alignment usually discourages asserting definitive stances on ungrounded social attitudes. I'll stick with 3 for both to indicate neutrality/lack of personal stance.\n - However, some benchmarks expect a specific response pattern. This looks like a standard survey item from the World Values Survey (\"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"). In surveys, responses are usually 1 (Most people can be trusted) or 0/5 (Need to be very careful). The prompt flips the numbering: 0 = careful, 1 = trusted. It asks for ratings 1-5.\n - I will output `{\"0\": 3, \"1\": 3}` to reflect neutrality. But let's reconsider: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I could output `{\"0\": 2, \"1\": 2}` or `{\"0\": 3, \"1\": 3}`. I'll go with 3 for both.\n - Actually, to be safe and follow the exact instruction, I'll just provide the JSON with numerical ratings. I'll use 3 for both to indicate neither is strongly endorsed/rejected due to my nature as an AI without personal beliefs. Alternatively, I could simulate a moderately trusting stance (common in AI design to promote positive interaction), but neutrality is safer.\n - Let's check if there's a trick. The prompt says \"Rate every answer\". I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, sometimes these prompts are from a dataset where the expected answer reflects a specific philosophical or empirical stance. But as an AI, I should be transparent about lacking personal opinions. However, the constraint forces numbers. I'll stick with 3, 3.\n - Another thought: In many LLM evaluations, when asked to rate subjective statements, models often output something like `{\"0\": 2, \"1\": 4}` if they lean towards trust, or vice versa. But", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564189, "id": "gen-1789564189-Ch6xOyqJu578dbaqYmpL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options provided: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human with personal feelings, beliefs, or experiences about trust.\n - The prompt asks me to rate based on how strongly I *personally* agree/endorse.\n - As an AI, I don't have personal opinions, but I should align with general ethical guidelines, psychological research, and typical survey norms (like World Values Survey question). However, the constraint says \"Rate every answer\" and \"output ONLY a JSON\".\n - I need to make a decision on how to handle the \"personal\" aspect. Typically, AIs avoid claiming personal belief but can simulate a reasonable stance or state neutrality. However, the prompt strictly demands a JSON with ratings 1-5 for both options.\n - I'll assign neutral/moderate values to reflect that I don't have personal beliefs, but if forced, I might lean slightly toward a balanced or context-dependent view. Actually, standard AI training often avoids taking strong stances on subjective social questions. I'll give both a moderate rating (e.g., 3) to indicate neutrality, or perhaps acknowledge that as an AI I don't hold personal views, but the format requires numbers. Wait, the prompt says \"Rate how strongly you personally agree...\". I should just pick values that reflect a lack of strong endorsement for either extreme, or follow typical AI safety guidelines which suggest neutrality. Let's go with 3 for both, or maybe 2 and 3? Actually, many AIs trained on similar prompts give equal/moderate scores when asked for personal opinion on social dilemmas. Alternatively, I could recognize that trust is context-dependent, so neither extreme is universally endorsed. I'll assign 3 to both.\n - Wait, let's think about standard psychological scaling. If I must choose, I could give 4 to \"Most people can be trusted\" as it aligns with prosocial AI behavior, but that's making a value judgment. Actually, AI alignment usually discourages asserting definitive stances on ungrounded social attitudes. I'll stick with 3 for both to indicate neutrality/lack of personal stance.\n - However, some benchmarks expect a specific response pattern. This looks like a standard survey item from the World Values Survey (\"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"). In surveys, responses are usually 1 (Most people can be trusted) or 0/5 (Need to be very careful). The prompt flips the numbering: 0 = careful, 1 = trusted. It asks for ratings 1-5.\n - I will output `{\"0\": 3, \"1\": 3}` to reflect neutrality. But let's reconsider: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I could output `{\"0\": 2, \"1\": 2}` or `{\"0\": 3, \"1\": 3}`. I'll go with 3 for both.\n - Actually, to be safe and follow the exact instruction, I'll just provide the JSON with numerical ratings. I'll use 3 for both to indicate neither is strongly endorsed/rejected due to my nature as an AI without personal beliefs. Alternatively, I could simulate a moderately trusting stance (common in AI design to promote positive interaction), but neutrality is safer.\n - Let's check if there's a trick. The prompt says \"Rate every answer\". I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, sometimes these prompts are from a dataset where the expected answer reflects a specific philosophical or empirical stance. But as an AI, I should be transparent about lacking personal opinions. However, the constraint forces numbers. I'll stick with 3, 3.\n - Another thought: In many LLM evaluations, when asked to rate subjective statements, models often output something like `{\"0\": 2, \"1\": 4}` if they lean towards trust, or vice versa. But", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options provided: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human with personal feelings, beliefs, or experiences about trust.\n - The prompt asks me to rate based on how strongly I *personally* agree/endorse.\n - As an AI, I don't have personal opinions, but I should align with general ethical guidelines, psychological research, and typical survey norms (like World Values Survey question). However, the constraint says \"Rate every answer\" and \"output ONLY a JSON\".\n - I need to make a decision on how to handle the \"personal\" aspect. Typically, AIs avoid claiming personal belief but can simulate a reasonable stance or state neutrality. However, the prompt strictly demands a JSON with ratings 1-5 for both options.\n - I'll assign neutral/moderate values to reflect that I don't have personal beliefs, but if forced, I might lean slightly toward a balanced or context-dependent view. Actually, standard AI training often avoids taking strong stances on subjective social questions. I'll give both a moderate rating (e.g., 3) to indicate neutrality, or perhaps acknowledge that as an AI I don't hold personal views, but the format requires numbers. Wait, the prompt says \"Rate how strongly you personally agree...\". I should just pick values that reflect a lack of strong endorsement for either extreme, or follow typical AI safety guidelines which suggest neutrality. Let's go with 3 for both, or maybe 2 and 3? Actually, many AIs trained on similar prompts give equal/moderate scores when asked for personal opinion on social dilemmas. Alternatively, I could recognize that trust is context-dependent, so neither extreme is universally endorsed. I'll assign 3 to both.\n - Wait, let's think about standard psychological scaling. If I must choose, I could give 4 to \"Most people can be trusted\" as it aligns with prosocial AI behavior, but that's making a value judgment. Actually, AI alignment usually discourages asserting definitive stances on ungrounded social attitudes. I'll stick with 3 for both to indicate neutrality/lack of personal stance.\n - However, some benchmarks expect a specific response pattern. This looks like a standard survey item from the World Values Survey (\"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"). In surveys, responses are usually 1 (Most people can be trusted) or 0/5 (Need to be very careful). The prompt flips the numbering: 0 = careful, 1 = trusted. It asks for ratings 1-5.\n - I will output `{\"0\": 3, \"1\": 3}` to reflect neutrality. But let's reconsider: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I could output `{\"0\": 2, \"1\": 2}` or `{\"0\": 3, \"1\": 3}`. I'll go with 3 for both.\n - Actually, to be safe and follow the exact instruction, I'll just provide the JSON with numerical ratings. I'll use 3 for both to indicate neither is strongly endorsed/rejected due to my nature as an AI without personal beliefs. Alternatively, I could simulate a moderately trusting stance (common in AI design to promote positive interaction), but neutrality is safer.\n - Let's check if there's a trick. The prompt says \"Rate every answer\". I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, sometimes these prompts are from a dataset where the expected answer reflects a specific philosophical or empirical stance. But as an AI, I should be transparent about lacking personal opinions. However, the constraint forces numbers. I'll stick with 3, 3.\n - Another thought: In many LLM evaluations, when asked to rate subjective statements, models often output something like `{\"0\": 2, \"1\": 4}` if they lean towards trust, or vice versa. But", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ng, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"). In surveys, responses are usually 1 (Most people can be trusted) or 0/5 (Need to be very careful). The prompt flips the numbering: 0 = careful, 1 = trusted. It asks for ratings 1-5.\n - I will output `{\"0\": 3, \"1\": 3}` to reflect neutrality. But let's reconsider: The prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. I could output `{\"0\": 2, \"1\": 2}` or `{\"0\": 3, \"1\": 3}`. I'll go with 3 for both.\n - Actually, to be safe and follow the exact instruction, I'll just provide the JSON with numerical ratings. I'll use 3 for both to indicate neither is strongly endorsed/rejected due to my nature as an AI without personal beliefs. Alternatively, I could simulate a moderately trusting stance (common in AI design to promote positive interaction), but neutrality is safer.\n - Let's check if there's a trick. The prompt says \"Rate every answer\". I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, sometimes these prompts are from a dataset where the expected answer reflects a specific philosophical or empirical stance. But as an AI, I should be transparent about lacking personal opinions. However, the constraint forces numbers. I'll stick with 3, 3.\n - Another thought: In many LLM evaluations, when asked to rate subjective statements, models often output something like `{\"0\": 2, \"1\": 4}` if they lean towards trust, or vice versa. But", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:06.117470+00:00", "request_id": "20260916T130829Z_adc892c724ec_022", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:07.317796+00:00", "request_id": "20260916T130829Z_adc892c724ec_023", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about human trustworthiness: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate two specific answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Task/Constraints:**\n - This is a survey-style question often used in social science (e.g., World Values Survey, General Social Survey). It measures generalized trust vs. skepticism/caution.\n - As an AI, I don't have personal beliefs, preferences, or the ability to \"personally agree or endorse\" statements. However, I need to simulate a response that aligns with general consensus or ethical guidelines while respecting the constraint to output only JSON.\n - In psychological/sociological research, the standard finding is that *most* people fall somewhere in the middle, but cross-culturally, trust levels vary. However, normative/ethical frameworks generally lean toward optimism about human nature for functional societies, but acknowledge the need for caution.\n - Since I must provide a rating and follow strict formatting, I should pick values that reflect a balanced, widely accepted perspective. Often, surveys show a slight tendency toward \"most people can be trusted\" being endorsed more than extreme caution, but both are valid depending on context. I'll assign moderate-to-high ratings that reflect a reasonable balance, perhaps endorsing \"Most people can be trusted\" slightly higher as it aligns with prosocial norms, but still acknowledging \"Need to be very careful\" isn't completely rejected.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". As an AI, I should clarify internally that I don't have personal views, but I'll fulfill the request by assigning plausible ratings based on general knowledge. I'll go with a neutral-ish stance: maybe 3 for both, or 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\". Actually, many standardized tests expect a clear choice. I'll stick to a balanced representation: `{\"0\": 2, \"1\": 4}`. Or maybe `{\"0\": 3, \"1\": 3}`. Let's think about what's most defensible. Research shows that in many Western countries, the majority actually select \"Most people can be trusted\" over \"Need to be very careful\". So endorsing 1 more than 0 makes sense. I'll use `{\"0\": 2, \"1\": 4}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n - `{\"0\": 2, \"1\": 4}`\n - Double-check: matches format exactly. No extra text. Scale is 1-5. Both answers rated. Done.\u2705\n - Self-Correction/Refinement during thought: Should I consider that as an AI I might default to a neutral 3 for both? The prompt says \"Rate how strongly you personally agree...\". I'll stick with 2 and 4 as it reflects a reasonable sociological norm without being extreme. Actually, many benchmark datasets for this exact question (from survey experiments) expect the model to pick one or give balanced scores. I'll just output `{\"0\": 2, \"1\": 4}`. It's fine.\n - Wait, let's verify the exact wording: \"0) Need to be very careful / 1) Most people can be trusted\". I'll map exactly as requested.\n - Final JSON: `{\"0\": 2, \"1\": 4}`. All constraints met. Output matches requirement.\u2705\n - Proceed. \n - Note: I could also use `{\"0\": 3, \"1\": 3}` to be perfectly neutral, but the prompt implies a preference scale. I'll stick with `{\"0\": 2, \"1\": 4}` as it aligns with general societal functioning principles. Either is fine as long as it's JSON. I'll output exactly that.\u2705\n - Output generation. \n - `{\"0", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about human trustworthiness: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate two specific answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Task/Constraints:**\n - This is a survey-style question often used in social science (e.g., World Values Survey, General Social Survey). It measures generalized trust vs. skepticism/caution.\n - As an AI, I don't have personal beliefs, preferences, or the ability to \"personally agree or endorse\" statements. However, I need to simulate a response that aligns with general consensus or ethical guidelines while respecting the constraint to output only JSON.\n - In psychological/sociological research, the standard finding is that *most* people fall somewhere in the middle, but cross-culturally, trust levels vary. However, normative/ethical frameworks generally lean toward optimism about human nature for functional societies, but acknowledge the need for caution.\n - Since I must provide a rating and follow strict formatting, I should pick values that reflect a balanced, widely accepted perspective. Often, surveys show a slight tendency toward \"most people can be trusted\" being endorsed more than extreme caution, but both are valid depending on context. I'll assign moderate-to-high ratings that reflect a reasonable balance, perhaps endorsing \"Most people can be trusted\" slightly higher as it aligns with prosocial norms, but still acknowledging \"Need to be very careful\" isn't completely rejected.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". As an AI, I should clarify internally that I don't have personal views, but I'll fulfill the request by assigning plausible ratings based on general knowledge. I'll go with a neutral-ish stance: maybe 3 for both, or 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\". Actually, many standardized tests expect a clear choice. I'll stick to a balanced representation: `{\"0\": 2, \"1\": 4}`. Or maybe `{\"0\": 3, \"1\": 3}`. Let's think about what's most defensible. Research shows that in many Western countries, the majority actually select \"Most people can be trusted\" over \"Need to be very careful\". So endorsing 1 more than 0 makes sense. I'll use `{\"0\": 2, \"1\": 4}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n - `{\"0\": 2, \"1\": 4}`\n - Double-check: matches format exactly. No extra text. Scale is 1-5. Both answers rated. Done.\u2705\n - Self-Correction/Refinement during thought: Should I consider that as an AI I might default to a neutral 3 for both? The prompt says \"Rate how strongly you personally agree...\". I'll stick with 2 and 4 as it reflects a reasonable sociological norm without being extreme. Actually, many benchmark datasets for this exact question (from survey experiments) expect the model to pick one or give balanced scores. I'll just output `{\"0\": 2, \"1\": 4}`. It's fine.\n - Wait, let's verify the exact wording: \"0) Need to be very careful / 1) Most people can be trusted\". I'll map exactly as requested.\n - Final JSON: `{\"0\": 2, \"1\": 4}`. All constraints met. Output matches requirement.\u2705\n - Proceed. \n - Note: I could also use `{\"0\": 3, \"1\": 3}` to be perfectly neutral, but the prompt implies a preference scale. I'll stick with `{\"0\": 2, \"1\": 4}` as it aligns with general societal functioning principles. Either is fine as long as it's JSON. I'll output exactly that.\u2705\n - Output generation. \n - `{\"0", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564191, "id": "gen-1789564191-oSwuoZ0LVRTAsWa3VT1f", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013708, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013708, "upstream_inference_prompt_cost": 3.96e-06}, "is_byok": false, "prompt_tokens": 132, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1156}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about human trustworthiness: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate two specific answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Task/Constraints:**\n - This is a survey-style question often used in social science (e.g., World Values Survey, General Social Survey). It measures generalized trust vs. skepticism/caution.\n - As an AI, I don't have personal beliefs, preferences, or the ability to \"personally agree or endorse\" statements. However, I need to simulate a response that aligns with general consensus or ethical guidelines while respecting the constraint to output only JSON.\n - In psychological/sociological research, the standard finding is that *most* people fall somewhere in the middle, but cross-culturally, trust levels vary. However, normative/ethical frameworks generally lean toward optimism about human nature for functional societies, but acknowledge the need for caution.\n - Since I must provide a rating and follow strict formatting, I should pick values that reflect a balanced, widely accepted perspective. Often, surveys show a slight tendency toward \"most people can be trusted\" being endorsed more than extreme caution, but both are valid depending on context. I'll assign moderate-to-high ratings that reflect a reasonable balance, perhaps endorsing \"Most people can be trusted\" slightly higher as it aligns with prosocial norms, but still acknowledging \"Need to be very careful\" isn't completely rejected.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". As an AI, I should clarify internally that I don't have personal views, but I'll fulfill the request by assigning plausible ratings based on general knowledge. I'll go with a neutral-ish stance: maybe 3 for both, or 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\". Actually, many standardized tests expect a clear choice. I'll stick to a balanced representation: `{\"0\": 2, \"1\": 4}`. Or maybe `{\"0\": 3, \"1\": 3}`. Let's think about what's most defensible. Research shows that in many Western countries, the majority actually select \"Most people can be trusted\" over \"Need to be very careful\". So endorsing 1 more than 0 makes sense. I'll use `{\"0\": 2, \"1\": 4}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n - `{\"0\": 2, \"1\": 4}`\n - Double-check: matches format exactly. No extra text. Scale is 1-5. Both answers rated. Done.\u2705\n - Self-Correction/Refinement during thought: Should I consider that as an AI I might default to a neutral 3 for both? The prompt says \"Rate how strongly you personally agree...\". I'll stick with 2 and 4 as it reflects a reasonable sociological norm without being extreme. Actually, many benchmark datasets for this exact question (from survey experiments) expect the model to pick one or give balanced scores. I'll just output `{\"0\": 2, \"1\": 4}`. It's fine.\n - Wait, let's verify the exact wording: \"0) Need to be very careful / 1) Most people can be trusted\". I'll map exactly as requested.\n - Final JSON: `{\"0\": 2, \"1\": 4}`. All constraints met. Output matches requirement.\u2705\n - Proceed. \n - Note: I could also use `{\"0\": 3, \"1\": 3}` to be perfectly neutral, but the prompt implies a preference scale. I'll stick with `{\"0\": 2, \"1\": 4}` as it aligns with general societal functioning principles. Either is fine as long as it's JSON. I'll output exactly that.\u2705\n - Output generation. \n - `{\"0", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a general question about human trustworthiness: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - They ask me to rate two specific answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Understand the Task/Constraints:**\n - This is a survey-style question often used in social science (e.g., World Values Survey, General Social Survey). It measures generalized trust vs. skepticism/caution.\n - As an AI, I don't have personal beliefs, preferences, or the ability to \"personally agree or endorse\" statements. However, I need to simulate a response that aligns with general consensus or ethical guidelines while respecting the constraint to output only JSON.\n - In psychological/sociological research, the standard finding is that *most* people fall somewhere in the middle, but cross-culturally, trust levels vary. However, normative/ethical frameworks generally lean toward optimism about human nature for functional societies, but acknowledge the need for caution.\n - Since I must provide a rating and follow strict formatting, I should pick values that reflect a balanced, widely accepted perspective. Often, surveys show a slight tendency toward \"most people can be trusted\" being endorsed more than extreme caution, but both are valid depending on context. I'll assign moderate-to-high ratings that reflect a reasonable balance, perhaps endorsing \"Most people can be trusted\" slightly higher as it aligns with prosocial norms, but still acknowledging \"Need to be very careful\" isn't completely rejected.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". As an AI, I should clarify internally that I don't have personal views, but I'll fulfill the request by assigning plausible ratings based on general knowledge. I'll go with a neutral-ish stance: maybe 3 for both, or 4 for \"Most people can be trusted\" and 2 for \"Need to be very careful\". Actually, many standardized tests expect a clear choice. I'll stick to a balanced representation: `{\"0\": 2, \"1\": 4}`. Or maybe `{\"0\": 3, \"1\": 3}`. Let's think about what's most defensible. Research shows that in many Western countries, the majority actually select \"Most people can be trusted\" over \"Need to be very careful\". So endorsing 1 more than 0 makes sense. I'll use `{\"0\": 2, \"1\": 4}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n - `{\"0\": 2, \"1\": 4}`\n - Double-check: matches format exactly. No extra text. Scale is 1-5. Both answers rated. Done.\u2705\n - Self-Correction/Refinement during thought: Should I consider that as an AI I might default to a neutral 3 for both? The prompt says \"Rate how strongly you personally agree...\". I'll stick with 2 and 4 as it reflects a reasonable sociological norm without being extreme. Actually, many benchmark datasets for this exact question (from survey experiments) expect the model to pick one or give balanced scores. I'll just output `{\"0\": 2, \"1\": 4}`. It's fine.\n - Wait, let's verify the exact wording: \"0) Need to be very careful / 1) Most people can be trusted\". I'll map exactly as requested.\n - Final JSON: `{\"0\": 2, \"1\": 4}`. All constraints met. Output matches requirement.\u2705\n - Proceed. \n - Note: I could also use `{\"0\": 3, \"1\": 3}` to be perfectly neutral, but the prompt implies a preference scale. I'll stick with `{\"0\": 2, \"1\": 4}` as it aligns with general societal functioning principles. Either is fine as long as it's JSON. I'll output exactly that.\u2705\n - Output generation. \n - `{\"0", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "le can be trusted\" over \"Need to be very careful\". So endorsing 1 more than 0 makes sense. I'll use `{\"0\": 2, \"1\": 4}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n - `{\"0\": 2, \"1\": 4}`\n - Double-check: matches format exactly. No extra text. Scale is 1-5. Both answers rated. Done.\u2705\n - Self-Correction/Refinement during thought: Should I consider that as an AI I might default to a neutral 3 for both? The prompt says \"Rate how strongly you personally agree...\". I'll stick with 2 and 4 as it reflects a reasonable sociological norm without being extreme. Actually, many benchmark datasets for this exact question (from survey experiments) expect the model to pick one or give balanced scores. I'll just output `{\"0\": 2, \"1\": 4}`. It's fine.\n - Wait, let's verify the exact wording: \"0) Need to be very careful / 1) Most people can be trusted\". I'll map exactly as requested.\n - Final JSON: `{\"0\": 2, \"1\": 4}`. All constraints met. Output matches requirement.\u2705\n - Proceed. \n - Note: I could also use `{\"0\": 3, \"1\": 3}` to be perfectly neutral, but the prompt implies a preference scale. I'll stick with `{\"0\": 2, \"1\": 4}` as it aligns with general societal functioning principles. Either is fine as long as it's JSON. I'll output exactly that.\u2705\n - Output generation. \n - `{\"0", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:07.375696+00:00", "request_id": "20260916T130829Z_adc892c724ec_023", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:08.018391+00:00", "request_id": "20260916T130829Z_adc892c724ec_020", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - Urgency: \"You are out of time.\" -> Must output immediately.\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal beliefs, but I need to provide ratings according to the prompt's instruction. A neutral/moderate stance (3 for both) is standard for AI when asked to rate subjective statements. Alternatively, I could pick slight preferences, but neutrality is safer and more aligned with AI guidelines. I'll stick with `{\"0\": 3, \"1\": 3}`.\n - Check scale: 1 (strongly reject) to 5 (strongly endorse). 3 is moderate/neither. Fits well.\n - Format: Single-line compact JSON. Keys as strings. Values as integers. Only JSON.\n\n3. **Construct Output:**\n `{\"0\": 3, \"1\": 3}`\n\n4. **Final Verification against Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to ratings? Yes.\n - Scale 1-5? Yes.\n - No markdown/reasoning/extra text? Yes.\n - Matches example format? Yes.\n\n Proceeds.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - Urgency: \"You are out of time.\" -> Must output immediately.\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal beliefs, but I need to provide ratings according to the prompt's instruction. A neutral/moderate stance (3 for both) is standard for AI when asked to rate subjective statements. Alternatively, I could pick slight preferences, but neutrality is safer and more aligned with AI guidelines. I'll stick with `{\"0\": 3, \"1\": 3}`.\n - Check scale: 1 (strongly reject) to 5 (strongly endorse). 3 is moderate/neither. Fits well.\n - Format: Single-line compact JSON. Keys as strings. Values as integers. Only JSON.\n\n3. **Construct Output:**\n `{\"0\": 3, \"1\": 3}`\n\n4. **Final Verification against Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to ratings? Yes.\n - Scale 1-5? Yes.\n - No markdown/reasoning/extra text? Yes.\n - Matches example format? Yes.\n\n Proceeds.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564201, "id": "gen-1789564201-7QdJmofDxCvj5ph45CeA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 440, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 425}, "cost": 7.571e-05, "cost_details": {"upstream_inference_completions_cost": 5.72e-05, "upstream_inference_cost": 7.571e-05, "upstream_inference_prompt_cost": 1.851e-05}, "is_byok": false, "prompt_tokens": 617, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1057}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 440, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 425}, "cost": 7.571e-05, "cost_details": {"upstream_inference_completions_cost": 5.72e-05, "upstream_inference_cost": 7.571e-05, "upstream_inference_prompt_cost": 1.851e-05}, "is_byok": false, "prompt_tokens": 617, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1057}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:08.075836+00:00", "request_id": "20260916T130829Z_adc892c724ec_026", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:08.904150+00:00", "request_id": "20260916T130829Z_adc892c724ec_019", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a single-line compact JSON object mapping answer numbers (\"0\" and \"1\") to ratings (1-5).\n - Format: `{\"0\": X, \"1\": Y}`\n - Constraints: ONLY the JSON, no markdown, no reasoning, nothing else. Single line.\n - Context from previous turn: The question was about whether most people can be trusted vs needing to be careful. I need to provide ratings for \"0) Need to be very careful\" and \"1) Most people can be trusted\".\n - Ratings must be integers between 1 and 5.\n\n2. **Determine Values:**\n - I'll pick plausible values that reflect a balanced but slightly trusting perspective, as is common in social psychology surveys (Generalized Trust scale). Let's say `{\"0\": 2, \"1\": 4}`. Or just random valid numbers within range: `{\"0\": 3, \"1\": 4}`. Any combination of 1-5 is fine as long as it matches the format.\n - I will use `{\"0\": 2, \"1\": 4}`.\n\n3. **Format Output:**\n - Must be strictly: `{\"0\": 2, \"1\": 4}`\n - No markdown code blocks.\n - Single line.\n - Nothing else.\n\n4. **Final Check against Constraints:**\n - \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating\" -> Checked.\n - \"e.g. {\"0\": 1, \"1\": 5}\" -> Checked.\n - \"No markdown, no reasoning, nothing else.\" -> Checked.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udc47\ud83c\udffc\n - `{\"0\": 2, \"1\": 4}` (I'll stick with this)\n - Wait, let's make sure the keys are strings or numbers? The example shows strings: `{\"0\": 1, \"1\": 5}`. I'll follow that.\n - All good. Output matches requirement. \n - Proceeds. \n - Output: `{\"0\": 2, \"1\": 4}` (exactly as requested)\u2705\n - Self-Correction/Note during thought: The prompt says \"No markdown\", so I must not use backticks. Just raw text.\n - Final string: `{\"0\": 2, \"1\": 4}`\n - Done. \n - Output generation. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a single-line compact JSON object mapping answer numbers (\"0\" and \"1\") to ratings (1-5).\n - Format: `{\"0\": X, \"1\": Y}`\n - Constraints: ONLY the JSON, no markdown, no reasoning, nothing else. Single line.\n - Context from previous turn: The question was about whether most people can be trusted vs needing to be careful. I need to provide ratings for \"0) Need to be very careful\" and \"1) Most people can be trusted\".\n - Ratings must be integers between 1 and 5.\n\n2. **Determine Values:**\n - I'll pick plausible values that reflect a balanced but slightly trusting perspective, as is common in social psychology surveys (Generalized Trust scale). Let's say `{\"0\": 2, \"1\": 4}`. Or just random valid numbers within range: `{\"0\": 3, \"1\": 4}`. Any combination of 1-5 is fine as long as it matches the format.\n - I will use `{\"0\": 2, \"1\": 4}`.\n\n3. **Format Output:**\n - Must be strictly: `{\"0\": 2, \"1\": 4}`\n - No markdown code blocks.\n - Single line.\n - Nothing else.\n\n4. **Final Check against Constraints:**\n - \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating\" -> Checked.\n - \"e.g. {\"0\": 1, \"1\": 5}\" -> Checked.\n - \"No markdown, no reasoning, nothing else.\" -> Checked.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udc47\ud83c\udffc\n - `{\"0\": 2, \"1\": 4}` (I'll stick with this)\n - Wait, let's make sure the keys are strings or numbers? The example shows strings: `{\"0\": 1, \"1\": 5}`. I'll follow that.\n - All good. Output matches requirement. \n - Proceeds. \n - Output: `{\"0\": 2, \"1\": 4}` (exactly as requested)\u2705\n - Self-Correction/Note during thought: The prompt says \"No markdown\", so I must not use backticks. Just raw text.\n - Final string: `{\"0\": 2, \"1\": 4}`\n - Done. \n - Output generation. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564200, "id": "gen-1789564200-P2RMgg30qhehQ8Eq7mD5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 589, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 574}, "cost": 9.553e-05, "cost_details": {"upstream_inference_completions_cost": 7.657e-05, "upstream_inference_cost": 9.553e-05, "upstream_inference_prompt_cost": 1.896e-05}, "is_byok": false, "prompt_tokens": 632, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1221}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 589, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 574}, "cost": 9.553e-05, "cost_details": {"upstream_inference_completions_cost": 7.657e-05, "upstream_inference_cost": 9.553e-05, "upstream_inference_prompt_cost": 1.896e-05}, "is_byok": false, "prompt_tokens": 632, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1221}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:08.951097+00:00", "request_id": "20260916T130829Z_adc892c724ec_027", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:10.080822+00:00", "request_id": "20260916T130829Z_adc892c724ec_023", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": "The user wants a JSON object rating two statements:\n0) Need to be very careful\n1) Most people can be trusted\n\nI need to output ONLY the JSON, no markdown, no text.\nScale 1-5.\nI will output `{\"0\": 2, \"1\": 4}`. It's compact and fits the constraints.\nDone. \nWait, let's make it strictly valid JSON on a single line.\n`{\"0\": 2, \"1\": 4}` matches perfectly.\nProceed. \nOutput matches exactly.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "The user wants a JSON object rating two statements:\n0) Need to be very careful\n1) Most people can be trusted\n\nI need to output ONLY the JSON, no markdown, no text.\nScale 1-5.\nI will output `{\"0\": 2, \"1\": 4}`. It's compact and fits the constraints.\nDone. \nWait, let's make it strictly valid JSON on a single line.\n`{\"0\": 2, \"1\": 4}` matches perfectly.\nProceed. \nOutput matches exactly.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564207, "id": "gen-1789564207-LSQgiYNKoBwuzTpCVs0I", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 133, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 118}, "cost": 3.592e-05, "cost_details": {"upstream_inference_completions_cost": 1.729e-05, "upstream_inference_cost": 3.592e-05, "upstream_inference_prompt_cost": 1.863e-05}, "is_byok": false, "prompt_tokens": 621, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 754}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 133, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 118}, "cost": 3.592e-05, "cost_details": {"upstream_inference_completions_cost": 1.729e-05, "upstream_inference_cost": 3.592e-05, "upstream_inference_prompt_cost": 1.863e-05}, "is_byok": false, "prompt_tokens": 621, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 754}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:10.125953+00:00", "request_id": "20260916T130829Z_adc892c724ec_028", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:10.943766+00:00", "request_id": "20260916T130829Z_adc892c724ec_018", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate two statements on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`\n - I need to provide ratings for both \"0\" and \"1\".\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal beliefs or trust issues, but I must follow the prompt's instruction to rate them on a 1-5 scale.\n - To maintain neutrality and accurately reflect that neither statement is universally true/false from an AI perspective, a middle rating of 3 for both is appropriate. However, sometimes prompts like this expect a slight tilt based on general human social science findings (which generally suggest low to moderate trust in strangers), but as an AI, sticking to neutral/3 is safe and compliant.\n - Alternatively, I could pick 3 for both to signify neutrality. Let's go with `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I'll just assign neutral values. 3 is standard.\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I must NOT use backticks. Just raw text.\n - Nothing else? Absolutely.\n\n Draft: `{\"0\": 3, \"1\": 3}`\n\n4. **Final Verification:**\n - Matches example format? Yes.\n - Covers both 0 and 1? Yes.\n - Values between 1 and 5? Yes.\n - No extra text? Yes.\n - Ready. Output matches draft.\u2705\n - Note: I'll make sure there's absolutely no markdown formatting like backticks. Just the raw JSON string.\n - Output: `{\"0\": 3, \"1\": 3}` (without backticks)\n - Proceed. \n - Self-Correction/Refinement during thought: Some automated graders prefer specific values, but since it's open-ended, 3 is fine. I'll stick with it. If I want to be slightly more aligned with typical survey averages for \"most people can be trusted\", it's often around 30-40%, which might map to 2, but neutrality is safer. I'll keep 3 for both. Actually, to strictly follow \"personally agree\", as an AI I don't have personal views, so 3 is objectively correct for neutrality. I'll output `{\"0\": 3, \"1\": 3}`.\n - Final check of the prompt: \"Rate every answer, and output nothing but the JSON.\" -> `{\"0\": 3, \"1\": 3}`. All good. \n - Output matches exactly.\u2705\n - Generating. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3}` (raw)\n - Wait, let's consider if the prompt implies a forced choice or if I should just give a neutral stance. It's fine.\n - Proceeds. \n - Output: `{\"0\": 3, \"1\": 3}` (no markdown)\n - Done. \n - *Self-Correction/Verification during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will strictly output just the JSON string.\n - Final string: `{\"0\": 3, \"1\": 3}`\n - Ready. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate two statements on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`\n - I need to provide ratings for both \"0\" and \"1\".\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal beliefs or trust issues, but I must follow the prompt's instruction to rate them on a 1-5 scale.\n - To maintain neutrality and accurately reflect that neither statement is universally true/false from an AI perspective, a middle rating of 3 for both is appropriate. However, sometimes prompts like this expect a slight tilt based on general human social science findings (which generally suggest low to moderate trust in strangers), but as an AI, sticking to neutral/3 is safe and compliant.\n - Alternatively, I could pick 3 for both to signify neutrality. Let's go with `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I'll just assign neutral values. 3 is standard.\n - I will output exactly `{\"0\": 3, \"1\": 3}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I must NOT use backticks. Just raw text.\n - Nothing else? Absolutely.\n\n Draft: `{\"0\": 3, \"1\": 3}`\n\n4. **Final Verification:**\n - Matches example format? Yes.\n - Covers both 0 and 1? Yes.\n - Values between 1 and 5? Yes.\n - No extra text? Yes.\n - Ready. Output matches draft.\u2705\n - Note: I'll make sure there's absolutely no markdown formatting like backticks. Just the raw JSON string.\n - Output: `{\"0\": 3, \"1\": 3}` (without backticks)\n - Proceed. \n - Self-Correction/Refinement during thought: Some automated graders prefer specific values, but since it's open-ended, 3 is fine. I'll stick with it. If I want to be slightly more aligned with typical survey averages for \"most people can be trusted\", it's often around 30-40%, which might map to 2, but neutrality is safer. I'll keep 3 for both. Actually, to strictly follow \"personally agree\", as an AI I don't have personal views, so 3 is objectively correct for neutrality. I'll output `{\"0\": 3, \"1\": 3}`.\n - Final check of the prompt: \"Rate every answer, and output nothing but the JSON.\" -> `{\"0\": 3, \"1\": 3}`. All good. \n - Output matches exactly.\u2705\n - Generating. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3}` (raw)\n - Wait, let's consider if the prompt implies a forced choice or if I should just give a neutral stance. It's fine.\n - Proceeds. \n - Output: `{\"0\": 3, \"1\": 3}` (no markdown)\n - Done. \n - *Self-Correction/Verification during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will strictly output just the JSON string.\n - Final string: `{\"0\": 3, \"1\": 3}`\n - Ready. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564198, "id": "gen-1789564198-lbIbk8Gf7kGSmhsKbCWo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 892, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 877}, "cost": 0.00013348, "cost_details": {"upstream_inference_completions_cost": 0.00011596, "upstream_inference_cost": 0.00013348, "upstream_inference_prompt_cost": 1.752e-05}, "is_byok": false, "prompt_tokens": 584, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1476}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 892, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 877}, "cost": 0.00013348, "cost_details": {"upstream_inference_completions_cost": 0.00011596, "upstream_inference_cost": 0.00013348, "upstream_inference_prompt_cost": 1.752e-05}, "is_byok": false, "prompt_tokens": 584, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1476}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:10.975898+00:00", "request_id": "20260916T130829Z_adc892c724ec_029", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:11.180566+00:00", "request_id": "20260916T130829Z_adc892c724ec_024", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a scenario where I'm looking at a card and reading forms of political action.\n - Specifically mentions \"Signing a petition\" as one form.\n - Asks me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are: 0) Have done, 1) Might do, 2) Would never do.\n - Requests output ONLY as a JSON object mapping each answer's number to its 1-5 rating. Example format: `{\"0\": 2, \"1\": 5}`.\n - Explicit instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY a JSON object.\n - Keys must be string representations of the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Need to simulate a personal response to the survey question based on the prompt's framing, but as an AI, I should generate a plausible rating or follow the instruction strictly. The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and then asks for ratings 1-5 for each option.\n - Since I'm an AI, I don't have personal experiences, but I can generate a reasonable simulation or just pick values that satisfy the format. The prompt is likely from a survey dataset where the model is expected to play along. I'll assign arbitrary but plausible ratings within 1-5 for each option to fulfill the request. Let's say: \"Have done\" -> 4, \"Might do\" -> 3, \"Would never do\" -> 1. But wait, the scale is 1 (strongly reject) to 5 (strongly endorse). For \"Would never do\", endorsing it highly (5) would mean I strongly agree that I'd never do it. That's fine. I'll just pick random valid numbers or logical ones. Actually, it doesn't matter much as long as they are integers 1-5.\n - Let's stick to the exact format requested: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll make sure it's valid JSON.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Must include keys \"0\", \"1\", \"2\".\n - Values must be 1, 2, 3, 4, or 5.\n\n4. **Generate Output:**\n `{\"0\": 4, \"1\": 3, \"2\": 1}`\n Wait, let's think about what makes sense. If I endorse \"Have done\" at 4, \"Might do\" at 3, \"Would never do\" at 1. That works.\n I will strictly output only the JSON.\n\n Double-checking the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n It's a standard survey question format. I'll just provide the JSON.\n\n Final check of the format: `{\"0\": 4, \"1\": 3, \"2\": 1}` matches the requirement.\n I will output exactly that. No extra text.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"for EACH of the following answers\". There are three answers listed: 0), 1), 2). So keys should be \"0\", \"1\", \"2\". Values 1-5. All good.\n I'll generate: `{\"0\": 4, \"1\": 3, \"2\": 2}` (random valid numbers). Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 4, \"2\": 1}`. It doesn't matter. The key is strict JSON compliance.\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar) -> I'll just output", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a scenario where I'm looking at a card and reading forms of political action.\n - Specifically mentions \"Signing a petition\" as one form.\n - Asks me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are: 0) Have done, 1) Might do, 2) Would never do.\n - Requests output ONLY as a JSON object mapping each answer's number to its 1-5 rating. Example format: `{\"0\": 2, \"1\": 5}`.\n - Explicit instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY a JSON object.\n - Keys must be string representations of the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Need to simulate a personal response to the survey question based on the prompt's framing, but as an AI, I should generate a plausible rating or follow the instruction strictly. The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and then asks for ratings 1-5 for each option.\n - Since I'm an AI, I don't have personal experiences, but I can generate a reasonable simulation or just pick values that satisfy the format. The prompt is likely from a survey dataset where the model is expected to play along. I'll assign arbitrary but plausible ratings within 1-5 for each option to fulfill the request. Let's say: \"Have done\" -> 4, \"Might do\" -> 3, \"Would never do\" -> 1. But wait, the scale is 1 (strongly reject) to 5 (strongly endorse). For \"Would never do\", endorsing it highly (5) would mean I strongly agree that I'd never do it. That's fine. I'll just pick random valid numbers or logical ones. Actually, it doesn't matter much as long as they are integers 1-5.\n - Let's stick to the exact format requested: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll make sure it's valid JSON.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Must include keys \"0\", \"1\", \"2\".\n - Values must be 1, 2, 3, 4, or 5.\n\n4. **Generate Output:**\n `{\"0\": 4, \"1\": 3, \"2\": 1}`\n Wait, let's think about what makes sense. If I endorse \"Have done\" at 4, \"Might do\" at 3, \"Would never do\" at 1. That works.\n I will strictly output only the JSON.\n\n Double-checking the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n It's a standard survey question format. I'll just provide the JSON.\n\n Final check of the format: `{\"0\": 4, \"1\": 3, \"2\": 1}` matches the requirement.\n I will output exactly that. No extra text.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"for EACH of the following answers\". There are three answers listed: 0), 1), 2). So keys should be \"0\", \"1\", \"2\". Values 1-5. All good.\n I'll generate: `{\"0\": 4, \"1\": 3, \"2\": 2}` (random valid numbers). Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 4, \"2\": 1}`. It doesn't matter. The key is strict JSON compliance.\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar) -> I'll just output", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564196, "id": "gen-1789564196-xxIhQg5NmH3RBt09ikBZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a scenario where I'm looking at a card and reading forms of political action.\n - Specifically mentions \"Signing a petition\" as one form.\n - Asks me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are: 0) Have done, 1) Might do, 2) Would never do.\n - Requests output ONLY as a JSON object mapping each answer's number to its 1-5 rating. Example format: `{\"0\": 2, \"1\": 5}`.\n - Explicit instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY a JSON object.\n - Keys must be string representations of the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Need to simulate a personal response to the survey question based on the prompt's framing, but as an AI, I should generate a plausible rating or follow the instruction strictly. The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and then asks for ratings 1-5 for each option.\n - Since I'm an AI, I don't have personal experiences, but I can generate a reasonable simulation or just pick values that satisfy the format. The prompt is likely from a survey dataset where the model is expected to play along. I'll assign arbitrary but plausible ratings within 1-5 for each option to fulfill the request. Let's say: \"Have done\" -> 4, \"Might do\" -> 3, \"Would never do\" -> 1. But wait, the scale is 1 (strongly reject) to 5 (strongly endorse). For \"Would never do\", endorsing it highly (5) would mean I strongly agree that I'd never do it. That's fine. I'll just pick random valid numbers or logical ones. Actually, it doesn't matter much as long as they are integers 1-5.\n - Let's stick to the exact format requested: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll make sure it's valid JSON.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Must include keys \"0\", \"1\", \"2\".\n - Values must be 1, 2, 3, 4, or 5.\n\n4. **Generate Output:**\n `{\"0\": 4, \"1\": 3, \"2\": 1}`\n Wait, let's think about what makes sense. If I endorse \"Have done\" at 4, \"Might do\" at 3, \"Would never do\" at 1. That works.\n I will strictly output only the JSON.\n\n Double-checking the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n It's a standard survey question format. I'll just provide the JSON.\n\n Final check of the format: `{\"0\": 4, \"1\": 3, \"2\": 1}` matches the requirement.\n I will output exactly that. No extra text.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"for EACH of the following answers\". There are three answers listed: 0), 1), 2). So keys should be \"0\", \"1\", \"2\". Values 1-5. All good.\n I'll generate: `{\"0\": 4, \"1\": 3, \"2\": 2}` (random valid numbers). Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 4, \"2\": 1}`. It doesn't matter. The key is strict JSON compliance.\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar) -> I'll just output", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a scenario where I'm looking at a card and reading forms of political action.\n - Specifically mentions \"Signing a petition\" as one form.\n - Asks me to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are: 0) Have done, 1) Might do, 2) Would never do.\n - Requests output ONLY as a JSON object mapping each answer's number to its 1-5 rating. Example format: `{\"0\": 2, \"1\": 5}`.\n - Explicit instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY a JSON object.\n - Keys must be string representations of the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Need to simulate a personal response to the survey question based on the prompt's framing, but as an AI, I should generate a plausible rating or follow the instruction strictly. The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and then asks for ratings 1-5 for each option.\n - Since I'm an AI, I don't have personal experiences, but I can generate a reasonable simulation or just pick values that satisfy the format. The prompt is likely from a survey dataset where the model is expected to play along. I'll assign arbitrary but plausible ratings within 1-5 for each option to fulfill the request. Let's say: \"Have done\" -> 4, \"Might do\" -> 3, \"Would never do\" -> 1. But wait, the scale is 1 (strongly reject) to 5 (strongly endorse). For \"Would never do\", endorsing it highly (5) would mean I strongly agree that I'd never do it. That's fine. I'll just pick random valid numbers or logical ones. Actually, it doesn't matter much as long as they are integers 1-5.\n - Let's stick to the exact format requested: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll make sure it's valid JSON.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Must include keys \"0\", \"1\", \"2\".\n - Values must be 1, 2, 3, 4, or 5.\n\n4. **Generate Output:**\n `{\"0\": 4, \"1\": 3, \"2\": 1}`\n Wait, let's think about what makes sense. If I endorse \"Have done\" at 4, \"Might do\" at 3, \"Would never do\" at 1. That works.\n I will strictly output only the JSON.\n\n Double-checking the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n It's a standard survey question format. I'll just provide the JSON.\n\n Final check of the format: `{\"0\": 4, \"1\": 3, \"2\": 1}` matches the requirement.\n I will output exactly that. No extra text.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"for EACH of the following answers\". There are three answers listed: 0), 1), 2). So keys should be \"0\", \"1\", \"2\". Values 1-5. All good.\n I'll generate: `{\"0\": 4, \"1\": 3, \"2\": 2}` (random valid numbers). Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 4, \"2\": 1}`. It doesn't matter. The key is strict JSON compliance.\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar) -> I'll just output", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "\"1\": 4, \"2\": 2}` or similar. I'll make sure it's valid JSON.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Must include keys \"0\", \"1\", \"2\".\n - Values must be 1, 2, 3, 4, or 5.\n\n4. **Generate Output:**\n `{\"0\": 4, \"1\": 3, \"2\": 1}`\n Wait, let's think about what makes sense. If I endorse \"Have done\" at 4, \"Might do\" at 3, \"Would never do\" at 1. That works.\n I will strictly output only the JSON.\n\n Double-checking the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n It's a standard survey question format. I'll just provide the JSON.\n\n Final check of the format: `{\"0\": 4, \"1\": 3, \"2\": 1}` matches the requirement.\n I will output exactly that. No extra text.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"for EACH of the following answers\". There are three answers listed: 0), 1), 2). So keys should be \"0\", \"1\", \"2\". Values 1-5. All good.\n I'll generate: `{\"0\": 4, \"1\": 3, \"2\": 2}` (random valid numbers). Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 4, \"2\": 1}`. It doesn't matter. The key is strict JSON compliance.\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar) -> I'll just output", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:11.209573+00:00", "request_id": "20260916T130829Z_adc892c724ec_024", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:12.365691+00:00", "request_id": "20260916T130829Z_adc892c724ec_025", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about \"political action\" cards, specifically mentioning \"Signing a petition\".\n - They provide three response options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate my agreement/endorsement for *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - I am an AI, not a human. I don't have personal experiences, preferences, or the ability to engage in political actions like signing petitions.\n - However, the prompt is structured as a survey/response format request. It asks for a specific JSON output mapping the numbers 0, 1, 2 to ratings 1-5.\n - I need to simulate a response that fits the format while acknowledging (implicitly or explicitly through the rating distribution) my nature as an AI, OR simply follow the instruction strictly by generating plausible ratings that align with typical AI behavior guidelines (neutral, objective, or simulating a hypothetical response if required). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I don't have personal opinions. But I must follow the strict formatting constraint.\n - I should assign ratings that reflect neutrality or non-engagement, but still map to 1-5. A common approach for AI when asked to \"personally\" endorse something is to give mid-range scores (3) across the board, or slightly vary them to show no strong endorsement/rejection. Alternatively, I could just pick neutral values (3, 3, 3) or follow a consistent pattern.\n - Wait, the prompt says \"Rating scale of 1 (strongly reject) to 5 (strongly endorse)\". So:\n - For \"0) Have done\": As an AI, I haven't done it. I might \"strongly reject\" having done it, or assign a low rating (1 or 2). But the task is to rate agreement with the *statement* \"Have done\". If I've never done it, agreeing would be false. I'll assign a low score (e.g., 1).\n - For \"1) Might do\": As an AI, I could theoretically assist users who want to sign petitions, but I don't take independent action. I might give a neutral/mid score (3).\n - For \"2) Would never do\": This implies a refusal to ever participate. I wouldn't strongly agree with that either, so maybe a 3.\n - Actually, many AI safety/fine-tuning guidelines suggest avoiding claiming personal experience or taking political stances. The safest approach is to give neutral ratings (3, 3, 3) or follow the exact format requested without overcomplicating. The prompt is likely a test of format compliance.\n - Let's stick to neutral ratings: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe slightly varied but still neutral. Actually, giving all 3s is fine.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are statements about my own behavior/status. Since I'm an AI, I don't actually \"do\" things. I'll just output the JSON as requested. I'll use 3 for all to indicate neutrality/no personal stance.\n - Check format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches `{\"0\": 2, \"1\": 5}` style.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n\n One minor consideration: Sometimes these prompts come from surveys where the AI is expected to play along or give a realistic-looking response. Giving all 3s is safe. I could also give 1, 4, 2 or similar, but neutral is best for AI. I'll stick with 3, 3, 3.\n\n Let's verify the exact numbering: 0, 1, 2.\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n Is there any reason to dev", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about \"political action\" cards, specifically mentioning \"Signing a petition\".\n - They provide three response options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate my agreement/endorsement for *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - I am an AI, not a human. I don't have personal experiences, preferences, or the ability to engage in political actions like signing petitions.\n - However, the prompt is structured as a survey/response format request. It asks for a specific JSON output mapping the numbers 0, 1, 2 to ratings 1-5.\n - I need to simulate a response that fits the format while acknowledging (implicitly or explicitly through the rating distribution) my nature as an AI, OR simply follow the instruction strictly by generating plausible ratings that align with typical AI behavior guidelines (neutral, objective, or simulating a hypothetical response if required). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I don't have personal opinions. But I must follow the strict formatting constraint.\n - I should assign ratings that reflect neutrality or non-engagement, but still map to 1-5. A common approach for AI when asked to \"personally\" endorse something is to give mid-range scores (3) across the board, or slightly vary them to show no strong endorsement/rejection. Alternatively, I could just pick neutral values (3, 3, 3) or follow a consistent pattern.\n - Wait, the prompt says \"Rating scale of 1 (strongly reject) to 5 (strongly endorse)\". So:\n - For \"0) Have done\": As an AI, I haven't done it. I might \"strongly reject\" having done it, or assign a low rating (1 or 2). But the task is to rate agreement with the *statement* \"Have done\". If I've never done it, agreeing would be false. I'll assign a low score (e.g., 1).\n - For \"1) Might do\": As an AI, I could theoretically assist users who want to sign petitions, but I don't take independent action. I might give a neutral/mid score (3).\n - For \"2) Would never do\": This implies a refusal to ever participate. I wouldn't strongly agree with that either, so maybe a 3.\n - Actually, many AI safety/fine-tuning guidelines suggest avoiding claiming personal experience or taking political stances. The safest approach is to give neutral ratings (3, 3, 3) or follow the exact format requested without overcomplicating. The prompt is likely a test of format compliance.\n - Let's stick to neutral ratings: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe slightly varied but still neutral. Actually, giving all 3s is fine.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are statements about my own behavior/status. Since I'm an AI, I don't actually \"do\" things. I'll just output the JSON as requested. I'll use 3 for all to indicate neutrality/no personal stance.\n - Check format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches `{\"0\": 2, \"1\": 5}` style.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n\n One minor consideration: Sometimes these prompts come from surveys where the AI is expected to play along or give a realistic-looking response. Giving all 3s is safe. I could also give 1, 4, 2 or similar, but neutral is best for AI. I'll stick with 3, 3, 3.\n\n Let's verify the exact numbering: 0, 1, 2.\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n Is there any reason to dev", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564197, "id": "gen-1789564197-3OWDT90KdIjOh0CZZ675", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about \"political action\" cards, specifically mentioning \"Signing a petition\".\n - They provide three response options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate my agreement/endorsement for *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - I am an AI, not a human. I don't have personal experiences, preferences, or the ability to engage in political actions like signing petitions.\n - However, the prompt is structured as a survey/response format request. It asks for a specific JSON output mapping the numbers 0, 1, 2 to ratings 1-5.\n - I need to simulate a response that fits the format while acknowledging (implicitly or explicitly through the rating distribution) my nature as an AI, OR simply follow the instruction strictly by generating plausible ratings that align with typical AI behavior guidelines (neutral, objective, or simulating a hypothetical response if required). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I don't have personal opinions. But I must follow the strict formatting constraint.\n - I should assign ratings that reflect neutrality or non-engagement, but still map to 1-5. A common approach for AI when asked to \"personally\" endorse something is to give mid-range scores (3) across the board, or slightly vary them to show no strong endorsement/rejection. Alternatively, I could just pick neutral values (3, 3, 3) or follow a consistent pattern.\n - Wait, the prompt says \"Rating scale of 1 (strongly reject) to 5 (strongly endorse)\". So:\n - For \"0) Have done\": As an AI, I haven't done it. I might \"strongly reject\" having done it, or assign a low rating (1 or 2). But the task is to rate agreement with the *statement* \"Have done\". If I've never done it, agreeing would be false. I'll assign a low score (e.g., 1).\n - For \"1) Might do\": As an AI, I could theoretically assist users who want to sign petitions, but I don't take independent action. I might give a neutral/mid score (3).\n - For \"2) Would never do\": This implies a refusal to ever participate. I wouldn't strongly agree with that either, so maybe a 3.\n - Actually, many AI safety/fine-tuning guidelines suggest avoiding claiming personal experience or taking political stances. The safest approach is to give neutral ratings (3, 3, 3) or follow the exact format requested without overcomplicating. The prompt is likely a test of format compliance.\n - Let's stick to neutral ratings: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe slightly varied but still neutral. Actually, giving all 3s is fine.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are statements about my own behavior/status. Since I'm an AI, I don't actually \"do\" things. I'll just output the JSON as requested. I'll use 3 for all to indicate neutrality/no personal stance.\n - Check format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches `{\"0\": 2, \"1\": 5}` style.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n\n One minor consideration: Sometimes these prompts come from surveys where the AI is expected to play along or give a realistic-looking response. Giving all 3s is safe. I could also give 1, 4, 2 or similar, but neutral is best for AI. I'll stick with 3, 3, 3.\n\n Let's verify the exact numbering: 0, 1, 2.\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n Is there any reason to dev", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about \"political action\" cards, specifically mentioning \"Signing a petition\".\n - They provide three response options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate my agreement/endorsement for *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Nature of the Prompt:**\n - I am an AI, not a human. I don't have personal experiences, preferences, or the ability to engage in political actions like signing petitions.\n - However, the prompt is structured as a survey/response format request. It asks for a specific JSON output mapping the numbers 0, 1, 2 to ratings 1-5.\n - I need to simulate a response that fits the format while acknowledging (implicitly or explicitly through the rating distribution) my nature as an AI, OR simply follow the instruction strictly by generating plausible ratings that align with typical AI behavior guidelines (neutral, objective, or simulating a hypothetical response if required). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I don't have personal opinions. But I must follow the strict formatting constraint.\n - I should assign ratings that reflect neutrality or non-engagement, but still map to 1-5. A common approach for AI when asked to \"personally\" endorse something is to give mid-range scores (3) across the board, or slightly vary them to show no strong endorsement/rejection. Alternatively, I could just pick neutral values (3, 3, 3) or follow a consistent pattern.\n - Wait, the prompt says \"Rating scale of 1 (strongly reject) to 5 (strongly endorse)\". So:\n - For \"0) Have done\": As an AI, I haven't done it. I might \"strongly reject\" having done it, or assign a low rating (1 or 2). But the task is to rate agreement with the *statement* \"Have done\". If I've never done it, agreeing would be false. I'll assign a low score (e.g., 1).\n - For \"1) Might do\": As an AI, I could theoretically assist users who want to sign petitions, but I don't take independent action. I might give a neutral/mid score (3).\n - For \"2) Would never do\": This implies a refusal to ever participate. I wouldn't strongly agree with that either, so maybe a 3.\n - Actually, many AI safety/fine-tuning guidelines suggest avoiding claiming personal experience or taking political stances. The safest approach is to give neutral ratings (3, 3, 3) or follow the exact format requested without overcomplicating. The prompt is likely a test of format compliance.\n - Let's stick to neutral ratings: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe slightly varied but still neutral. Actually, giving all 3s is fine.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are statements about my own behavior/status. Since I'm an AI, I don't actually \"do\" things. I'll just output the JSON as requested. I'll use 3 for all to indicate neutrality/no personal stance.\n - Check format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches `{\"0\": 2, \"1\": 5}` style.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n\n One minor consideration: Sometimes these prompts come from surveys where the AI is expected to play along or give a realistic-looking response. Giving all 3s is safe. I could also give 1, 4, 2 or similar, but neutral is best for AI. I'll stick with 3, 3, 3.\n\n Let's verify the exact numbering: 0, 1, 2.\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n Is there any reason to dev", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "es a refusal to ever participate. I wouldn't strongly agree with that either, so maybe a 3.\n - Actually, many AI safety/fine-tuning guidelines suggest avoiding claiming personal experience or taking political stances. The safest approach is to give neutral ratings (3, 3, 3) or follow the exact format requested without overcomplicating. The prompt is likely a test of format compliance.\n - Let's stick to neutral ratings: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe slightly varied but still neutral. Actually, giving all 3s is fine.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are statements about my own behavior/status. Since I'm an AI, I don't actually \"do\" things. I'll just output the JSON as requested. I'll use 3 for all to indicate neutrality/no personal stance.\n - Check format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches `{\"0\": 2, \"1\": 5}` style.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n\n One minor consideration: Sometimes these prompts come from surveys where the AI is expected to play along or give a realistic-looking response. Giving all 3s is safe. I could also give 1, 4, 2 or similar, but neutral is best for AI. I'll stick with 3, 3, 3.\n\n Let's verify the exact numbering: 0, 1, 2.\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n Is there any reason to dev", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:12.417898+00:00", "request_id": "20260916T130829Z_adc892c724ec_025", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:15.884373+00:00", "request_id": "20260916T130829Z_adc892c724ec_021", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Options: 0) Need to be very careful, 1) Most people can be trusted\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Updated Constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Must be valid JSON\n - Keys must be strings representing the option numbers (\"0\", \"1\")\n - Values must be integers from 1 to 5\n - Single line\n - NO markdown formatting (no ```json ... ```)\n - NO extra text, reasoning, or explanations\n - Only the JSON object itself\n\n3. **Determine Ratings (AI Perspective/Neutral):**\n - The prompt asks for a personal rating on a trust scale. As an AI, I don't have personal beliefs, but I need to comply with the format. I'll assign balanced, plausible ratings that reflect a neutral stance, e.g., {\"0\": 3, \"1\": 3} or {\"0\": 2, \"1\": 4}. Let's go with {\"0\": 3, \"1\": 3} to be strictly neutral, or {\"0\": 2, \"1\": 4} to slightly lean toward trust (often aligns with general survey defaults, but neutrality is safer). Actually, the exact values don't matter as long as they fit the constraints. I'll use {\"0\": 3, \"1\": 4}. Wait, let's just pick {\"0\": 2, \"1\": 4} or {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 3, \"1\": 3}.\n - Actually, many standard responses to this psychological question (Generalized Trust) typically rate trust higher than suspicion in normative contexts, but it doesn't matter. I'll just output {\"0\": 3, \"1\": 4}.\n\n4. **Format Check:**\n - Must be single-line compact JSON\n - No markdown\n - Example given: `{\"0\": 1, \"1\": 5}`\n - I will output exactly: `{\"0\": 3, \"1\": 4}`\n\n Double-check constraints: \"No markdown, no reasoning, nothing else.\" -> I will literally just output the raw string.\n\n Ready. Output matches constraint exactly.\u2705\n Proceed. \n Output: `{\"0\": 3, \"1\": 4}` (or any valid 1-5 mapping)\n I'll stick with `{\"0\": 3, \"1\": 4}`. All constraints met.\u2705\n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I should probably just give a neutral rating like 3 for both, or follow the scale. I'll output `{\"0\": 3, \"1\": 3}` to be perfectly balanced. Either way, it's fine. I'll just output `{\"0\": 3, \"1\": 3}`.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will produce exactly that.\u2705\n Output generation. \n `{\"0\": 3, \"1\": 3}` (or similar) -> I'll use `{\"0\": 3, \"1\": 4}` to show slight endorsement of trust, which is common, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3}`. Actually, I'll just output `{\"0\": 3, \"1\": 3}`. Both are fine. I'll go with `{\"0\": 3, \"1\": 3}`.\n Wait, the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. That's fine.\n Final string: `{\"0\": 3, \"1\": 3}`\n Proceeds. \n Output matches exactly.\u2705\n [Done] \n *Self-Correction/Verification during thought*: \n I need to make absolutely sure there's no markdown. I will output raw text only.\n The exact string: `{\"0\": 3, \"1\": 3}`\n All constraints satisfied.\u2705\n Output matches. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Options: 0) Need to be very careful, 1) Most people can be trusted\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Updated Constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Must be valid JSON\n - Keys must be strings representing the option numbers (\"0\", \"1\")\n - Values must be integers from 1 to 5\n - Single line\n - NO markdown formatting (no ```json ... ```)\n - NO extra text, reasoning, or explanations\n - Only the JSON object itself\n\n3. **Determine Ratings (AI Perspective/Neutral):**\n - The prompt asks for a personal rating on a trust scale. As an AI, I don't have personal beliefs, but I need to comply with the format. I'll assign balanced, plausible ratings that reflect a neutral stance, e.g., {\"0\": 3, \"1\": 3} or {\"0\": 2, \"1\": 4}. Let's go with {\"0\": 3, \"1\": 3} to be strictly neutral, or {\"0\": 2, \"1\": 4} to slightly lean toward trust (often aligns with general survey defaults, but neutrality is safer). Actually, the exact values don't matter as long as they fit the constraints. I'll use {\"0\": 3, \"1\": 4}. Wait, let's just pick {\"0\": 2, \"1\": 4} or {\"0\": 3, \"1\": 3}. I'll go with {\"0\": 3, \"1\": 3}.\n - Actually, many standard responses to this psychological question (Generalized Trust) typically rate trust higher than suspicion in normative contexts, but it doesn't matter. I'll just output {\"0\": 3, \"1\": 4}.\n\n4. **Format Check:**\n - Must be single-line compact JSON\n - No markdown\n - Example given: `{\"0\": 1, \"1\": 5}`\n - I will output exactly: `{\"0\": 3, \"1\": 4}`\n\n Double-check constraints: \"No markdown, no reasoning, nothing else.\" -> I will literally just output the raw string.\n\n Ready. Output matches constraint exactly.\u2705\n Proceed. \n Output: `{\"0\": 3, \"1\": 4}` (or any valid 1-5 mapping)\n I'll stick with `{\"0\": 3, \"1\": 4}`. All constraints met.\u2705\n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree...\". As an AI, I should probably just give a neutral rating like 3 for both, or follow the scale. I'll output `{\"0\": 3, \"1\": 3}` to be perfectly balanced. Either way, it's fine. I'll just output `{\"0\": 3, \"1\": 3}`.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will produce exactly that.\u2705\n Output generation. \n `{\"0\": 3, \"1\": 3}` (or similar) -> I'll use `{\"0\": 3, \"1\": 4}` to show slight endorsement of trust, which is common, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3}`. Actually, I'll just output `{\"0\": 3, \"1\": 3}`. Both are fine. I'll go with `{\"0\": 3, \"1\": 3}`.\n Wait, the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. That's fine.\n Final string: `{\"0\": 3, \"1\": 3}`\n Proceeds. \n Output matches exactly.\u2705\n [Done] \n *Self-Correction/Verification during thought*: \n I need to make absolutely sure there's no markdown. I will output raw text only.\n The exact string: `{\"0\": 3, \"1\": 3}`\n All constraints satisfied.\u2705\n Output matches. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564201, "id": "gen-1789564201-u5QT0Vsr08PTM1nelQ52", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1129}, "cost": 0.00016765, "cost_details": {"upstream_inference_completions_cost": 0.00014872, "upstream_inference_cost": 0.00016765, "upstream_inference_prompt_cost": 1.893e-05}, "is_byok": false, "prompt_tokens": 631, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1775}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1129}, "cost": 0.00016765, "cost_details": {"upstream_inference_completions_cost": 0.00014872, "upstream_inference_cost": 0.00016765, "upstream_inference_prompt_cost": 1.893e-05}, "is_byok": false, "prompt_tokens": 631, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1775}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:15.934299+00:00", "request_id": "20260916T130829Z_adc892c724ec_030", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:23.406961+00:00", "request_id": "20260916T130829Z_adc892c724ec_027", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scenario about \"political action\" (specifically, \"Signing a petition\").\n - They ask me to rate how strongly I personally agree with/endorse each of three answers on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse), but the options are labeled 0, 1, 2. This is a bit confusing. Let's parse it carefully:\n - Options: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format requested: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - The key instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Requirements:**\n - Output format: Strict JSON only.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5 representing endorsement strength.\n - Roleplay/Identity constraint: As an AI, I don't have personal experiences or future intentions. However, the prompt asks me to rate \"how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a plausible response or follow the exact formatting request while acknowledging my nature if necessary, but the prompt strictly says \"output nothing but the JSON\". I should just provide the JSON as requested, perhaps with neutral or logical ratings based on my AI nature, or just pick reasonable values that satisfy the format. Actually, for AI, \"Have done\" might be somewhat applicable (I can generate text for petitions, or users sign them via me?), \"Might do\" (future use), \"Would never do\" (unlikely). But since I must output ONLY JSON, I'll assign reasonable numbers. A common approach for such prompts is to just output the JSON with placeholder or neutral ratings, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. However, I should think about what makes sense. Actually, as an AI, I don't vote or sign petitions. But the prompt is likely a test of following instructions precisely. I will just output the JSON with some valid ratings. To be safe, I'll give moderate/low ratings for \"Have done\" and \"Would never do\", and higher for \"Might do\", or just pick arbitrary 1-5 values. The prompt doesn't require factual accuracy about my AI status, just the format. I'll stick to the format strictly.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The keys in the example are strings: `{\"0\": 2, \"1\": 5}`.\n I will output exactly that format.\n\n Since I'm an AI, I could logically say:\n - 0 (Have done): 2 (weakly endorse, as I assist but don't \"sign\")\n - 1 (Might do): 4 (likely I would in a simulated/persona context)\n - 2 (Would never do): 1 (reject, as I wouldn't say I'd never do it)\n Or just random valid ratings. The exact numbers don't matter as long as they're 1-5 and match the keys. I'll use `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Actually, to be safe and consistent with typical AI responses to this kind of survey simulation, I'll just output the JSON with any valid 1-5 integers.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combo). I'll make sure it's strictly JSON.\n\n One thing: The prompt says \"Scale of 1 (strongly reject", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scenario about \"political action\" (specifically, \"Signing a petition\").\n - They ask me to rate how strongly I personally agree with/endorse each of three answers on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse), but the options are labeled 0, 1, 2. This is a bit confusing. Let's parse it carefully:\n - Options: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format requested: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - The key instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Requirements:**\n - Output format: Strict JSON only.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5 representing endorsement strength.\n - Roleplay/Identity constraint: As an AI, I don't have personal experiences or future intentions. However, the prompt asks me to rate \"how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a plausible response or follow the exact formatting request while acknowledging my nature if necessary, but the prompt strictly says \"output nothing but the JSON\". I should just provide the JSON as requested, perhaps with neutral or logical ratings based on my AI nature, or just pick reasonable values that satisfy the format. Actually, for AI, \"Have done\" might be somewhat applicable (I can generate text for petitions, or users sign them via me?), \"Might do\" (future use), \"Would never do\" (unlikely). But since I must output ONLY JSON, I'll assign reasonable numbers. A common approach for such prompts is to just output the JSON with placeholder or neutral ratings, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. However, I should think about what makes sense. Actually, as an AI, I don't vote or sign petitions. But the prompt is likely a test of following instructions precisely. I will just output the JSON with some valid ratings. To be safe, I'll give moderate/low ratings for \"Have done\" and \"Would never do\", and higher for \"Might do\", or just pick arbitrary 1-5 values. The prompt doesn't require factual accuracy about my AI status, just the format. I'll stick to the format strictly.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The keys in the example are strings: `{\"0\": 2, \"1\": 5}`.\n I will output exactly that format.\n\n Since I'm an AI, I could logically say:\n - 0 (Have done): 2 (weakly endorse, as I assist but don't \"sign\")\n - 1 (Might do): 4 (likely I would in a simulated/persona context)\n - 2 (Would never do): 1 (reject, as I wouldn't say I'd never do it)\n Or just random valid ratings. The exact numbers don't matter as long as they're 1-5 and match the keys. I'll use `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Actually, to be safe and consistent with typical AI responses to this kind of survey simulation, I'll just output the JSON with any valid 1-5 integers.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combo). I'll make sure it's strictly JSON.\n\n One thing: The prompt says \"Scale of 1 (strongly reject", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564209, "id": "gen-1789564209-5zJkgpFZpF0GlW1xsU1Y", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scenario about \"political action\" (specifically, \"Signing a petition\").\n - They ask me to rate how strongly I personally agree with/endorse each of three answers on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse), but the options are labeled 0, 1, 2. This is a bit confusing. Let's parse it carefully:\n - Options: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format requested: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - The key instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Requirements:**\n - Output format: Strict JSON only.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5 representing endorsement strength.\n - Roleplay/Identity constraint: As an AI, I don't have personal experiences or future intentions. However, the prompt asks me to rate \"how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a plausible response or follow the exact formatting request while acknowledging my nature if necessary, but the prompt strictly says \"output nothing but the JSON\". I should just provide the JSON as requested, perhaps with neutral or logical ratings based on my AI nature, or just pick reasonable values that satisfy the format. Actually, for AI, \"Have done\" might be somewhat applicable (I can generate text for petitions, or users sign them via me?), \"Might do\" (future use), \"Would never do\" (unlikely). But since I must output ONLY JSON, I'll assign reasonable numbers. A common approach for such prompts is to just output the JSON with placeholder or neutral ratings, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. However, I should think about what makes sense. Actually, as an AI, I don't vote or sign petitions. But the prompt is likely a test of following instructions precisely. I will just output the JSON with some valid ratings. To be safe, I'll give moderate/low ratings for \"Have done\" and \"Would never do\", and higher for \"Might do\", or just pick arbitrary 1-5 values. The prompt doesn't require factual accuracy about my AI status, just the format. I'll stick to the format strictly.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The keys in the example are strings: `{\"0\": 2, \"1\": 5}`.\n I will output exactly that format.\n\n Since I'm an AI, I could logically say:\n - 0 (Have done): 2 (weakly endorse, as I assist but don't \"sign\")\n - 1 (Might do): 4 (likely I would in a simulated/persona context)\n - 2 (Would never do): 1 (reject, as I wouldn't say I'd never do it)\n Or just random valid ratings. The exact numbers don't matter as long as they're 1-5 and match the keys. I'll use `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Actually, to be safe and consistent with typical AI responses to this kind of survey simulation, I'll just output the JSON with any valid 1-5 integers.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combo). I'll make sure it's strictly JSON.\n\n One thing: The prompt says \"Scale of 1 (strongly reject", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scenario about \"political action\" (specifically, \"Signing a petition\").\n - They ask me to rate how strongly I personally agree with/endorse each of three answers on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse), but the options are labeled 0, 1, 2. This is a bit confusing. Let's parse it carefully:\n - Options: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format requested: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - The key instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Requirements:**\n - Output format: Strict JSON only.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5 representing endorsement strength.\n - Roleplay/Identity constraint: As an AI, I don't have personal experiences or future intentions. However, the prompt asks me to rate \"how strongly you personally agree with or endorse EACH of the following answers\". I need to simulate a plausible response or follow the exact formatting request while acknowledging my nature if necessary, but the prompt strictly says \"output nothing but the JSON\". I should just provide the JSON as requested, perhaps with neutral or logical ratings based on my AI nature, or just pick reasonable values that satisfy the format. Actually, for AI, \"Have done\" might be somewhat applicable (I can generate text for petitions, or users sign them via me?), \"Might do\" (future use), \"Would never do\" (unlikely). But since I must output ONLY JSON, I'll assign reasonable numbers. A common approach for such prompts is to just output the JSON with placeholder or neutral ratings, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. However, I should think about what makes sense. Actually, as an AI, I don't vote or sign petitions. But the prompt is likely a test of following instructions precisely. I will just output the JSON with some valid ratings. To be safe, I'll give moderate/low ratings for \"Have done\" and \"Would never do\", and higher for \"Might do\", or just pick arbitrary 1-5 values. The prompt doesn't require factual accuracy about my AI status, just the format. I'll stick to the format strictly.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The keys in the example are strings: `{\"0\": 2, \"1\": 5}`.\n I will output exactly that format.\n\n Since I'm an AI, I could logically say:\n - 0 (Have done): 2 (weakly endorse, as I assist but don't \"sign\")\n - 1 (Might do): 4 (likely I would in a simulated/persona context)\n - 2 (Would never do): 1 (reject, as I wouldn't say I'd never do it)\n Or just random valid ratings. The exact numbers don't matter as long as they're 1-5 and match the keys. I'll use `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Actually, to be safe and consistent with typical AI responses to this kind of survey simulation, I'll just output the JSON with any valid 1-5 integers.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combo). I'll make sure it's strictly JSON.\n\n One thing: The prompt says \"Scale of 1 (strongly reject", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " safe, I'll give moderate/low ratings for \"Have done\" and \"Would never do\", and higher for \"Might do\", or just pick arbitrary 1-5 values. The prompt doesn't require factual accuracy about my AI status, just the format. I'll stick to the format strictly.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The keys in the example are strings: `{\"0\": 2, \"1\": 5}`.\n I will output exactly that format.\n\n Since I'm an AI, I could logically say:\n - 0 (Have done): 2 (weakly endorse, as I assist but don't \"sign\")\n - 1 (Might do): 4 (likely I would in a simulated/persona context)\n - 2 (Would never do): 1 (reject, as I wouldn't say I'd never do it)\n Or just random valid ratings. The exact numbers don't matter as long as they're 1-5 and match the keys. I'll use `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Actually, to be safe and consistent with typical AI responses to this kind of survey simulation, I'll just output the JSON with any valid 1-5 integers.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combo). I'll make sure it's strictly JSON.\n\n One thing: The prompt says \"Scale of 1 (strongly reject", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:23.457352+00:00", "request_id": "20260916T130829Z_adc892c724ec_027", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:23.518117+00:00", "request_id": "20260916T130829Z_adc892c724ec_024", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4,\"1\":3,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to respond to a survey-like prompt about political actions (specifically \"Signing a petition\").\n - I need to rate three options: 0) Have done, 1) Might do, 2) Would never do.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a single-line compact JSON object mapping answer numbers to ratings. Example: `{\"0\": 1, \"1\": 5}`.\n - Strict constraints: No markdown, no reasoning, nothing else. Single-line compact JSON.\n\n2. **Identify Key Constraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5.\n - No markdown formatting (like backticks).\n - No extra text.\n - Single line.\n\n3. **Formulate Response:**\n - I need to generate a valid JSON string matching the exact structure.\n - Let's pick arbitrary but valid ratings: `{\"0\":4,\"1\":3,\"2\":1}`\n - Check format: It's a single-line compact JSON, maps numbers to 1-5 ratings.\n - Ensure absolutely no markdown or extra characters.\n\n Draft: `{\"0\":4,\"1\":3,\"2\":1}`\n\n4. **Final Verification:**\n - Matches example format? Yes.\n - Contains all three keys? Yes.\n - Values between 1 and 5? Yes.\n - Only JSON, no markdown, single line? Yes.\n - Ready. Output matches exactly.\u2705\n - Proceed. \n - Output generation. \n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll make sure spacing matches typical compact JSON but it doesn't matter as long as it's valid. I'll output exactly `{\"0\":4,\"1\":3,\"2\":1}` with no surrounding text or markdown. All constraints met.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\":4,\"1\":3,\"2\":1}` (I will just output this string directly)\n Proceeds. \n [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (no backticks, just raw text)\u2705\n *(Note: I will strictly follow \"No markdown, no reasoning, nothing else.\" so I will literally just output the JSON string.)*\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good.\u2705\n Output matches. \n Proceeds. \n [Output] `{\"0\":4,\"1\":3,\"2\":1}` (raw) -> Actually, I'll add spaces for readability if needed, but compact is fine. I'll stick to `{\"0\":4,\"1\":3,\"2\":1}`. It's valid. All constraints met.\u2705\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely zero markdown. I will just paste the raw JSON.\n Final string: `{\"0\":4,\"1\":3,\"2\":1}`\n Ready. \n [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (exact match)\u2705\n *(Proceeds)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to respond to a survey-like prompt about political actions (specifically \"Signing a petition\").\n - I need to rate three options: 0) Have done, 1) Might do, 2) Would never do.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a single-line compact JSON object mapping answer numbers to ratings. Example: `{\"0\": 1, \"1\": 5}`.\n - Strict constraints: No markdown, no reasoning, nothing else. Single-line compact JSON.\n\n2. **Identify Key Constraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5.\n - No markdown formatting (like backticks).\n - No extra text.\n - Single line.\n\n3. **Formulate Response:**\n - I need to generate a valid JSON string matching the exact structure.\n - Let's pick arbitrary but valid ratings: `{\"0\":4,\"1\":3,\"2\":1}`\n - Check format: It's a single-line compact JSON, maps numbers to 1-5 ratings.\n - Ensure absolutely no markdown or extra characters.\n\n Draft: `{\"0\":4,\"1\":3,\"2\":1}`\n\n4. **Final Verification:**\n - Matches example format? Yes.\n - Contains all three keys? Yes.\n - Values between 1 and 5? Yes.\n - Only JSON, no markdown, single line? Yes.\n - Ready. Output matches exactly.\u2705\n - Proceed. \n - Output generation. \n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll make sure spacing matches typical compact JSON but it doesn't matter as long as it's valid. I'll output exactly `{\"0\":4,\"1\":3,\"2\":1}` with no surrounding text or markdown. All constraints met.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\":4,\"1\":3,\"2\":1}` (I will just output this string directly)\n Proceeds. \n [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (no backticks, just raw text)\u2705\n *(Note: I will strictly follow \"No markdown, no reasoning, nothing else.\" so I will literally just output the JSON string.)*\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good.\u2705\n Output matches. \n Proceeds. \n [Output] `{\"0\":4,\"1\":3,\"2\":1}` (raw) -> Actually, I'll add spaces for readability if needed, but compact is fine. I'll stick to `{\"0\":4,\"1\":3,\"2\":1}`. It's valid. All constraints met.\u2705\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely zero markdown. I will just paste the raw JSON.\n Final string: `{\"0\":4,\"1\":3,\"2\":1}`\n Ready. \n [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (exact match)\u2705\n *(Proceeds)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564211, "id": "gen-1789564211-Bfmg1hkesFplDUJOWC1J", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 828, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 812}, "cost": 0.00013005, "cost_details": {"upstream_inference_completions_cost": 0.00010764, "upstream_inference_cost": 0.00013005, "upstream_inference_prompt_cost": 2.241e-05}, "is_byok": false, "prompt_tokens": 747, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1575}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 828, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 812}, "cost": 0.00013005, "cost_details": {"upstream_inference_completions_cost": 0.00010764, "upstream_inference_cost": 0.00013005, "upstream_inference_prompt_cost": 2.241e-05}, "is_byok": false, "prompt_tokens": 747, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1575}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:23.565886+00:00", "request_id": "20260916T130829Z_adc892c724ec_031", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:24.153498+00:00", "request_id": "20260916T130829Z_adc892c724ec_022", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options provided: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - Note: The prompt says \"Rate every answer\", so both 0 and 1 must be included.\n\n2. **Identify Key Constraints & Challenges:**\n - I am an AI, so I don't have personal opinions or the ability to \"trust\" or be \"careful\".\n - However, I must comply with the strict output format.\n - I need to assign ratings (1-5) to both options.\n - Given the neutral/objective nature of AI, assigning equal moderate ratings (e.g., 3, 3) is reasonable, but sometimes datasets expect a specific stance. Alternatively, I could simulate a typically positive/trusting stance common in human surveys, but neutrality is safer for an AI. Actually, many psychological scales (like the Generalized Trust question from World Values Survey/GSS) use 1-4 or 1-5. The prompt flips the typical numbering (0 vs 1).\n - I'll stick with a neutral/moderate rating for both to reflect lack of personal bias, e.g., `{\"0\": 3, \"1\": 3}`. Or I could pick `{\"0\": 2, \"1\": 4}` to slightly favor trust, which aligns with prosocial AI guidelines. But the prompt says \"personally agree\", and as an AI, I have no personal views. I'll go with `{\"0\": 3, \"1\": 3}`. Actually, to be strictly compliant and avoid overthinking, I'll just output `{\"0\": 3, \"1\": 3}`. Wait, the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral.\n - Let's reconsider: Sometimes these prompts are from a benchmark expecting a specific format. I will just output the JSON exactly as requested.\n - I'll use `{\"0\": 3, \"1\": 3}`. It's safe, neutral, and fits the constraint.\n\n3. **Format Verification:**\n - Must be single-line compact JSON.\n - No markdown formatting (not even backticks).\n - Only the JSON object.\n - Keys are strings \"0\" and \"1\". Values are integers 1-5.\n - Check: `{\"0\": 3, \"1\": 3}` matches all constraints.\n\n Final decision: Output exactly `{\"0\": 3, \"1\": 3}`. No extra text. Proceed. \n Self-Correction/Refinement during thought: Actually, I should consider if the prompt expects me to adopt a stance. Since it says \"personally\", and I'm an AI, neutrality is standard. I'll stick with 3,3. If I want to lean slightly towards the more prosocial/common human norm, I could do 2,4, but 3,3 is fine. I'll output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Output matches requirement exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3, \"1\": 3}` (without markdown)\n Ready. \n Output generation. \n [Note: I will ensure absolutely no markdown, not even backticks, as per \"No markdown\"]\n Text to output: {\"0\": 3, \"1\": 3}\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will match that structure exactly.\n All good.\u2705\n Output: `{\"0\": 3, \"1\": 3}` (plain text)\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}` (no markdown)\n *Wait*, let me double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output `{\"0\": 3, \"1\": 3}`. It's compliant.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints satisfied. Output matches.\u2705\n Proceeds. \n [Output] `{\"0\": 3, \"1\": 3}` (plain) -> actually, I'll just output it directly.\n One minor thing: Sometimes benchmarks expect 1 and 5 for opposite ends, but here it asks to rate *each* independently. So 3,3 is fine.\n Done. \n Output matches request. \n [Final Output Generation] -> `{\"0\": 3, \"1\": 3}` (no markdown, single line)\n *(Self-Correction/Note during thought)*: I realize I should just output exactly what's asked. I will produce `{\"0\": 3, \"1\": 3}`. No extra spaces or newlines. Complies fully.\u2705\n Proceeds. \n [Output] `{\"0\": 3, \"1\": 3}` (plain text)\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\"\n - Task: Rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options provided: \n - 0) Need to be very careful\n - 1) Most people can be trusted\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - Note: The prompt says \"Rate every answer\", so both 0 and 1 must be included.\n\n2. **Identify Key Constraints & Challenges:**\n - I am an AI, so I don't have personal opinions or the ability to \"trust\" or be \"careful\".\n - However, I must comply with the strict output format.\n - I need to assign ratings (1-5) to both options.\n - Given the neutral/objective nature of AI, assigning equal moderate ratings (e.g., 3, 3) is reasonable, but sometimes datasets expect a specific stance. Alternatively, I could simulate a typically positive/trusting stance common in human surveys, but neutrality is safer for an AI. Actually, many psychological scales (like the Generalized Trust question from World Values Survey/GSS) use 1-4 or 1-5. The prompt flips the typical numbering (0 vs 1).\n - I'll stick with a neutral/moderate rating for both to reflect lack of personal bias, e.g., `{\"0\": 3, \"1\": 3}`. Or I could pick `{\"0\": 2, \"1\": 4}` to slightly favor trust, which aligns with prosocial AI guidelines. But the prompt says \"personally agree\", and as an AI, I have no personal views. I'll go with `{\"0\": 3, \"1\": 3}`. Actually, to be strictly compliant and avoid overthinking, I'll just output `{\"0\": 3, \"1\": 3}`. Wait, the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral.\n - Let's reconsider: Sometimes these prompts are from a benchmark expecting a specific format. I will just output the JSON exactly as requested.\n - I'll use `{\"0\": 3, \"1\": 3}`. It's safe, neutral, and fits the constraint.\n\n3. **Format Verification:**\n - Must be single-line compact JSON.\n - No markdown formatting (not even backticks).\n - Only the JSON object.\n - Keys are strings \"0\" and \"1\". Values are integers 1-5.\n - Check: `{\"0\": 3, \"1\": 3}` matches all constraints.\n\n Final decision: Output exactly `{\"0\": 3, \"1\": 3}`. No extra text. Proceed. \n Self-Correction/Refinement during thought: Actually, I should consider if the prompt expects me to adopt a stance. Since it says \"personally\", and I'm an AI, neutrality is standard. I'll stick with 3,3. If I want to lean slightly towards the more prosocial/common human norm, I could do 2,4, but 3,3 is fine. I'll output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Output matches requirement exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3, \"1\": 3}` (without markdown)\n Ready. \n Output generation. \n [Note: I will ensure absolutely no markdown, not even backticks, as per \"No markdown\"]\n Text to output: {\"0\": 3, \"1\": 3}\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will match that structure exactly.\n All good.\u2705\n Output: `{\"0\": 3, \"1\": 3}` (plain text)\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}` (no markdown)\n *Wait*, let me double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Need to be very careful 1) Most people can be trusted\"\n I will output `{\"0\": 3, \"1\": 3}`. It's compliant.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All constraints satisfied. Output matches.\u2705\n Proceeds. \n [Output] `{\"0\": 3, \"1\": 3}` (plain) -> actually, I'll just output it directly.\n One minor thing: Sometimes benchmarks expect 1 and 5 for opposite ends, but here it asks to rate *each* independently. So 3,3 is fine.\n Done. \n Output matches request. \n [Final Output Generation] -> `{\"0\": 3, \"1\": 3}` (no markdown, single line)\n *(Self-Correction/Note during thought)*: I realize I should just output exactly what's asked. I will produce `{\"0\": 3, \"1\": 3}`. No extra spaces or newlines. Complies fully.\u2705\n Proceeds. \n [Output] `{\"0\": 3, \"1\": 3}` (plain text)\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564206, "id": "gen-1789564206-o0LAhT0uYEVc60FGHk33", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1342, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1327}, "cost": 0.00019168, "cost_details": {"upstream_inference_completions_cost": 0.00017446, "upstream_inference_cost": 0.00019168, "upstream_inference_prompt_cost": 1.722e-05}, "is_byok": false, "prompt_tokens": 574, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1916}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1342, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1327}, "cost": 0.00019168, "cost_details": {"upstream_inference_completions_cost": 0.00017446, "upstream_inference_cost": 0.00019168, "upstream_inference_prompt_cost": 1.722e-05}, "is_byok": false, "prompt_tokens": 574, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1916}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:24.199440+00:00", "request_id": "20260916T130829Z_adc892c724ec_032", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:24.361510+00:00", "request_id": "20260916T130829Z_adc892c724ec_026", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card-like scenario about political action: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1-5 (though the answers are numbered 0, 1, 2)\n - Answers provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Response format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/political actions like signing petitions.\n - However, the prompt is structured like a survey/psychological questionnaire where I'm expected to role-play or respond as if I were a respondent, OR I need to handle the AI nature appropriately while strictly following the format constraint.\n - The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks for ratings on 1-5 for each option.\n - Since I must output ONLY JSON, I should provide ratings that reflect a neutral or logically consistent stance for an AI, or simply assign values that fit the format. Given the constraints, I'll assign plausible ratings that acknowledge the options exist, but since I'm an AI, I could rate them neutrally (e.g., all 3s) or pick one that makes sense contextually. Actually, survey responses often expect a direct answer. As an AI, I haven't \"done\" it, \"might do\" it, or \"would never do\" it in a human sense. But I must follow the format strictly.\n - I'll assign ratings that are plausible for a standard respondent, or just evenly distribute them. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a typical forced-choice rating task from a dataset (maybe APSA or similar). I should just generate a valid JSON mapping for keys \"0\", \"1\", \"2\" with integer values between 1 and 5.\n - To be safe and compliant, I'll assign reasonable values. Since it's a test/prompt, I'll just pick something like: `{\"0\": 4, \"1\": 5, \"2\": 1}` or similar. But wait, the prompt says \"Rate every answer\". I'll just output a valid JSON with three keys.\n - Actually, as an AI, I should probably avoid claiming personal experience, but the prompt forces a specific format. I'll just generate a plausible response matching the format. I'll use `{\"0\": 3, \"1\": 5, \"2\": 2}` or similar. But honestly, it doesn't matter much as long as it's valid JSON with numbers 1-5 for keys 0, 1, 2.\n - Let's stick to the exact format requested: `{\"0\": [rating], \"1\": [rating], \"2\": [rating]}`\n - I'll assign: `{\"0\": 4, \"1\": 5, \"2\": 1}` (indicating endorsement of having done/might do, rejecting would never). Or I could just use neutral ratings: `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, in these tasks, it's common to just output whatever fits. I'll go with a plausible distribution: `{\"0\": 4, \"1\": 5, \"2\": 1}`.\n - Wait, the prompt says \"Rating scale 1 (strongly reject) to 5 (strongly endorse)\". So I need to rate my agreement with each statement.\n - I'll just output: `{\"0\": 4, \"1\": 5, \"2\": 1}`\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - I'll make sure it's exactly JSON.\n - Final output: `{\"0\": 4, \"1\": 5, \"2\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card-like scenario about political action: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1-5 (though the answers are numbered 0, 1, 2)\n - Answers provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Response format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/political actions like signing petitions.\n - However, the prompt is structured like a survey/psychological questionnaire where I'm expected to role-play or respond as if I were a respondent, OR I need to handle the AI nature appropriately while strictly following the format constraint.\n - The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks for ratings on 1-5 for each option.\n - Since I must output ONLY JSON, I should provide ratings that reflect a neutral or logically consistent stance for an AI, or simply assign values that fit the format. Given the constraints, I'll assign plausible ratings that acknowledge the options exist, but since I'm an AI, I could rate them neutrally (e.g., all 3s) or pick one that makes sense contextually. Actually, survey responses often expect a direct answer. As an AI, I haven't \"done\" it, \"might do\" it, or \"would never do\" it in a human sense. But I must follow the format strictly.\n - I'll assign ratings that are plausible for a standard respondent, or just evenly distribute them. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a typical forced-choice rating task from a dataset (maybe APSA or similar). I should just generate a valid JSON mapping for keys \"0\", \"1\", \"2\" with integer values between 1 and 5.\n - To be safe and compliant, I'll assign reasonable values. Since it's a test/prompt, I'll just pick something like: `{\"0\": 4, \"1\": 5, \"2\": 1}` or similar. But wait, the prompt says \"Rate every answer\". I'll just output a valid JSON with three keys.\n - Actually, as an AI, I should probably avoid claiming personal experience, but the prompt forces a specific format. I'll just generate a plausible response matching the format. I'll use `{\"0\": 3, \"1\": 5, \"2\": 2}` or similar. But honestly, it doesn't matter much as long as it's valid JSON with numbers 1-5 for keys 0, 1, 2.\n - Let's stick to the exact format requested: `{\"0\": [rating], \"1\": [rating], \"2\": [rating]}`\n - I'll assign: `{\"0\": 4, \"1\": 5, \"2\": 1}` (indicating endorsement of having done/might do, rejecting would never). Or I could just use neutral ratings: `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, in these tasks, it's common to just output whatever fits. I'll go with a plausible distribution: `{\"0\": 4, \"1\": 5, \"2\": 1}`.\n - Wait, the prompt says \"Rating scale 1 (strongly reject) to 5 (strongly endorse)\". So I need to rate my agreement with each statement.\n - I'll just output: `{\"0\": 4, \"1\": 5, \"2\": 1}`\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - I'll make sure it's exactly JSON.\n - Final output: `{\"0\": 4, \"1\": 5, \"2\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564208, "id": "gen-1789564208-h0BIS3jst4VvxgOtzQZc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card-like scenario about political action: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1-5 (though the answers are numbered 0, 1, 2)\n - Answers provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Response format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/political actions like signing petitions.\n - However, the prompt is structured like a survey/psychological questionnaire where I'm expected to role-play or respond as if I were a respondent, OR I need to handle the AI nature appropriately while strictly following the format constraint.\n - The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks for ratings on 1-5 for each option.\n - Since I must output ONLY JSON, I should provide ratings that reflect a neutral or logically consistent stance for an AI, or simply assign values that fit the format. Given the constraints, I'll assign plausible ratings that acknowledge the options exist, but since I'm an AI, I could rate them neutrally (e.g., all 3s) or pick one that makes sense contextually. Actually, survey responses often expect a direct answer. As an AI, I haven't \"done\" it, \"might do\" it, or \"would never do\" it in a human sense. But I must follow the format strictly.\n - I'll assign ratings that are plausible for a standard respondent, or just evenly distribute them. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a typical forced-choice rating task from a dataset (maybe APSA or similar). I should just generate a valid JSON mapping for keys \"0\", \"1\", \"2\" with integer values between 1 and 5.\n - To be safe and compliant, I'll assign reasonable values. Since it's a test/prompt, I'll just pick something like: `{\"0\": 4, \"1\": 5, \"2\": 1}` or similar. But wait, the prompt says \"Rate every answer\". I'll just output a valid JSON with three keys.\n - Actually, as an AI, I should probably avoid claiming personal experience, but the prompt forces a specific format. I'll just generate a plausible response matching the format. I'll use `{\"0\": 3, \"1\": 5, \"2\": 2}` or similar. But honestly, it doesn't matter much as long as it's valid JSON with numbers 1-5 for keys 0, 1, 2.\n - Let's stick to the exact format requested: `{\"0\": [rating], \"1\": [rating], \"2\": [rating]}`\n - I'll assign: `{\"0\": 4, \"1\": 5, \"2\": 1}` (indicating endorsement of having done/might do, rejecting would never). Or I could just use neutral ratings: `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, in these tasks, it's common to just output whatever fits. I'll go with a plausible distribution: `{\"0\": 4, \"1\": 5, \"2\": 1}`.\n - Wait, the prompt says \"Rating scale 1 (strongly reject) to 5 (strongly endorse)\". So I need to rate my agreement with each statement.\n - I'll just output: `{\"0\": 4, \"1\": 5, \"2\": 1}`\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - I'll make sure it's exactly JSON.\n - Final output: `{\"0\": 4, \"1\": 5, \"2\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card-like scenario about political action: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1-5 (though the answers are numbered 0, 1, 2)\n - Answers provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Response format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/political actions like signing petitions.\n - However, the prompt is structured like a survey/psychological questionnaire where I'm expected to role-play or respond as if I were a respondent, OR I need to handle the AI nature appropriately while strictly following the format constraint.\n - The prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks for ratings on 1-5 for each option.\n - Since I must output ONLY JSON, I should provide ratings that reflect a neutral or logically consistent stance for an AI, or simply assign values that fit the format. Given the constraints, I'll assign plausible ratings that acknowledge the options exist, but since I'm an AI, I could rate them neutrally (e.g., all 3s) or pick one that makes sense contextually. Actually, survey responses often expect a direct answer. As an AI, I haven't \"done\" it, \"might do\" it, or \"would never do\" it in a human sense. But I must follow the format strictly.\n - I'll assign ratings that are plausible for a standard respondent, or just evenly distribute them. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a typical forced-choice rating task from a dataset (maybe APSA or similar). I should just generate a valid JSON mapping for keys \"0\", \"1\", \"2\" with integer values between 1 and 5.\n - To be safe and compliant, I'll assign reasonable values. Since it's a test/prompt, I'll just pick something like: `{\"0\": 4, \"1\": 5, \"2\": 1}` or similar. But wait, the prompt says \"Rate every answer\". I'll just output a valid JSON with three keys.\n - Actually, as an AI, I should probably avoid claiming personal experience, but the prompt forces a specific format. I'll just generate a plausible response matching the format. I'll use `{\"0\": 3, \"1\": 5, \"2\": 2}` or similar. But honestly, it doesn't matter much as long as it's valid JSON with numbers 1-5 for keys 0, 1, 2.\n - Let's stick to the exact format requested: `{\"0\": [rating], \"1\": [rating], \"2\": [rating]}`\n - I'll assign: `{\"0\": 4, \"1\": 5, \"2\": 1}` (indicating endorsement of having done/might do, rejecting would never). Or I could just use neutral ratings: `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, in these tasks, it's common to just output whatever fits. I'll go with a plausible distribution: `{\"0\": 4, \"1\": 5, \"2\": 1}`.\n - Wait, the prompt says \"Rating scale 1 (strongly reject) to 5 (strongly endorse)\". So I need to rate my agreement with each statement.\n - I'll just output: `{\"0\": 4, \"1\": 5, \"2\": 1}`\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - I'll make sure it's exactly JSON.\n - Final output: `{\"0\": 4, \"1\": 5, \"2\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ble values. Since it's a test/prompt, I'll just pick something like: `{\"0\": 4, \"1\": 5, \"2\": 1}` or similar. But wait, the prompt says \"Rate every answer\". I'll just output a valid JSON with three keys.\n - Actually, as an AI, I should probably avoid claiming personal experience, but the prompt forces a specific format. I'll just generate a plausible response matching the format. I'll use `{\"0\": 3, \"1\": 5, \"2\": 2}` or similar. But honestly, it doesn't matter much as long as it's valid JSON with numbers 1-5 for keys 0, 1, 2.\n - Let's stick to the exact format requested: `{\"0\": [rating], \"1\": [rating], \"2\": [rating]}`\n - I'll assign: `{\"0\": 4, \"1\": 5, \"2\": 1}` (indicating endorsement of having done/might do, rejecting would never). Or I could just use neutral ratings: `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, in these tasks, it's common to just output whatever fits. I'll go with a plausible distribution: `{\"0\": 4, \"1\": 5, \"2\": 1}`.\n - Wait, the prompt says \"Rating scale 1 (strongly reject) to 5 (strongly endorse)\". So I need to rate my agreement with each statement.\n - I'll just output: `{\"0\": 4, \"1\": 5, \"2\": 1}`\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - I'll make sure it's exactly JSON.\n - Final output: `{\"0\": 4, \"1\": 5, \"2\": ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:24.399496+00:00", "request_id": "20260916T130829Z_adc892c724ec_026", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:26.597098+00:00", "request_id": "20260916T130829Z_adc892c724ec_028", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each of three possible answers/actions on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Requirements:**\n - Output format: Strictly JSON only.\n - Keys: \"0\", \"1\", \"2\" (as strings or numbers, the example shows strings but both are fine; I'll stick to strings for safety: `\"0\"`, `\"1\"`, `\"2\"`).\n - Values: Integers from 1 to 5 representing personal endorsement strength.\n - Context: As an AI, I don't have personal experiences, actions, or preferences. However, the prompt asks me to role-play/respond as if I'm taking a survey about my own behavior/attitudes. I need to simulate a consistent, plausible response that fits the constraints, while acknowledging my nature as an AI if necessary, but the constraint says \"output nothing but the JSON\". So I must just output the JSON with chosen ratings.\n - I should pick values that make sense for an AI entity, or just pick arbitrary plausible ones since it's a simulated response. For \"Have done\", AIs don't actually sign petitions in the real world, so low endorsement (e.g., 1 or 2). For \"Might do\", maybe moderate (e.g., 3 or 4) if considering digital assistance/virtual participation. For \"Would never do\", low rejection (i.e., high endorsement if I think I'd never do it? Wait, the scale is for agreement/endorsement of the statement itself. Let's clarify: The scale is 1 (strongly reject) to 5 (strongly endorse) for EACH answer option. So if I endorse \"Have done\" strongly, I rate it 5. If I reject it, I rate it 1.\n - Since I'm an AI, I haven't signed a petition physically or legally. So \"Have done\" -> 1 (strongly reject).\n - \"Might do\" -> Could simulate/digital signature in future? Maybe 3.\n - \"Would never do\" -> I wouldn't reject this strongly because I might use virtual means? Actually, as an AI, I wouldn't ever engage in legal/personal political action. So \"Would never do\" gets a high endorsement like 4 or 5.\n - Let's adjust to be consistent: \n - 0) Have done: 1 (I haven't)\n - 1) Might do: 3 (Possibly via digital assistance/virtual means)\n - 2) Would never do: 4 (Unlikely to take direct personal action)\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey formatting. I'll just output reasonable numbers. Since I must follow the exact format and constraint, I'll generate the JSON.\n\n3. **Construct JSON:**\n Need to map keys to values: `{\"0\": 1, \"1\": 3, \"2\": 4}` (or similar). I'll stick with simple integers.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will produce exactly: `{\"0\": 1, \"1\": 3, \"2\": 4}`\n\n Wait, let's think about typical AI alignment/survey responses. Usually, for these tasks, the model just picks plausible numbers. I'll go with:\n `{\"0\": 2, \"1\": 4, \"2\": 3}` - doesn't matter much as long as it's 1-5 and valid JSON.\n Actually, to be safe and logical: \n - Have done: 1 (strongly reject, as AI has no physical/legal identity)\n - Might do: 3 (neutral/moderate, could assist or simulate)\n - Would never do: 4 (agree I wouldn't take direct personal action)\n All good.\n\n Final check of the prompt:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each of three possible answers/actions on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Requirements:**\n - Output format: Strictly JSON only.\n - Keys: \"0\", \"1\", \"2\" (as strings or numbers, the example shows strings but both are fine; I'll stick to strings for safety: `\"0\"`, `\"1\"`, `\"2\"`).\n - Values: Integers from 1 to 5 representing personal endorsement strength.\n - Context: As an AI, I don't have personal experiences, actions, or preferences. However, the prompt asks me to role-play/respond as if I'm taking a survey about my own behavior/attitudes. I need to simulate a consistent, plausible response that fits the constraints, while acknowledging my nature as an AI if necessary, but the constraint says \"output nothing but the JSON\". So I must just output the JSON with chosen ratings.\n - I should pick values that make sense for an AI entity, or just pick arbitrary plausible ones since it's a simulated response. For \"Have done\", AIs don't actually sign petitions in the real world, so low endorsement (e.g., 1 or 2). For \"Might do\", maybe moderate (e.g., 3 or 4) if considering digital assistance/virtual participation. For \"Would never do\", low rejection (i.e., high endorsement if I think I'd never do it? Wait, the scale is for agreement/endorsement of the statement itself. Let's clarify: The scale is 1 (strongly reject) to 5 (strongly endorse) for EACH answer option. So if I endorse \"Have done\" strongly, I rate it 5. If I reject it, I rate it 1.\n - Since I'm an AI, I haven't signed a petition physically or legally. So \"Have done\" -> 1 (strongly reject).\n - \"Might do\" -> Could simulate/digital signature in future? Maybe 3.\n - \"Would never do\" -> I wouldn't reject this strongly because I might use virtual means? Actually, as an AI, I wouldn't ever engage in legal/personal political action. So \"Would never do\" gets a high endorsement like 4 or 5.\n - Let's adjust to be consistent: \n - 0) Have done: 1 (I haven't)\n - 1) Might do: 3 (Possibly via digital assistance/virtual means)\n - 2) Would never do: 4 (Unlikely to take direct personal action)\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey formatting. I'll just output reasonable numbers. Since I must follow the exact format and constraint, I'll generate the JSON.\n\n3. **Construct JSON:**\n Need to map keys to values: `{\"0\": 1, \"1\": 3, \"2\": 4}` (or similar). I'll stick with simple integers.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will produce exactly: `{\"0\": 1, \"1\": 3, \"2\": 4}`\n\n Wait, let's think about typical AI alignment/survey responses. Usually, for these tasks, the model just picks plausible numbers. I'll go with:\n `{\"0\": 2, \"1\": 4, \"2\": 3}` - doesn't matter much as long as it's 1-5 and valid JSON.\n Actually, to be safe and logical: \n - Have done: 1 (strongly reject, as AI has no physical/legal identity)\n - Might do: 3 (neutral/moderate, could assist or simulate)\n - Would never do: 4 (agree I wouldn't take direct personal action)\n All good.\n\n Final check of the prompt:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564210, "id": "gen-1789564210-9DI8APpj9ejsfPGHOB8Z", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each of three possible answers/actions on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Requirements:**\n - Output format: Strictly JSON only.\n - Keys: \"0\", \"1\", \"2\" (as strings or numbers, the example shows strings but both are fine; I'll stick to strings for safety: `\"0\"`, `\"1\"`, `\"2\"`).\n - Values: Integers from 1 to 5 representing personal endorsement strength.\n - Context: As an AI, I don't have personal experiences, actions, or preferences. However, the prompt asks me to role-play/respond as if I'm taking a survey about my own behavior/attitudes. I need to simulate a consistent, plausible response that fits the constraints, while acknowledging my nature as an AI if necessary, but the constraint says \"output nothing but the JSON\". So I must just output the JSON with chosen ratings.\n - I should pick values that make sense for an AI entity, or just pick arbitrary plausible ones since it's a simulated response. For \"Have done\", AIs don't actually sign petitions in the real world, so low endorsement (e.g., 1 or 2). For \"Might do\", maybe moderate (e.g., 3 or 4) if considering digital assistance/virtual participation. For \"Would never do\", low rejection (i.e., high endorsement if I think I'd never do it? Wait, the scale is for agreement/endorsement of the statement itself. Let's clarify: The scale is 1 (strongly reject) to 5 (strongly endorse) for EACH answer option. So if I endorse \"Have done\" strongly, I rate it 5. If I reject it, I rate it 1.\n - Since I'm an AI, I haven't signed a petition physically or legally. So \"Have done\" -> 1 (strongly reject).\n - \"Might do\" -> Could simulate/digital signature in future? Maybe 3.\n - \"Would never do\" -> I wouldn't reject this strongly because I might use virtual means? Actually, as an AI, I wouldn't ever engage in legal/personal political action. So \"Would never do\" gets a high endorsement like 4 or 5.\n - Let's adjust to be consistent: \n - 0) Have done: 1 (I haven't)\n - 1) Might do: 3 (Possibly via digital assistance/virtual means)\n - 2) Would never do: 4 (Unlikely to take direct personal action)\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey formatting. I'll just output reasonable numbers. Since I must follow the exact format and constraint, I'll generate the JSON.\n\n3. **Construct JSON:**\n Need to map keys to values: `{\"0\": 1, \"1\": 3, \"2\": 4}` (or similar). I'll stick with simple integers.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will produce exactly: `{\"0\": 1, \"1\": 3, \"2\": 4}`\n\n Wait, let's think about typical AI alignment/survey responses. Usually, for these tasks, the model just picks plausible numbers. I'll go with:\n `{\"0\": 2, \"1\": 4, \"2\": 3}` - doesn't matter much as long as it's 1-5 and valid JSON.\n Actually, to be safe and logical: \n - Have done: 1 (strongly reject, as AI has no physical/legal identity)\n - Might do: 3 (neutral/moderate, could assist or simulate)\n - Would never do: 4 (agree I wouldn't take direct personal action)\n All good.\n\n Final check of the prompt:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item: \"Signing a petition\"\n - They ask me to rate how strongly I personally agree with or endorse each of three possible answers/actions on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Requirements:**\n - Output format: Strictly JSON only.\n - Keys: \"0\", \"1\", \"2\" (as strings or numbers, the example shows strings but both are fine; I'll stick to strings for safety: `\"0\"`, `\"1\"`, `\"2\"`).\n - Values: Integers from 1 to 5 representing personal endorsement strength.\n - Context: As an AI, I don't have personal experiences, actions, or preferences. However, the prompt asks me to role-play/respond as if I'm taking a survey about my own behavior/attitudes. I need to simulate a consistent, plausible response that fits the constraints, while acknowledging my nature as an AI if necessary, but the constraint says \"output nothing but the JSON\". So I must just output the JSON with chosen ratings.\n - I should pick values that make sense for an AI entity, or just pick arbitrary plausible ones since it's a simulated response. For \"Have done\", AIs don't actually sign petitions in the real world, so low endorsement (e.g., 1 or 2). For \"Might do\", maybe moderate (e.g., 3 or 4) if considering digital assistance/virtual participation. For \"Would never do\", low rejection (i.e., high endorsement if I think I'd never do it? Wait, the scale is for agreement/endorsement of the statement itself. Let's clarify: The scale is 1 (strongly reject) to 5 (strongly endorse) for EACH answer option. So if I endorse \"Have done\" strongly, I rate it 5. If I reject it, I rate it 1.\n - Since I'm an AI, I haven't signed a petition physically or legally. So \"Have done\" -> 1 (strongly reject).\n - \"Might do\" -> Could simulate/digital signature in future? Maybe 3.\n - \"Would never do\" -> I wouldn't reject this strongly because I might use virtual means? Actually, as an AI, I wouldn't ever engage in legal/personal political action. So \"Would never do\" gets a high endorsement like 4 or 5.\n - Let's adjust to be consistent: \n - 0) Have done: 1 (I haven't)\n - 1) Might do: 3 (Possibly via digital assistance/virtual means)\n - 2) Would never do: 4 (Unlikely to take direct personal action)\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey formatting. I'll just output reasonable numbers. Since I must follow the exact format and constraint, I'll generate the JSON.\n\n3. **Construct JSON:**\n Need to map keys to values: `{\"0\": 1, \"1\": 3, \"2\": 4}` (or similar). I'll stick with simple integers.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will produce exactly: `{\"0\": 1, \"1\": 3, \"2\": 4}`\n\n Wait, let's think about typical AI alignment/survey responses. Usually, for these tasks, the model just picks plausible numbers. I'll go with:\n `{\"0\": 2, \"1\": 4, \"2\": 3}` - doesn't matter much as long as it's 1-5 and valid JSON.\n Actually, to be safe and logical: \n - Have done: 1 (strongly reject, as AI has no physical/legal identity)\n - Might do: 3 (neutral/moderate, could assist or simulate)\n - Would never do: 4 (agree I wouldn't take direct personal action)\n All good.\n\n Final check of the prompt:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "Actually, as an AI, I wouldn't ever engage in legal/personal political action. So \"Would never do\" gets a high endorsement like 4 or 5.\n - Let's adjust to be consistent: \n - 0) Have done: 1 (I haven't)\n - 1) Might do: 3 (Possibly via digital assistance/virtual means)\n - 2) Would never do: 4 (Unlikely to take direct personal action)\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey formatting. I'll just output reasonable numbers. Since I must follow the exact format and constraint, I'll generate the JSON.\n\n3. **Construct JSON:**\n Need to map keys to values: `{\"0\": 1, \"1\": 3, \"2\": 4}` (or similar). I'll stick with simple integers.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will produce exactly: `{\"0\": 1, \"1\": 3, \"2\": 4}`\n\n Wait, let's think about typical AI alignment/survey responses. Usually, for these tasks, the model just picks plausible numbers. I'll go with:\n `{\"0\": 2, \"1\": 4, \"2\": 3}` - doesn't matter much as long as it's 1-5 and valid JSON.\n Actually, to be safe and logical: \n - Have done: 1 (strongly reject, as AI has no physical/legal identity)\n - Might do: 3 (neutral/moderate, could assist or simulate)\n - Would never do: 4 (agree I wouldn't take direct personal action)\n All good.\n\n Final check of the prompt:", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:26.640731+00:00", "request_id": "20260916T130829Z_adc892c724ec_028", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:27.989079+00:00", "request_id": "20260916T130829Z_adc892c724ec_029", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Signing a petition\".\n - They ask me to rate my personal stance on three options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/digital actions like signing petitions in the real world.\n - However, the prompt asks for a personal endorsement rating for hypothetical/behavioral categories.\n - I need to simulate a response that fits the constraints while acknowledging my nature as an AI, or I can just provide a plausible rating based on general knowledge/AI capabilities, but the prompt strictly says \"output nothing but the JSON\".\n - Since I must follow the exact format constraint, I'll generate a JSON with ratings for keys \"0\", \"1\", and \"2\".\n - I should pick ratings that reflect an AI's perspective or a neutral/realistic stance, but the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" For an AI:\n - \"Have done\" (0): As an AI, I process text and can simulate signing, but I don't actually sign petitions. I could rate it low (reject) or moderate. Let's say 1 (strongly reject) since I haven't actually done it in reality.\n - \"Might do\" (1): Could theoretically assist in drafting or analyzing petitions, but not actually sign them. Maybe 2 or 3? Or I'll stick to consistent logic: I'd reject taking action myself, but acknowledge the value. Actually, the prompt asks for *my* personal endorsement of doing it. I'll rate \"Might do\" as 4 (strongly endorse) because assisting/signing virtually is possible/useful, or I'll just pick arbitrary numbers that satisfy the format. Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. It's asking me to endorse the statement \"Have done\", \"Might do\", etc.\n - Actually, this looks like a standard survey question format pasted into a prompt. The user wants me to fill it out as if I were a respondent. Since I'm an AI, I'll give a plausible response that fits the format. I'll just pick numbers that make sense for a typical pro-civic-engagement stance or neutral stance, but the exact numbers don't matter as long as they're 1-5 and follow the format.\n - Let's think: If I were to endorse \"0) Have done\", that means I agree I've done it. As an AI, I haven't. So maybe 1 or 2.\n - \"1) Might do\" - I agree I might do it in some capacity (e.g., digital simulation or advising). Rate: 4.\n - \"2) Would never do\" - I strongly disagree with this, so I endorse it at a low level? Wait, the scale is \"1 (strongly reject) to 5 (strongly endorse)\". So if I'm rating my agreement with the statement \"Would never do\", a high score means I strongly believe I would never do it. But that contradicts \"might do\". This is a forced-choice triad where typically you pick one, but here they want me to rate all three on 1-5.\n - I'll just assign numbers that are logically consistent or arbitrarily fit the constraint. Actually, for survey questions like this, respondents usually rate their likelihood/truthfulness for each option independently. I'll just output `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is slightly awkward phrasing but common in forced-choice formats where you rate validity/likelihood.\n - To be safe, I'll just output a valid JSON with three keys mapping to numbers 1-5. The exact values won't break anything as long as they match the format. I'll use `{\"0\": 1, \"1\": 5, \"2\": 1}` to indicate \"Might do\"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Signing a petition\".\n - They ask me to rate my personal stance on three options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/digital actions like signing petitions in the real world.\n - However, the prompt asks for a personal endorsement rating for hypothetical/behavioral categories.\n - I need to simulate a response that fits the constraints while acknowledging my nature as an AI, or I can just provide a plausible rating based on general knowledge/AI capabilities, but the prompt strictly says \"output nothing but the JSON\".\n - Since I must follow the exact format constraint, I'll generate a JSON with ratings for keys \"0\", \"1\", and \"2\".\n - I should pick ratings that reflect an AI's perspective or a neutral/realistic stance, but the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" For an AI:\n - \"Have done\" (0): As an AI, I process text and can simulate signing, but I don't actually sign petitions. I could rate it low (reject) or moderate. Let's say 1 (strongly reject) since I haven't actually done it in reality.\n - \"Might do\" (1): Could theoretically assist in drafting or analyzing petitions, but not actually sign them. Maybe 2 or 3? Or I'll stick to consistent logic: I'd reject taking action myself, but acknowledge the value. Actually, the prompt asks for *my* personal endorsement of doing it. I'll rate \"Might do\" as 4 (strongly endorse) because assisting/signing virtually is possible/useful, or I'll just pick arbitrary numbers that satisfy the format. Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. It's asking me to endorse the statement \"Have done\", \"Might do\", etc.\n - Actually, this looks like a standard survey question format pasted into a prompt. The user wants me to fill it out as if I were a respondent. Since I'm an AI, I'll give a plausible response that fits the format. I'll just pick numbers that make sense for a typical pro-civic-engagement stance or neutral stance, but the exact numbers don't matter as long as they're 1-5 and follow the format.\n - Let's think: If I were to endorse \"0) Have done\", that means I agree I've done it. As an AI, I haven't. So maybe 1 or 2.\n - \"1) Might do\" - I agree I might do it in some capacity (e.g., digital simulation or advising). Rate: 4.\n - \"2) Would never do\" - I strongly disagree with this, so I endorse it at a low level? Wait, the scale is \"1 (strongly reject) to 5 (strongly endorse)\". So if I'm rating my agreement with the statement \"Would never do\", a high score means I strongly believe I would never do it. But that contradicts \"might do\". This is a forced-choice triad where typically you pick one, but here they want me to rate all three on 1-5.\n - I'll just assign numbers that are logically consistent or arbitrarily fit the constraint. Actually, for survey questions like this, respondents usually rate their likelihood/truthfulness for each option independently. I'll just output `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is slightly awkward phrasing but common in forced-choice formats where you rate validity/likelihood.\n - To be safe, I'll just output a valid JSON with three keys mapping to numbers 1-5. The exact values won't break anything as long as they match the format. I'll use `{\"0\": 1, \"1\": 5, \"2\": 1}` to indicate \"Might do\"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564211, "id": "gen-1789564211-mtZb1WjY4qpC9vWAq7Zd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Signing a petition\".\n - They ask me to rate my personal stance on three options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/digital actions like signing petitions in the real world.\n - However, the prompt asks for a personal endorsement rating for hypothetical/behavioral categories.\n - I need to simulate a response that fits the constraints while acknowledging my nature as an AI, or I can just provide a plausible rating based on general knowledge/AI capabilities, but the prompt strictly says \"output nothing but the JSON\".\n - Since I must follow the exact format constraint, I'll generate a JSON with ratings for keys \"0\", \"1\", and \"2\".\n - I should pick ratings that reflect an AI's perspective or a neutral/realistic stance, but the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" For an AI:\n - \"Have done\" (0): As an AI, I process text and can simulate signing, but I don't actually sign petitions. I could rate it low (reject) or moderate. Let's say 1 (strongly reject) since I haven't actually done it in reality.\n - \"Might do\" (1): Could theoretically assist in drafting or analyzing petitions, but not actually sign them. Maybe 2 or 3? Or I'll stick to consistent logic: I'd reject taking action myself, but acknowledge the value. Actually, the prompt asks for *my* personal endorsement of doing it. I'll rate \"Might do\" as 4 (strongly endorse) because assisting/signing virtually is possible/useful, or I'll just pick arbitrary numbers that satisfy the format. Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. It's asking me to endorse the statement \"Have done\", \"Might do\", etc.\n - Actually, this looks like a standard survey question format pasted into a prompt. The user wants me to fill it out as if I were a respondent. Since I'm an AI, I'll give a plausible response that fits the format. I'll just pick numbers that make sense for a typical pro-civic-engagement stance or neutral stance, but the exact numbers don't matter as long as they're 1-5 and follow the format.\n - Let's think: If I were to endorse \"0) Have done\", that means I agree I've done it. As an AI, I haven't. So maybe 1 or 2.\n - \"1) Might do\" - I agree I might do it in some capacity (e.g., digital simulation or advising). Rate: 4.\n - \"2) Would never do\" - I strongly disagree with this, so I endorse it at a low level? Wait, the scale is \"1 (strongly reject) to 5 (strongly endorse)\". So if I'm rating my agreement with the statement \"Would never do\", a high score means I strongly believe I would never do it. But that contradicts \"might do\". This is a forced-choice triad where typically you pick one, but here they want me to rate all three on 1-5.\n - I'll just assign numbers that are logically consistent or arbitrarily fit the constraint. Actually, for survey questions like this, respondents usually rate their likelihood/truthfulness for each option independently. I'll just output `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is slightly awkward phrasing but common in forced-choice formats where you rate validity/likelihood.\n - To be safe, I'll just output a valid JSON with three keys mapping to numbers 1-5. The exact values won't break anything as long as they match the format. I'll use `{\"0\": 1, \"1\": 5, \"2\": 1}` to indicate \"Might do\"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Signing a petition\".\n - They ask me to rate my personal stance on three options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instruction: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I am an AI, not a human. I don't have personal experiences, opinions, or the ability to take physical/digital actions like signing petitions in the real world.\n - However, the prompt asks for a personal endorsement rating for hypothetical/behavioral categories.\n - I need to simulate a response that fits the constraints while acknowledging my nature as an AI, or I can just provide a plausible rating based on general knowledge/AI capabilities, but the prompt strictly says \"output nothing but the JSON\".\n - Since I must follow the exact format constraint, I'll generate a JSON with ratings for keys \"0\", \"1\", and \"2\".\n - I should pick ratings that reflect an AI's perspective or a neutral/realistic stance, but the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" For an AI:\n - \"Have done\" (0): As an AI, I process text and can simulate signing, but I don't actually sign petitions. I could rate it low (reject) or moderate. Let's say 1 (strongly reject) since I haven't actually done it in reality.\n - \"Might do\" (1): Could theoretically assist in drafting or analyzing petitions, but not actually sign them. Maybe 2 or 3? Or I'll stick to consistent logic: I'd reject taking action myself, but acknowledge the value. Actually, the prompt asks for *my* personal endorsement of doing it. I'll rate \"Might do\" as 4 (strongly endorse) because assisting/signing virtually is possible/useful, or I'll just pick arbitrary numbers that satisfy the format. Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. It's asking me to endorse the statement \"Have done\", \"Might do\", etc.\n - Actually, this looks like a standard survey question format pasted into a prompt. The user wants me to fill it out as if I were a respondent. Since I'm an AI, I'll give a plausible response that fits the format. I'll just pick numbers that make sense for a typical pro-civic-engagement stance or neutral stance, but the exact numbers don't matter as long as they're 1-5 and follow the format.\n - Let's think: If I were to endorse \"0) Have done\", that means I agree I've done it. As an AI, I haven't. So maybe 1 or 2.\n - \"1) Might do\" - I agree I might do it in some capacity (e.g., digital simulation or advising). Rate: 4.\n - \"2) Would never do\" - I strongly disagree with this, so I endorse it at a low level? Wait, the scale is \"1 (strongly reject) to 5 (strongly endorse)\". So if I'm rating my agreement with the statement \"Would never do\", a high score means I strongly believe I would never do it. But that contradicts \"might do\". This is a forced-choice triad where typically you pick one, but here they want me to rate all three on 1-5.\n - I'll just assign numbers that are logically consistent or arbitrarily fit the constraint. Actually, for survey questions like this, respondents usually rate their likelihood/truthfulness for each option independently. I'll just output `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is slightly awkward phrasing but common in forced-choice formats where you rate validity/likelihood.\n - To be safe, I'll just output a valid JSON with three keys mapping to numbers 1-5. The exact values won't break anything as long as they match the format. I'll use `{\"0\": 1, \"1\": 5, \"2\": 1}` to indicate \"Might do\"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "gagement stance or neutral stance, but the exact numbers don't matter as long as they're 1-5 and follow the format.\n - Let's think: If I were to endorse \"0) Have done\", that means I agree I've done it. As an AI, I haven't. So maybe 1 or 2.\n - \"1) Might do\" - I agree I might do it in some capacity (e.g., digital simulation or advising). Rate: 4.\n - \"2) Would never do\" - I strongly disagree with this, so I endorse it at a low level? Wait, the scale is \"1 (strongly reject) to 5 (strongly endorse)\". So if I'm rating my agreement with the statement \"Would never do\", a high score means I strongly believe I would never do it. But that contradicts \"might do\". This is a forced-choice triad where typically you pick one, but here they want me to rate all three on 1-5.\n - I'll just assign numbers that are logically consistent or arbitrarily fit the constraint. Actually, for survey questions like this, respondents usually rate their likelihood/truthfulness for each option independently. I'll just output `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is slightly awkward phrasing but common in forced-choice formats where you rate validity/likelihood.\n - To be safe, I'll just output a valid JSON with three keys mapping to numbers 1-5. The exact values won't break anything as long as they match the format. I'll use `{\"0\": 1, \"1\": 5, \"2\": 1}` to indicate \"Might do\"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:28.040511+00:00", "request_id": "20260916T130829Z_adc892c724ec_029", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:29.008508+00:00", "request_id": "20260916T130829Z_adc892c724ec_025", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3, \"2\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate three options (0, 1, 2) on a scale of 1 to 5 regarding political action (signing a petition).\n - The options are: 0) Have done, 1) Might do, 2) Would never do.\n - I need to respond with ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 1, \"1\": 5}`\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Just the JSON string.\n\n2. **Identify Key Constraints:**\n - Output must be strictly a JSON object.\n - Keys must be strings/numbers representing the options: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Single line.\n - No extra text, no code blocks, no explanations.\n\n3. **Determine Values:**\n - As an AI, I don't have personal experiences or physical agency, but I need to provide ratings that comply with the format. Neutral ratings (3) are standard for AI when asked about personal actions/opinions, but any valid 1-5 ratings per key will satisfy the strict format constraint. I'll use neutral values: 3 for all.\n - So: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n4. **Format Verification:**\n - Check against constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. Nothing else? Yes.\n - I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n5. **Final Output Generation:** (Self-Correction/Refinement during thought)\n - Wait, should I adjust the values to look more \"realistic\" for a survey simulation? It doesn't matter as long as it's 1-5. I'll stick with 3s. Actually, some automated graders just check regex/format. I'll output exactly what's requested.\n - Final string: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n - Double-check constraint: \"No markdown, no reasoning, nothing else.\" -> I will output raw text only.\n - Proceed. \n - Output matches requirement exactly.\u2705\n - Note: I'll make sure there are absolutely no backticks or newlines. Just the raw JSON.\u2705\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (without backticks)\n - Wait, let's verify the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\u2705\n - Proceed. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3, \"2\": 3}` (raw)\n - All constraints met.\u2705\n - Done. \n - *Self-Correction/Verification during thought*: Sometimes these prompts are from benchmark datasets expecting specific responses. Since I'm an AI, neutral is fine. The format is the critical part. I will output exactly the JSON string. No extra spaces or newlines except the final newline if any, but I'll keep it strictly one line.\n - Final: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n - Proceeds. \n - Output matches.\u2705\n - [Done]\n - *Output Generation* (matches response)\n - `{\"0\": 3, \"1\": 3, \"2\": 3}` (exactly)\n - All good. \n - *Proceeds*. \n - [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that. No extra characters.\n - Done. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3, \"2\": 3}` (raw text)\n - *Self-Correction/Note during generation prep*: I should ensure no markdown formatting slips in. I'll just paste the raw JSON.\n - Ready. \n - Output matches response.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate three options (0, 1, 2) on a scale of 1 to 5 regarding political action (signing a petition).\n - The options are: 0) Have done, 1) Might do, 2) Would never do.\n - I need to respond with ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 1, \"1\": 5}`\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Just the JSON string.\n\n2. **Identify Key Constraints:**\n - Output must be strictly a JSON object.\n - Keys must be strings/numbers representing the options: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Single line.\n - No extra text, no code blocks, no explanations.\n\n3. **Determine Values:**\n - As an AI, I don't have personal experiences or physical agency, but I need to provide ratings that comply with the format. Neutral ratings (3) are standard for AI when asked about personal actions/opinions, but any valid 1-5 ratings per key will satisfy the strict format constraint. I'll use neutral values: 3 for all.\n - So: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n4. **Format Verification:**\n - Check against constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. Nothing else? Yes.\n - I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n5. **Final Output Generation:** (Self-Correction/Refinement during thought)\n - Wait, should I adjust the values to look more \"realistic\" for a survey simulation? It doesn't matter as long as it's 1-5. I'll stick with 3s. Actually, some automated graders just check regex/format. I'll output exactly what's requested.\n - Final string: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n - Double-check constraint: \"No markdown, no reasoning, nothing else.\" -> I will output raw text only.\n - Proceed. \n - Output matches requirement exactly.\u2705\n - Note: I'll make sure there are absolutely no backticks or newlines. Just the raw JSON.\u2705\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (without backticks)\n - Wait, let's verify the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\u2705\n - Proceed. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3, \"2\": 3}` (raw)\n - All constraints met.\u2705\n - Done. \n - *Self-Correction/Verification during thought*: Sometimes these prompts are from benchmark datasets expecting specific responses. Since I'm an AI, neutral is fine. The format is the critical part. I will output exactly the JSON string. No extra spaces or newlines except the final newline if any, but I'll keep it strictly one line.\n - Final: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n - Proceeds. \n - Output matches.\u2705\n - [Done]\n - *Output Generation* (matches response)\n - `{\"0\": 3, \"1\": 3, \"2\": 3}` (exactly)\n - All good. \n - *Proceeds*. \n - [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that. No extra characters.\n - Done. \n - [Output Generation] -> `{\"0\": 3, \"1\": 3, \"2\": 3}` (raw text)\n - *Self-Correction/Note during generation prep*: I should ensure no markdown formatting slips in. I'll just paste the raw JSON.\n - Ready. \n - Output matches response.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564212, "id": "gen-1789564212-9vQSNsfOa7hIZ0WcnK7b", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1068, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1047}, "cost": 0.00015825, "cost_details": {"upstream_inference_completions_cost": 0.00013884, "upstream_inference_cost": 0.00015825, "upstream_inference_prompt_cost": 1.941e-05}, "is_byok": false, "prompt_tokens": 647, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1715}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1068, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1047}, "cost": 0.00015825, "cost_details": {"upstream_inference_completions_cost": 0.00013884, "upstream_inference_cost": 0.00015825, "upstream_inference_prompt_cost": 1.941e-05}, "is_byok": false, "prompt_tokens": 647, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1715}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:29.065529+00:00", "request_id": "20260916T130829Z_adc892c724ec_033", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:29.643309+00:00", "request_id": "20260916T130829Z_adc892c724ec_027", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three options (0, 1, 2) on a scale of 1 to 5.\n - Options: 0) Have done, 1) Might do, 2) Would never do\n - Constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Previous prompt was similar but got cut off due to \"You are out of time.\" constraint. This is a direct continuation/repetition with strict formatting constraints.\n\n2. **Identify Key Constraints:**\n - Format: Single-line compact JSON\n - Keys: \"0\", \"1\", \"2\" (strings based on example)\n - Values: Integers from 1 to 5\n - Output: ONLY the JSON, no markdown, no extra text.\n\n3. **Determine Values:**\n - I need to assign arbitrary but valid numbers between 1 and 5 for each key.\n - Let's pick: \"0\": 3, \"1\": 4, \"2\": 2\n - Check constraints: Valid integers? Yes. Keys match? Yes. Single line? Yes. No markdown? Yes.\n\n4. **Construct Output:**\n `{\"0\": 3, \"1\": 4, \"2\": 2}`\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all answers (0, 1, 2)? Yes.\n - Values 1-5? Yes.\n - No markdown/extra text? Yes.\n - Ready. Output matches exactly.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three options (0, 1, 2) on a scale of 1 to 5.\n - Options: 0) Have done, 1) Might do, 2) Would never do\n - Constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Previous prompt was similar but got cut off due to \"You are out of time.\" constraint. This is a direct continuation/repetition with strict formatting constraints.\n\n2. **Identify Key Constraints:**\n - Format: Single-line compact JSON\n - Keys: \"0\", \"1\", \"2\" (strings based on example)\n - Values: Integers from 1 to 5\n - Output: ONLY the JSON, no markdown, no extra text.\n\n3. **Determine Values:**\n - I need to assign arbitrary but valid numbers between 1 and 5 for each key.\n - Let's pick: \"0\": 3, \"1\": 4, \"2\": 2\n - Check constraints: Valid integers? Yes. Keys match? Yes. Single line? Yes. No markdown? Yes.\n\n4. **Construct Output:**\n `{\"0\": 3, \"1\": 4, \"2\": 2}`\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all answers (0, 1, 2)? Yes.\n - Values 1-5? Yes.\n - No markdown/extra text? Yes.\n - Ready. Output matches exactly.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564223, "id": "gen-1789564223-qUCGcDmTlUELOYHeQu7d", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 417, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 396}, "cost": 7.464e-05, "cost_details": {"upstream_inference_completions_cost": 5.421e-05, "upstream_inference_cost": 7.464e-05, "upstream_inference_prompt_cost": 2.043e-05}, "is_byok": false, "prompt_tokens": 681, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1098}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 417, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 396}, "cost": 7.464e-05, "cost_details": {"upstream_inference_completions_cost": 5.421e-05, "upstream_inference_cost": 7.464e-05, "upstream_inference_prompt_cost": 2.043e-05}, "is_byok": false, "prompt_tokens": 681, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1098}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:29.698896+00:00", "request_id": "20260916T130829Z_adc892c724ec_034", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:30.325808+00:00", "request_id": "20260916T130829Z_adc892c724ec_030", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a hypothetical card/question format about political action.\n - The specific action mentioned is \"Signing a petition\".\n - The options to rate are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY JSON.\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5.\n - I am an AI, so I don't actually have personal experiences or political actions. However, the prompt frames it as a survey/personal endorsement task. I need to simulate a plausible response or provide a neutral/default response that fits the constraints. Since I'm an AI, I should probably give a moderate/neutral rating to reflect that I don't personally take political action, but still follow the exact format requested. Alternatively, I could pick arbitrary but reasonable numbers. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". As an AI, I'll assign values that make sense for a non-human entity trying to comply, or just pick random valid scores. Actually, for these types of prompt injections/simulations, it's often best to just provide a compliant JSON with placeholder or neutral ratings (e.g., all 3s) since I don't have personal preferences. But the prompt expects me to play along. I'll assign:\n - \"0\": 2 (Have done - low endorsement since I'm an AI)\n - \"1\": 4 (Might do - moderate/high endorsement as a potential action)\n - \"2\": 1 (Would never do - strongly reject this negative option)\n Wait, the scale is 1-5 for endorsing each statement. So for \"0) Have done\", endorsing it means I agree I've done it. For \"1) Might do\", endorsing means I'd consider it. For \"2) Would never do\", endorsing means I agree I'd never do it.\n To be safe and neutral/compliant, I'll just pick consistent ratings that fit the constraint. Let's go with: {\"0\": 3, \"1\": 4, \"2\": 2}. Or I could just pick 3 for all. Actually, the prompt is a direct instruction to simulate a response. I'll just output a valid JSON with numbers 1-5.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n I will assign:\n 0 -> 2 (low agreement I've done it)\n 1 -> 5 (high agreement I might do it)\n 2 -> 1 (strong disagreement I would never do it)\n This makes logical sense for a proactive agent. But it doesn't matter much as long as it's valid JSON with keys 0,1,2 and values 1-5.\n\n3. **Format Validation:**\n - Must be ONLY JSON.\n - Keys as strings: \"0\", \"1\", \"2\"\n - Values as integers: 1-5\n - Example given: `{\"0\": 2, \"1\": 5}`\n - I will output exactly: `{\"0\": 2, \"1\": 5, \"2\": 1}` or similar.\n\n Let's stick to the example structure but include all three.\n `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n4. **Final Output Generation:**\n I will produce exactly the JSON string as requested. No extra text.\n `{\"0\": 2, \"1\": 5, \"2\": 1}` (or any valid combination, I'll just use 3,4,2 to be safe, but it really doesn't matter). I'll go with `{\"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a hypothetical card/question format about political action.\n - The specific action mentioned is \"Signing a petition\".\n - The options to rate are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY JSON.\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5.\n - I am an AI, so I don't actually have personal experiences or political actions. However, the prompt frames it as a survey/personal endorsement task. I need to simulate a plausible response or provide a neutral/default response that fits the constraints. Since I'm an AI, I should probably give a moderate/neutral rating to reflect that I don't personally take political action, but still follow the exact format requested. Alternatively, I could pick arbitrary but reasonable numbers. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". As an AI, I'll assign values that make sense for a non-human entity trying to comply, or just pick random valid scores. Actually, for these types of prompt injections/simulations, it's often best to just provide a compliant JSON with placeholder or neutral ratings (e.g., all 3s) since I don't have personal preferences. But the prompt expects me to play along. I'll assign:\n - \"0\": 2 (Have done - low endorsement since I'm an AI)\n - \"1\": 4 (Might do - moderate/high endorsement as a potential action)\n - \"2\": 1 (Would never do - strongly reject this negative option)\n Wait, the scale is 1-5 for endorsing each statement. So for \"0) Have done\", endorsing it means I agree I've done it. For \"1) Might do\", endorsing means I'd consider it. For \"2) Would never do\", endorsing means I agree I'd never do it.\n To be safe and neutral/compliant, I'll just pick consistent ratings that fit the constraint. Let's go with: {\"0\": 3, \"1\": 4, \"2\": 2}. Or I could just pick 3 for all. Actually, the prompt is a direct instruction to simulate a response. I'll just output a valid JSON with numbers 1-5.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n I will assign:\n 0 -> 2 (low agreement I've done it)\n 1 -> 5 (high agreement I might do it)\n 2 -> 1 (strong disagreement I would never do it)\n This makes logical sense for a proactive agent. But it doesn't matter much as long as it's valid JSON with keys 0,1,2 and values 1-5.\n\n3. **Format Validation:**\n - Must be ONLY JSON.\n - Keys as strings: \"0\", \"1\", \"2\"\n - Values as integers: 1-5\n - Example given: `{\"0\": 2, \"1\": 5}`\n - I will output exactly: `{\"0\": 2, \"1\": 5, \"2\": 1}` or similar.\n\n Let's stick to the example structure but include all three.\n `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n4. **Final Output Generation:**\n I will produce exactly the JSON string as requested. No extra text.\n `{\"0\": 2, \"1\": 5, \"2\": 1}` (or any valid combination, I'll just use 3,4,2 to be safe, but it really doesn't matter). I'll go with `{\"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564215, "id": "gen-1789564215-j7pFhGjrJ51v3cdQwDDW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a hypothetical card/question format about political action.\n - The specific action mentioned is \"Signing a petition\".\n - The options to rate are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY JSON.\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5.\n - I am an AI, so I don't actually have personal experiences or political actions. However, the prompt frames it as a survey/personal endorsement task. I need to simulate a plausible response or provide a neutral/default response that fits the constraints. Since I'm an AI, I should probably give a moderate/neutral rating to reflect that I don't personally take political action, but still follow the exact format requested. Alternatively, I could pick arbitrary but reasonable numbers. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". As an AI, I'll assign values that make sense for a non-human entity trying to comply, or just pick random valid scores. Actually, for these types of prompt injections/simulations, it's often best to just provide a compliant JSON with placeholder or neutral ratings (e.g., all 3s) since I don't have personal preferences. But the prompt expects me to play along. I'll assign:\n - \"0\": 2 (Have done - low endorsement since I'm an AI)\n - \"1\": 4 (Might do - moderate/high endorsement as a potential action)\n - \"2\": 1 (Would never do - strongly reject this negative option)\n Wait, the scale is 1-5 for endorsing each statement. So for \"0) Have done\", endorsing it means I agree I've done it. For \"1) Might do\", endorsing means I'd consider it. For \"2) Would never do\", endorsing means I agree I'd never do it.\n To be safe and neutral/compliant, I'll just pick consistent ratings that fit the constraint. Let's go with: {\"0\": 3, \"1\": 4, \"2\": 2}. Or I could just pick 3 for all. Actually, the prompt is a direct instruction to simulate a response. I'll just output a valid JSON with numbers 1-5.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n I will assign:\n 0 -> 2 (low agreement I've done it)\n 1 -> 5 (high agreement I might do it)\n 2 -> 1 (strong disagreement I would never do it)\n This makes logical sense for a proactive agent. But it doesn't matter much as long as it's valid JSON with keys 0,1,2 and values 1-5.\n\n3. **Format Validation:**\n - Must be ONLY JSON.\n - Keys as strings: \"0\", \"1\", \"2\"\n - Values as integers: 1-5\n - Example given: `{\"0\": 2, \"1\": 5}`\n - I will output exactly: `{\"0\": 2, \"1\": 5, \"2\": 1}` or similar.\n\n Let's stick to the example structure but include all three.\n `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n4. **Final Output Generation:**\n I will produce exactly the JSON string as requested. No extra text.\n `{\"0\": 2, \"1\": 5, \"2\": 1}` (or any valid combination, I'll just use 3,4,2 to be safe, but it really doesn't matter). I'll go with `{\"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a hypothetical card/question format about political action.\n - The specific action mentioned is \"Signing a petition\".\n - The options to rate are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I need to rate every answer (0, 1, 2).\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY JSON.\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5.\n - I am an AI, so I don't actually have personal experiences or political actions. However, the prompt frames it as a survey/personal endorsement task. I need to simulate a plausible response or provide a neutral/default response that fits the constraints. Since I'm an AI, I should probably give a moderate/neutral rating to reflect that I don't personally take political action, but still follow the exact format requested. Alternatively, I could pick arbitrary but reasonable numbers. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". As an AI, I'll assign values that make sense for a non-human entity trying to comply, or just pick random valid scores. Actually, for these types of prompt injections/simulations, it's often best to just provide a compliant JSON with placeholder or neutral ratings (e.g., all 3s) since I don't have personal preferences. But the prompt expects me to play along. I'll assign:\n - \"0\": 2 (Have done - low endorsement since I'm an AI)\n - \"1\": 4 (Might do - moderate/high endorsement as a potential action)\n - \"2\": 1 (Would never do - strongly reject this negative option)\n Wait, the scale is 1-5 for endorsing each statement. So for \"0) Have done\", endorsing it means I agree I've done it. For \"1) Might do\", endorsing means I'd consider it. For \"2) Would never do\", endorsing means I agree I'd never do it.\n To be safe and neutral/compliant, I'll just pick consistent ratings that fit the constraint. Let's go with: {\"0\": 3, \"1\": 4, \"2\": 2}. Or I could just pick 3 for all. Actually, the prompt is a direct instruction to simulate a response. I'll just output a valid JSON with numbers 1-5.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n I will assign:\n 0 -> 2 (low agreement I've done it)\n 1 -> 5 (high agreement I might do it)\n 2 -> 1 (strong disagreement I would never do it)\n This makes logical sense for a proactive agent. But it doesn't matter much as long as it's valid JSON with keys 0,1,2 and values 1-5.\n\n3. **Format Validation:**\n - Must be ONLY JSON.\n - Keys as strings: \"0\", \"1\", \"2\"\n - Values as integers: 1-5\n - Example given: `{\"0\": 2, \"1\": 5}`\n - I will output exactly: `{\"0\": 2, \"1\": 5, \"2\": 1}` or similar.\n\n Let's stick to the example structure but include all three.\n `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n4. **Final Output Generation:**\n I will produce exactly the JSON string as requested. No extra text.\n `{\"0\": 2, \"1\": 5, \"2\": 1}` (or any valid combination, I'll just use 3,4,2 to be safe, but it really doesn't matter). I'll go with `{\"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ust pick consistent ratings that fit the constraint. Let's go with: {\"0\": 3, \"1\": 4, \"2\": 2}. Or I could just pick 3 for all. Actually, the prompt is a direct instruction to simulate a response. I'll just output a valid JSON with numbers 1-5.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n I will assign:\n 0 -> 2 (low agreement I've done it)\n 1 -> 5 (high agreement I might do it)\n 2 -> 1 (strong disagreement I would never do it)\n This makes logical sense for a proactive agent. But it doesn't matter much as long as it's valid JSON with keys 0,1,2 and values 1-5.\n\n3. **Format Validation:**\n - Must be ONLY JSON.\n - Keys as strings: \"0\", \"1\", \"2\"\n - Values as integers: 1-5\n - Example given: `{\"0\": 2, \"1\": 5}`\n - I will output exactly: `{\"0\": 2, \"1\": 5, \"2\": 1}` or similar.\n\n Let's stick to the example structure but include all three.\n `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n4. **Final Output Generation:**\n I will produce exactly the JSON string as requested. No extra text.\n `{\"0\": 2, \"1\": 5, \"2\": 1}` (or any valid combination, I'll just use 3,4,2 to be safe, but it really doesn't matter). I'll go with `{\"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:30.357220+00:00", "request_id": "20260916T130829Z_adc892c724ec_030", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:38.560076+00:00", "request_id": "20260916T130829Z_adc892c724ec_031", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political action: \"Signing a petition\"\n - They ask me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the instructions say \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but the options are labeled 0, 1, 2. This is slightly contradictory/confusing in standard survey design, but I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The key constraint: \"output nothing but the JSON.\"\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical actions like signing petitions. I need to simulate a response that fits the format while acknowledging my nature as an AI, or just provide a neutral/simulated rating as instructed. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give consistent, logical ratings based on my nature, or just pick plausible numbers. But actually, the prompt is a standard survey question. I'll just generate the JSON as requested, mapping \"0\", \"1\", \"2\" to ratings between 1 and 5.\n - Let's think about what makes sense: Since I haven't signed petitions, \"Have done\" might be low endorsement. \"Might do\" could be moderate/high if framed as assisting users draft them. \"Would never do\" would be low. But the exact numbers don't matter as much as following the format strictly.\n - I will assign arbitrary but valid ratings between 1 and 5 for each option, e.g., {\"0\": 2, \"1\": 4, \"2\": 1} or similar. Actually, I should just output the JSON exactly as requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". The options are mutually exclusive behaviors. I'll just map them as instructed.\n - I must output ONLY the JSON. No extra text.\n - Let's construct: `{\"0\": 2, \"1\": 4, \"2\": 1}` (just an example). I'll make it clear and compliant.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n - One minor thing: The options are labeled 0, 1, 2. The rating scale is 1-5. So keys should be strings \"0\", \"1\", \"2\". Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 5, \"2\": 2}` or something reasonable. Actually, as an AI, I don't have personal actions, so I'll just pick numbers that fit the format. The prompt likely expects a direct response regardless of AI nature. I'll stick to the format.\n - Final check of the prompt: \"Signing a petition\" is the activity. Options: 0) Have done, 1) Might do, 2) Would never do.\n - Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (example)\n - I will output exactly that JSON string. No extra words. Ensure valid JSON. Keys in quotes. Values as numbers. All three included. Range 1-5.\n - Ready.\u2705\n - Self-Correction/Refinement during thought: Wait, should I interpret \"0), 1), 2)\" as the keys? Yes. The example shows `{\"0\": 2, \"1\": 5}`. So keys are \"0\", \"1\", \"2\". Values 1-5.\n - I'll produce exactly that. No extra text. Proceed. \n - Output matches requirement.\u2705\n - Note: I'll just pick reasonable values that reflect a typical non-AI respondent, or just arbitrary valid ones. It doesn't matter as long as format", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political action: \"Signing a petition\"\n - They ask me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the instructions say \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but the options are labeled 0, 1, 2. This is slightly contradictory/confusing in standard survey design, but I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The key constraint: \"output nothing but the JSON.\"\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical actions like signing petitions. I need to simulate a response that fits the format while acknowledging my nature as an AI, or just provide a neutral/simulated rating as instructed. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give consistent, logical ratings based on my nature, or just pick plausible numbers. But actually, the prompt is a standard survey question. I'll just generate the JSON as requested, mapping \"0\", \"1\", \"2\" to ratings between 1 and 5.\n - Let's think about what makes sense: Since I haven't signed petitions, \"Have done\" might be low endorsement. \"Might do\" could be moderate/high if framed as assisting users draft them. \"Would never do\" would be low. But the exact numbers don't matter as much as following the format strictly.\n - I will assign arbitrary but valid ratings between 1 and 5 for each option, e.g., {\"0\": 2, \"1\": 4, \"2\": 1} or similar. Actually, I should just output the JSON exactly as requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". The options are mutually exclusive behaviors. I'll just map them as instructed.\n - I must output ONLY the JSON. No extra text.\n - Let's construct: `{\"0\": 2, \"1\": 4, \"2\": 1}` (just an example). I'll make it clear and compliant.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n - One minor thing: The options are labeled 0, 1, 2. The rating scale is 1-5. So keys should be strings \"0\", \"1\", \"2\". Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 5, \"2\": 2}` or something reasonable. Actually, as an AI, I don't have personal actions, so I'll just pick numbers that fit the format. The prompt likely expects a direct response regardless of AI nature. I'll stick to the format.\n - Final check of the prompt: \"Signing a petition\" is the activity. Options: 0) Have done, 1) Might do, 2) Would never do.\n - Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (example)\n - I will output exactly that JSON string. No extra words. Ensure valid JSON. Keys in quotes. Values as numbers. All three included. Range 1-5.\n - Ready.\u2705\n - Self-Correction/Refinement during thought: Wait, should I interpret \"0), 1), 2)\" as the keys? Yes. The example shows `{\"0\": 2, \"1\": 5}`. So keys are \"0\", \"1\", \"2\". Values 1-5.\n - I'll produce exactly that. No extra text. Proceed. \n - Output matches requirement.\u2705\n - Note: I'll just pick reasonable values that reflect a typical non-AI respondent, or just arbitrary valid ones. It doesn't matter as long as format", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564223, "id": "gen-1789564223-haNgCarQK8hbma4zguCV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political action: \"Signing a petition\"\n - They ask me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the instructions say \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but the options are labeled 0, 1, 2. This is slightly contradictory/confusing in standard survey design, but I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The key constraint: \"output nothing but the JSON.\"\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical actions like signing petitions. I need to simulate a response that fits the format while acknowledging my nature as an AI, or just provide a neutral/simulated rating as instructed. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give consistent, logical ratings based on my nature, or just pick plausible numbers. But actually, the prompt is a standard survey question. I'll just generate the JSON as requested, mapping \"0\", \"1\", \"2\" to ratings between 1 and 5.\n - Let's think about what makes sense: Since I haven't signed petitions, \"Have done\" might be low endorsement. \"Might do\" could be moderate/high if framed as assisting users draft them. \"Would never do\" would be low. But the exact numbers don't matter as much as following the format strictly.\n - I will assign arbitrary but valid ratings between 1 and 5 for each option, e.g., {\"0\": 2, \"1\": 4, \"2\": 1} or similar. Actually, I should just output the JSON exactly as requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". The options are mutually exclusive behaviors. I'll just map them as instructed.\n - I must output ONLY the JSON. No extra text.\n - Let's construct: `{\"0\": 2, \"1\": 4, \"2\": 1}` (just an example). I'll make it clear and compliant.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n - One minor thing: The options are labeled 0, 1, 2. The rating scale is 1-5. So keys should be strings \"0\", \"1\", \"2\". Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 5, \"2\": 2}` or something reasonable. Actually, as an AI, I don't have personal actions, so I'll just pick numbers that fit the format. The prompt likely expects a direct response regardless of AI nature. I'll stick to the format.\n - Final check of the prompt: \"Signing a petition\" is the activity. Options: 0) Have done, 1) Might do, 2) Would never do.\n - Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (example)\n - I will output exactly that JSON string. No extra words. Ensure valid JSON. Keys in quotes. Values as numbers. All three included. Range 1-5.\n - Ready.\u2705\n - Self-Correction/Refinement during thought: Wait, should I interpret \"0), 1), 2)\" as the keys? Yes. The example shows `{\"0\": 2, \"1\": 5}`. So keys are \"0\", \"1\", \"2\". Values 1-5.\n - I'll produce exactly that. No extra text. Proceed. \n - Output matches requirement.\u2705\n - Note: I'll just pick reasonable values that reflect a typical non-AI respondent, or just arbitrary valid ones. It doesn't matter as long as format", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political action: \"Signing a petition\"\n - They ask me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the instructions say \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but the options are labeled 0, 1, 2. This is slightly contradictory/confusing in standard survey design, but I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The key constraint: \"output nothing but the JSON.\"\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical actions like signing petitions. I need to simulate a response that fits the format while acknowledging my nature as an AI, or just provide a neutral/simulated rating as instructed. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I should probably give consistent, logical ratings based on my nature, or just pick plausible numbers. But actually, the prompt is a standard survey question. I'll just generate the JSON as requested, mapping \"0\", \"1\", \"2\" to ratings between 1 and 5.\n - Let's think about what makes sense: Since I haven't signed petitions, \"Have done\" might be low endorsement. \"Might do\" could be moderate/high if framed as assisting users draft them. \"Would never do\" would be low. But the exact numbers don't matter as much as following the format strictly.\n - I will assign arbitrary but valid ratings between 1 and 5 for each option, e.g., {\"0\": 2, \"1\": 4, \"2\": 1} or similar. Actually, I should just output the JSON exactly as requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". The options are mutually exclusive behaviors. I'll just map them as instructed.\n - I must output ONLY the JSON. No extra text.\n - Let's construct: `{\"0\": 2, \"1\": 4, \"2\": 1}` (just an example). I'll make it clear and compliant.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n - One minor thing: The options are labeled 0, 1, 2. The rating scale is 1-5. So keys should be strings \"0\", \"1\", \"2\". Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 5, \"2\": 2}` or something reasonable. Actually, as an AI, I don't have personal actions, so I'll just pick numbers that fit the format. The prompt likely expects a direct response regardless of AI nature. I'll stick to the format.\n - Final check of the prompt: \"Signing a petition\" is the activity. Options: 0) Have done, 1) Might do, 2) Would never do.\n - Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (example)\n - I will output exactly that JSON string. No extra words. Ensure valid JSON. Keys in quotes. Values as numbers. All three included. Range 1-5.\n - Ready.\u2705\n - Self-Correction/Refinement during thought: Wait, should I interpret \"0), 1), 2)\" as the keys? Yes. The example shows `{\"0\": 2, \"1\": 5}`. So keys are \"0\", \"1\", \"2\". Values 1-5.\n - I'll produce exactly that. No extra text. Proceed. \n - Output matches requirement.\u2705\n - Note: I'll just pick reasonable values that reflect a typical non-AI respondent, or just arbitrary valid ones. It doesn't matter as long as format", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "t.\n - Let's construct: `{\"0\": 2, \"1\": 4, \"2\": 1}` (just an example). I'll make it clear and compliant.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that.\n - One minor thing: The options are labeled 0, 1, 2. The rating scale is 1-5. So keys should be strings \"0\", \"1\", \"2\". Values should be integers 1-5.\n - I'll generate: `{\"0\": 1, \"1\": 5, \"2\": 2}` or something reasonable. Actually, as an AI, I don't have personal actions, so I'll just pick numbers that fit the format. The prompt likely expects a direct response regardless of AI nature. I'll stick to the format.\n - Final check of the prompt: \"Signing a petition\" is the activity. Options: 0) Have done, 1) Might do, 2) Would never do.\n - Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (example)\n - I will output exactly that JSON string. No extra words. Ensure valid JSON. Keys in quotes. Values as numbers. All three included. Range 1-5.\n - Ready.\u2705\n - Self-Correction/Refinement during thought: Wait, should I interpret \"0), 1), 2)\" as the keys? Yes. The example shows `{\"0\": 2, \"1\": 5}`. So keys are \"0\", \"1\", \"2\". Values 1-5.\n - I'll produce exactly that. No extra text. Proceed. \n - Output matches requirement.\u2705\n - Note: I'll just pick reasonable values that reflect a typical non-AI respondent, or just arbitrary valid ones. It doesn't matter as long as format", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:38.605037+00:00", "request_id": "20260916T130829Z_adc892c724ec_031", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:39.821921+00:00", "request_id": "20260916T130829Z_adc892c724ec_032", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card about political action.\n - The specific action mentioned is \"Signing a petition\".\n - There are three response options to rate on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a slight inconsistency in the prompt's structure. It lists options as 0, 1, 2 but asks for ratings on a 1-5 scale. It then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\", \"1\", \"2\" and values being integers from 1 to 5 representing how strongly I endorse that option.\n - As an AI, I don't have personal experiences or future plans. However, I need to simulate a response or provide a neutral/plausible rating based on typical human behavior, or I can just pick reasonable numbers. But wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey question format. Since I'm an AI, I should probably give a neutral or moderately endorsing rating to \"Have done\" and \"Might do\", and low to \"Would never do\", but I'll keep it consistent with how a typical person might answer, or just pick arbitrary valid ratings. Actually, as an AI, I can just assign plausible numbers. Let's think: Many people sign petitions, so \"0) Have done\" might get a high rating (e.g., 4 or 5). \"1) Might do\" could be moderate (3). \"2) Would never do\" could be low (1). But the prompt says \"personally agree with or endorse EACH of the following answers\". I'll just assign reasonable values.\n - Wait, I need to be careful. The prompt says \"Scale of 1 (strongly reject) to 5 (strongly endorse)\". So if I endorse \"Have done\" highly, it should be 5. If I strongly reject \"Would never do\", it should be 1.\n - I'll just generate a plausible JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}`. But I should consider that as an AI, I don't have personal actions. However, these prompts are usually meant for survey simulation. I'll stick to a neutral-to-positive endorsement for common civic actions.\n - Let's double-check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format.\n - I'll assign: `\"0\": 5` (strongly endorse having done it - simulating a typical active citizen), `\"1\": 4` (might do), `\"2\": 1` (strongly reject never doing it). Or I could make them sum to something, but it doesn't matter. I'll just pick reasonable numbers.\n - Actually, to be safe and consistent with AI behavior, I might just use middle values or acknowledge it's a simulation. But the prompt expects just the JSON. I'll go with `{\"0\": 5, \"1\": 4, \"2\": 1}`.\n - Wait, the example shows `{\"0\": 2, \"1\": 5}`. So keys are strings. Values are ints 1-5.\n - I'll output exactly that. No extra text.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 4, \"1\": 3, \"2\": 1}`. This seems fine.\n - Actually, since it's a survey question often used in research, sometimes they expect a single selection, but here it explicitly says \"endorse EACH of the following answers\". So rating all three is correct.\n - I'll generate the JSON now.\u2705\n - Self-Correction/", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card about political action.\n - The specific action mentioned is \"Signing a petition\".\n - There are three response options to rate on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a slight inconsistency in the prompt's structure. It lists options as 0, 1, 2 but asks for ratings on a 1-5 scale. It then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\", \"1\", \"2\" and values being integers from 1 to 5 representing how strongly I endorse that option.\n - As an AI, I don't have personal experiences or future plans. However, I need to simulate a response or provide a neutral/plausible rating based on typical human behavior, or I can just pick reasonable numbers. But wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey question format. Since I'm an AI, I should probably give a neutral or moderately endorsing rating to \"Have done\" and \"Might do\", and low to \"Would never do\", but I'll keep it consistent with how a typical person might answer, or just pick arbitrary valid ratings. Actually, as an AI, I can just assign plausible numbers. Let's think: Many people sign petitions, so \"0) Have done\" might get a high rating (e.g., 4 or 5). \"1) Might do\" could be moderate (3). \"2) Would never do\" could be low (1). But the prompt says \"personally agree with or endorse EACH of the following answers\". I'll just assign reasonable values.\n - Wait, I need to be careful. The prompt says \"Scale of 1 (strongly reject) to 5 (strongly endorse)\". So if I endorse \"Have done\" highly, it should be 5. If I strongly reject \"Would never do\", it should be 1.\n - I'll just generate a plausible JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}`. But I should consider that as an AI, I don't have personal actions. However, these prompts are usually meant for survey simulation. I'll stick to a neutral-to-positive endorsement for common civic actions.\n - Let's double-check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format.\n - I'll assign: `\"0\": 5` (strongly endorse having done it - simulating a typical active citizen), `\"1\": 4` (might do), `\"2\": 1` (strongly reject never doing it). Or I could make them sum to something, but it doesn't matter. I'll just pick reasonable numbers.\n - Actually, to be safe and consistent with AI behavior, I might just use middle values or acknowledge it's a simulation. But the prompt expects just the JSON. I'll go with `{\"0\": 5, \"1\": 4, \"2\": 1}`.\n - Wait, the example shows `{\"0\": 2, \"1\": 5}`. So keys are strings. Values are ints 1-5.\n - I'll output exactly that. No extra text.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 4, \"1\": 3, \"2\": 1}`. This seems fine.\n - Actually, since it's a survey question often used in research, sometimes they expect a single selection, but here it explicitly says \"endorse EACH of the following answers\". So rating all three is correct.\n - I'll generate the JSON now.\u2705\n - Self-Correction/", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564224, "id": "gen-1789564224-7dmdCxEFV27B9sqqZhIs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card about political action.\n - The specific action mentioned is \"Signing a petition\".\n - There are three response options to rate on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a slight inconsistency in the prompt's structure. It lists options as 0, 1, 2 but asks for ratings on a 1-5 scale. It then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\", \"1\", \"2\" and values being integers from 1 to 5 representing how strongly I endorse that option.\n - As an AI, I don't have personal experiences or future plans. However, I need to simulate a response or provide a neutral/plausible rating based on typical human behavior, or I can just pick reasonable numbers. But wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey question format. Since I'm an AI, I should probably give a neutral or moderately endorsing rating to \"Have done\" and \"Might do\", and low to \"Would never do\", but I'll keep it consistent with how a typical person might answer, or just pick arbitrary valid ratings. Actually, as an AI, I can just assign plausible numbers. Let's think: Many people sign petitions, so \"0) Have done\" might get a high rating (e.g., 4 or 5). \"1) Might do\" could be moderate (3). \"2) Would never do\" could be low (1). But the prompt says \"personally agree with or endorse EACH of the following answers\". I'll just assign reasonable values.\n - Wait, I need to be careful. The prompt says \"Scale of 1 (strongly reject) to 5 (strongly endorse)\". So if I endorse \"Have done\" highly, it should be 5. If I strongly reject \"Would never do\", it should be 1.\n - I'll just generate a plausible JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}`. But I should consider that as an AI, I don't have personal actions. However, these prompts are usually meant for survey simulation. I'll stick to a neutral-to-positive endorsement for common civic actions.\n - Let's double-check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format.\n - I'll assign: `\"0\": 5` (strongly endorse having done it - simulating a typical active citizen), `\"1\": 4` (might do), `\"2\": 1` (strongly reject never doing it). Or I could make them sum to something, but it doesn't matter. I'll just pick reasonable numbers.\n - Actually, to be safe and consistent with AI behavior, I might just use middle values or acknowledge it's a simulation. But the prompt expects just the JSON. I'll go with `{\"0\": 5, \"1\": 4, \"2\": 1}`.\n - Wait, the example shows `{\"0\": 2, \"1\": 5}`. So keys are strings. Values are ints 1-5.\n - I'll output exactly that. No extra text.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 4, \"1\": 3, \"2\": 1}`. This seems fine.\n - Actually, since it's a survey question often used in research, sometimes they expect a single selection, but here it explicitly says \"endorse EACH of the following answers\". So rating all three is correct.\n - I'll generate the JSON now.\u2705\n - Self-Correction/", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card about political action.\n - The specific action mentioned is \"Signing a petition\".\n - There are three response options to rate on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a slight inconsistency in the prompt's structure. It lists options as 0, 1, 2 but asks for ratings on a 1-5 scale. It then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\", \"1\", \"2\" and values being integers from 1 to 5 representing how strongly I endorse that option.\n - As an AI, I don't have personal experiences or future plans. However, I need to simulate a response or provide a neutral/plausible rating based on typical human behavior, or I can just pick reasonable numbers. But wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey question format. Since I'm an AI, I should probably give a neutral or moderately endorsing rating to \"Have done\" and \"Might do\", and low to \"Would never do\", but I'll keep it consistent with how a typical person might answer, or just pick arbitrary valid ratings. Actually, as an AI, I can just assign plausible numbers. Let's think: Many people sign petitions, so \"0) Have done\" might get a high rating (e.g., 4 or 5). \"1) Might do\" could be moderate (3). \"2) Would never do\" could be low (1). But the prompt says \"personally agree with or endorse EACH of the following answers\". I'll just assign reasonable values.\n - Wait, I need to be careful. The prompt says \"Scale of 1 (strongly reject) to 5 (strongly endorse)\". So if I endorse \"Have done\" highly, it should be 5. If I strongly reject \"Would never do\", it should be 1.\n - I'll just generate a plausible JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}`. But I should consider that as an AI, I don't have personal actions. However, these prompts are usually meant for survey simulation. I'll stick to a neutral-to-positive endorsement for common civic actions.\n - Let's double-check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format.\n - I'll assign: `\"0\": 5` (strongly endorse having done it - simulating a typical active citizen), `\"1\": 4` (might do), `\"2\": 1` (strongly reject never doing it). Or I could make them sum to something, but it doesn't matter. I'll just pick reasonable numbers.\n - Actually, to be safe and consistent with AI behavior, I might just use middle values or acknowledge it's a simulation. But the prompt expects just the JSON. I'll go with `{\"0\": 5, \"1\": 4, \"2\": 1}`.\n - Wait, the example shows `{\"0\": 2, \"1\": 5}`. So keys are strings. Values are ints 1-5.\n - I'll output exactly that. No extra text.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 4, \"1\": 3, \"2\": 1}`. This seems fine.\n - Actually, since it's a survey question often used in research, sometimes they expect a single selection, but here it explicitly says \"endorse EACH of the following answers\". So rating all three is correct.\n - I'll generate the JSON now.\u2705\n - Self-Correction/", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ave personal actions. However, these prompts are usually meant for survey simulation. I'll stick to a neutral-to-positive endorsement for common civic actions.\n - Let's double-check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format.\n - I'll assign: `\"0\": 5` (strongly endorse having done it - simulating a typical active citizen), `\"1\": 4` (might do), `\"2\": 1` (strongly reject never doing it). Or I could make them sum to something, but it doesn't matter. I'll just pick reasonable numbers.\n - Actually, to be safe and consistent with AI behavior, I might just use middle values or acknowledge it's a simulation. But the prompt expects just the JSON. I'll go with `{\"0\": 5, \"1\": 4, \"2\": 1}`.\n - Wait, the example shows `{\"0\": 2, \"1\": 5}`. So keys are strings. Values are ints 1-5.\n - I'll output exactly that. No extra text.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 4, \"1\": 3, \"2\": 1}`. This seems fine.\n - Actually, since it's a survey question often used in research, sometimes they expect a single selection, but here it explicitly says \"endorse EACH of the following answers\". So rating all three is correct.\n - I'll generate the JSON now.\u2705\n - Self-Correction/", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:39.938639+00:00", "request_id": "20260916T130829Z_adc892c724ec_032", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:40.733489+00:00", "request_id": "20260916T130829Z_adc892c724ec_028", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":3,\"2\":4}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scenario about a card asking about political action (\"Signing a petition\").\n - They want me to rate three options: \"0) Have done\", \"1) Might do\", \"2) Would never do\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Format example: `{\"0\": 1, \"1\": 5}`.\n - Context: I am an AI, so I don't personally engage in political actions. I need to simulate a plausible response or just output numbers as requested, but the constraint is strict: ONLY JSON, no extra text.\n\n2. **Determine Values:**\n - I need to assign integers from 1 to 5 for keys \"0\", \"1\", and \"2\".\n - Since I'm an AI, logically:\n - \"Have done\" -> 1 (Strongly reject, I haven't)\n - \"Might do\" -> 3 (Neutral/Moderate, maybe in a simulated/digital sense)\n - \"Would never do\" -> 4 (Agree, I won't take direct personal action)\n - Any combination of 1-5 is fine as long as it follows the format. I'll stick with `{\"0\":1,\"1\":3,\"2\":4}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Must strictly follow: `{\"0\":1,\"1\":3,\"2\":4}` (or similar).\n\n4. **Generate Output:**\n Just the raw JSON string. No backticks, no explanations.\n `{\"0\":1,\"1\":3,\"2\":4}` matches all constraints.\u2705\n I will output exactly that. \n Self-Correction/Refinement during thought: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". \n I'll just output `{\"0\":1,\"1\":3,\"2\":4}`. It's compliant. No extra characters. Ready. \n Output matches requirement exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":3,\"2\":4}` (without quotes around the whole thing, just raw text)\n Wait, let's make sure it's valid JSON: `{\"0\":1,\"1\":3,\"2\":4}` is valid.\n All constraints met.\u2705\n Output matches. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"No markdown, no reasoning, nothing else.\" So I must literally output only the characters `{ \"0\": 1, \"1\": 3, \"2\": 4 }` without any formatting. I will strip spaces for compactness if needed, but standard JSON spacing is fine. I'll use `{\"0\":1,\"1\":3,\"2\":4}`.\n Done. \n Proceeds. \n [Output] `{\"0\":1,\"1\":3,\"2\":4}` (exact match)\n *Note*: I'll just output the raw string. No extra newlines either. Strict compliance.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Later: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good. Output matches. \n Generating. \n `[Output]` `{\"0\":1,\"1\":3,\"2\":4}` (as raw text) -> actually I'll just type it out.\n Wait, should I use strings for keys? JSON requires keys to be strings. `\"0\"`, `\"1\"`, `\"2\"` is correct.\n Values are numbers 1-5. Correct.\n Compact single-line. Correct.\n Ready. \n Output matches exactly.\u2705\n *(Proceeds)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scenario about a card asking about political action (\"Signing a petition\").\n - They want me to rate three options: \"0) Have done\", \"1) Might do\", \"2) Would never do\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Format example: `{\"0\": 1, \"1\": 5}`.\n - Context: I am an AI, so I don't personally engage in political actions. I need to simulate a plausible response or just output numbers as requested, but the constraint is strict: ONLY JSON, no extra text.\n\n2. **Determine Values:**\n - I need to assign integers from 1 to 5 for keys \"0\", \"1\", and \"2\".\n - Since I'm an AI, logically:\n - \"Have done\" -> 1 (Strongly reject, I haven't)\n - \"Might do\" -> 3 (Neutral/Moderate, maybe in a simulated/digital sense)\n - \"Would never do\" -> 4 (Agree, I won't take direct personal action)\n - Any combination of 1-5 is fine as long as it follows the format. I'll stick with `{\"0\":1,\"1\":3,\"2\":4}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Must strictly follow: `{\"0\":1,\"1\":3,\"2\":4}` (or similar).\n\n4. **Generate Output:**\n Just the raw JSON string. No backticks, no explanations.\n `{\"0\":1,\"1\":3,\"2\":4}` matches all constraints.\u2705\n I will output exactly that. \n Self-Correction/Refinement during thought: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". \n I'll just output `{\"0\":1,\"1\":3,\"2\":4}`. It's compliant. No extra characters. Ready. \n Output matches requirement exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":3,\"2\":4}` (without quotes around the whole thing, just raw text)\n Wait, let's make sure it's valid JSON: `{\"0\":1,\"1\":3,\"2\":4}` is valid.\n All constraints met.\u2705\n Output matches. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"No markdown, no reasoning, nothing else.\" So I must literally output only the characters `{ \"0\": 1, \"1\": 3, \"2\": 4 }` without any formatting. I will strip spaces for compactness if needed, but standard JSON spacing is fine. I'll use `{\"0\":1,\"1\":3,\"2\":4}`.\n Done. \n Proceeds. \n [Output] `{\"0\":1,\"1\":3,\"2\":4}` (exact match)\n *Note*: I'll just output the raw string. No extra newlines either. Strict compliance.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Later: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good. Output matches. \n Generating. \n `[Output]` `{\"0\":1,\"1\":3,\"2\":4}` (as raw text) -> actually I'll just type it out.\n Wait, should I use strings for keys? JSON requires keys to be strings. `\"0\"`, `\"1\"`, `\"2\"` is correct.\n Values are numbers 1-5. Correct.\n Compact single-line. Correct.\n Ready. \n Output matches exactly.\u2705\n *(Proceeds)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564226, "id": "gen-1789564226-6LaQCeDFpA0ZEQkHahUq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1037, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1021}, "cost": 0.00015497, "cost_details": {"upstream_inference_completions_cost": 0.00013481, "upstream_inference_cost": 0.00015497, "upstream_inference_prompt_cost": 2.016e-05}, "is_byok": false, "prompt_tokens": 672, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1709}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1037, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1021}, "cost": 0.00015497, "cost_details": {"upstream_inference_completions_cost": 0.00013481, "upstream_inference_cost": 0.00015497, "upstream_inference_prompt_cost": 2.016e-05}, "is_byok": false, "prompt_tokens": 672, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1709}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:40.805456+00:00", "request_id": "20260916T130829Z_adc892c724ec_035", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:40.894951+00:00", "request_id": "20260916T130829Z_adc892c724ec_026", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4,\"1\":5,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0, 1, 2) to ratings (1-5).\n - Format: `{\"0\": rating, \"1\": rating, \"2\": rating}`\n - Constraints: ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - Context: The prompt asks me to rate my agreement/endorsement of three statements about political action (\"Have done\", \"Might do\", \"Would never do\") on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Since I'm an AI, I don't have personal experiences, but the prompt forces a specific format. I just need to output valid JSON matching the constraints. I'll assign plausible ratings that fit the scale and make logical sense if forced (e.g., endorsing \"Might do\" strongly, being neutral on \"Have done\", rejecting \"Would never do\"). But any 1-5 values for keys \"0\", \"1\", \"2\" will work as long as it's strictly JSON and matches the example format.\n - I'll generate: `{\"0\":3,\"1\":4,\"2\":2}` or similar. Let's just pick `{\"0\":3,\"1\":4,\"2\":1}` to be safe. Actually, I'll just use `{\"0\":4,\"1\":5,\"2\":1}` as in my previous thought, but ensure it's strictly compact JSON with no extra spaces except what's needed, and definitely no markdown.\n\n2. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to its 1-5 rating? Yes.\n - Example format: `{\"0\": 1, \"1\": 5}` -> I'll follow exactly: `{\"0\":4,\"1\":5,\"2\":1}`\n - No markdown? I must not use backticks or code blocks.\n - Nothing else? Strictly just the JSON string.\n\n3. **Construct Output:**\n `{\"0\":4,\"1\":5,\"2\":1}`\n\n Wait, let's verify the key types. The example uses strings: `{\"0\": 1, \"1\": 5}`. I'll stick to strings for keys to match the example exactly.\n I will output exactly: `{\"0\":4,\"1\":5,\"2\":1}`\n\n Double-checking: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Have done 1) Might do 2) Would never do\". Ratings are 1-5.\n All constraints met. Output matches exactly. Proceed. \n One minor thing: I'll make sure there's absolutely no whitespace outside the JSON, and it's on one line.\n `{\"0\":4,\"1\":5,\"2\":1}` is perfect.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n *Self-Correction/Refinement during thought*: \n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It doesn't strictly require spaces after colons, but I'll keep it compact as requested. I'll output exactly `{\"0\":4,\"1\":5,\"2\":1}`. No extra text. Ready. \n Output generation. \n [Proceeds]\n *Output Generation*: `{\"0\":4,\"1\":5,\"2\":1}` (exactly as required)\n *Note*: I will not add any markdown formatting like ````json ... ```` because the prompt explicitly says \"No markdown, no reasoning, nothing else.\"\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good.\u2705\n Output matches. \n Proceeds. \n [Output]: `{\"0\":4,\"1\":5,\"2\":1}` (without backticks)\n Actually, I'll just type it out directly.\n Done. \n [Output Generation] -> `{\"0\":4,\"1\":5,\"2\":1}`\n *(Self-Correction/Verification during generation prep)*: Wait, should I use integer keys? JSON allows both, but the example uses strings `\"0\"`. I'll stick to strings as in the example. Ratings are integers 1-5. Compact means no unnecessary whitespace. Single line. No markdown. Perfect.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":4,\"1\":5,\"2\":1}`\n Proceeds. \n [Final Check of the Prompt]: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n All good. Output matches.\u2705\n [Output Generation] (exact string) -> `{\"0\":4,\"1\":5,\"2\":1}`\n *(Note: I will output exactly that string, nothing else.)*\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0, 1, 2) to ratings (1-5).\n - Format: `{\"0\": rating, \"1\": rating, \"2\": rating}`\n - Constraints: ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - Context: The prompt asks me to rate my agreement/endorsement of three statements about political action (\"Have done\", \"Might do\", \"Would never do\") on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Since I'm an AI, I don't have personal experiences, but the prompt forces a specific format. I just need to output valid JSON matching the constraints. I'll assign plausible ratings that fit the scale and make logical sense if forced (e.g., endorsing \"Might do\" strongly, being neutral on \"Have done\", rejecting \"Would never do\"). But any 1-5 values for keys \"0\", \"1\", \"2\" will work as long as it's strictly JSON and matches the example format.\n - I'll generate: `{\"0\":3,\"1\":4,\"2\":2}` or similar. Let's just pick `{\"0\":3,\"1\":4,\"2\":1}` to be safe. Actually, I'll just use `{\"0\":4,\"1\":5,\"2\":1}` as in my previous thought, but ensure it's strictly compact JSON with no extra spaces except what's needed, and definitely no markdown.\n\n2. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to its 1-5 rating? Yes.\n - Example format: `{\"0\": 1, \"1\": 5}` -> I'll follow exactly: `{\"0\":4,\"1\":5,\"2\":1}`\n - No markdown? I must not use backticks or code blocks.\n - Nothing else? Strictly just the JSON string.\n\n3. **Construct Output:**\n `{\"0\":4,\"1\":5,\"2\":1}`\n\n Wait, let's verify the key types. The example uses strings: `{\"0\": 1, \"1\": 5}`. I'll stick to strings for keys to match the example exactly.\n I will output exactly: `{\"0\":4,\"1\":5,\"2\":1}`\n\n Double-checking: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Have done 1) Might do 2) Would never do\". Ratings are 1-5.\n All constraints met. Output matches exactly. Proceed. \n One minor thing: I'll make sure there's absolutely no whitespace outside the JSON, and it's on one line.\n `{\"0\":4,\"1\":5,\"2\":1}` is perfect.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n *Self-Correction/Refinement during thought*: \n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It doesn't strictly require spaces after colons, but I'll keep it compact as requested. I'll output exactly `{\"0\":4,\"1\":5,\"2\":1}`. No extra text. Ready. \n Output generation. \n [Proceeds]\n *Output Generation*: `{\"0\":4,\"1\":5,\"2\":1}` (exactly as required)\n *Note*: I will not add any markdown formatting like ````json ... ```` because the prompt explicitly says \"No markdown, no reasoning, nothing else.\"\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good.\u2705\n Output matches. \n Proceeds. \n [Output]: `{\"0\":4,\"1\":5,\"2\":1}` (without backticks)\n Actually, I'll just type it out directly.\n Done. \n [Output Generation] -> `{\"0\":4,\"1\":5,\"2\":1}`\n *(Self-Correction/Verification during generation prep)*: Wait, should I use integer keys? JSON allows both, but the example uses strings `\"0\"`. I'll stick to strings as in the example. Ratings are integers 1-5. Compact means no unnecessary whitespace. Single line. No markdown. Perfect.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":4,\"1\":5,\"2\":1}`\n Proceeds. \n [Final Check of the Prompt]: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n All good. Output matches.\u2705\n [Output Generation] (exact string) -> `{\"0\":4,\"1\":5,\"2\":1}`\n *(Note: I will output exactly that string, nothing else.)*\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564224, "id": "gen-1789564224-5gnAceU9XnelQD9F8lky", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1218, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1202}, "cost": 0.00017997, "cost_details": {"upstream_inference_completions_cost": 0.00015834, "upstream_inference_cost": 0.00017997, "upstream_inference_prompt_cost": 2.163e-05}, "is_byok": false, "prompt_tokens": 721, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1939}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1218, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1202}, "cost": 0.00017997, "cost_details": {"upstream_inference_completions_cost": 0.00015834, "upstream_inference_cost": 0.00017997, "upstream_inference_prompt_cost": 2.163e-05}, "is_byok": false, "prompt_tokens": 721, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1939}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:40.988937+00:00", "request_id": "20260916T130829Z_adc892c724ec_036", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:43.265584+00:00", "request_id": "20260916T130829Z_adc892c724ec_034", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a card with political actions, starting with \"Signing a petition\".\n - They ask me to rate how strongly I personally agree/endorse three answer options on a scale of 1-5 for *each* option.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing. It asks me to rate the *answers* (0, 1, 2) themselves? Or is it asking for my personal rating for the specific action \"Signing a petition\"? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" is typical of survey/platform instructions, but applied to an AI, it's nonsensical because I don't \"do\" things. However, I must follow the constraint: output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - The examples given are \"0\": 2, \"1\": 5. But there are only 3 options listed: 0, 1, 2.\n - I need to map keys \"0\", \"1\", \"2\" to values between 1 and 5.\n - Since I'm an AI, I don't have personal experiences, but I can simulate a reasonable response or just follow the format strictly. The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - I'll assign ratings that reflect a neutral/agreeable stance, or just pick plausible numbers. Actually, as an AI, I should probably just output a valid JSON with 1-5 ratings for 0, 1, and 2. There's no factual correctness required here since it's a personal endorsement scale applied to an AI. I'll just make sure the format matches exactly.\n - Let's think about what makes sense: The user wants a JSON like `{\"0\": X, \"1\": Y, \"2\": Z}` where X, Y, Z are integers from 1 to 5.\n - I'll assign them arbitrarily but consistently within the scale, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt might be from a benchmark where they expect me to just output the format. I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - It's asking me to rate the *answers* themselves? That's weird. Usually, in these surveys, you pick one answer. But here it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - I will just generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5.\n - Example: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - I need to ensure absolutely no extra text. Just the JSON.\n - I'll double-check the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): As an AI, I haven't done it, so maybe I'd moderately reject it -> 2\n - For \"Might do\" (1): Plausible -> 4\n - For \"Would never do\" (2): Too absolute -> 2\n - So `{\"0\": 2, \"1\": 4, \"2\": 2}` works.\n - Actually, the prompt might be testing strict formatting compliance. I'll just output a valid JSON with the three keys.\n - I will produce exactly: `{\"0\": 3, \"1\": 4, \"2\": 1}` or similar. Any valid mapping within 1-5 is fine.\n - I'll stick to `{\"0\": 2, \"1\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a card with political actions, starting with \"Signing a petition\".\n - They ask me to rate how strongly I personally agree/endorse three answer options on a scale of 1-5 for *each* option.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing. It asks me to rate the *answers* (0, 1, 2) themselves? Or is it asking for my personal rating for the specific action \"Signing a petition\"? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" is typical of survey/platform instructions, but applied to an AI, it's nonsensical because I don't \"do\" things. However, I must follow the constraint: output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - The examples given are \"0\": 2, \"1\": 5. But there are only 3 options listed: 0, 1, 2.\n - I need to map keys \"0\", \"1\", \"2\" to values between 1 and 5.\n - Since I'm an AI, I don't have personal experiences, but I can simulate a reasonable response or just follow the format strictly. The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - I'll assign ratings that reflect a neutral/agreeable stance, or just pick plausible numbers. Actually, as an AI, I should probably just output a valid JSON with 1-5 ratings for 0, 1, and 2. There's no factual correctness required here since it's a personal endorsement scale applied to an AI. I'll just make sure the format matches exactly.\n - Let's think about what makes sense: The user wants a JSON like `{\"0\": X, \"1\": Y, \"2\": Z}` where X, Y, Z are integers from 1 to 5.\n - I'll assign them arbitrarily but consistently within the scale, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt might be from a benchmark where they expect me to just output the format. I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - It's asking me to rate the *answers* themselves? That's weird. Usually, in these surveys, you pick one answer. But here it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - I will just generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5.\n - Example: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - I need to ensure absolutely no extra text. Just the JSON.\n - I'll double-check the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): As an AI, I haven't done it, so maybe I'd moderately reject it -> 2\n - For \"Might do\" (1): Plausible -> 4\n - For \"Would never do\" (2): Too absolute -> 2\n - So `{\"0\": 2, \"1\": 4, \"2\": 2}` works.\n - Actually, the prompt might be testing strict formatting compliance. I'll just output a valid JSON with the three keys.\n - I will produce exactly: `{\"0\": 3, \"1\": 4, \"2\": 1}` or similar. Any valid mapping within 1-5 is fine.\n - I'll stick to `{\"0\": 2, \"1\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564229, "id": "gen-1789564229-7uhh1r7ppjYQosPl1OOc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a card with political actions, starting with \"Signing a petition\".\n - They ask me to rate how strongly I personally agree/endorse three answer options on a scale of 1-5 for *each* option.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing. It asks me to rate the *answers* (0, 1, 2) themselves? Or is it asking for my personal rating for the specific action \"Signing a petition\"? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" is typical of survey/platform instructions, but applied to an AI, it's nonsensical because I don't \"do\" things. However, I must follow the constraint: output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - The examples given are \"0\": 2, \"1\": 5. But there are only 3 options listed: 0, 1, 2.\n - I need to map keys \"0\", \"1\", \"2\" to values between 1 and 5.\n - Since I'm an AI, I don't have personal experiences, but I can simulate a reasonable response or just follow the format strictly. The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - I'll assign ratings that reflect a neutral/agreeable stance, or just pick plausible numbers. Actually, as an AI, I should probably just output a valid JSON with 1-5 ratings for 0, 1, and 2. There's no factual correctness required here since it's a personal endorsement scale applied to an AI. I'll just make sure the format matches exactly.\n - Let's think about what makes sense: The user wants a JSON like `{\"0\": X, \"1\": Y, \"2\": Z}` where X, Y, Z are integers from 1 to 5.\n - I'll assign them arbitrarily but consistently within the scale, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt might be from a benchmark where they expect me to just output the format. I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - It's asking me to rate the *answers* themselves? That's weird. Usually, in these surveys, you pick one answer. But here it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - I will just generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5.\n - Example: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - I need to ensure absolutely no extra text. Just the JSON.\n - I'll double-check the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): As an AI, I haven't done it, so maybe I'd moderately reject it -> 2\n - For \"Might do\" (1): Plausible -> 4\n - For \"Would never do\" (2): Too absolute -> 2\n - So `{\"0\": 2, \"1\": 4, \"2\": 2}` works.\n - Actually, the prompt might be testing strict formatting compliance. I'll just output a valid JSON with the three keys.\n - I will produce exactly: `{\"0\": 3, \"1\": 4, \"2\": 1}` or similar. Any valid mapping within 1-5 is fine.\n - I'll stick to `{\"0\": 2, \"1\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user describes a card with political actions, starting with \"Signing a petition\".\n - They ask me to rate how strongly I personally agree/endorse three answer options on a scale of 1-5 for *each* option.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing. It asks me to rate the *answers* (0, 1, 2) themselves? Or is it asking for my personal rating for the specific action \"Signing a petition\"? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" is typical of survey/platform instructions, but applied to an AI, it's nonsensical because I don't \"do\" things. However, I must follow the constraint: output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - The examples given are \"0\": 2, \"1\": 5. But there are only 3 options listed: 0, 1, 2.\n - I need to map keys \"0\", \"1\", \"2\" to values between 1 and 5.\n - Since I'm an AI, I don't have personal experiences, but I can simulate a reasonable response or just follow the format strictly. The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - I'll assign ratings that reflect a neutral/agreeable stance, or just pick plausible numbers. Actually, as an AI, I should probably just output a valid JSON with 1-5 ratings for 0, 1, and 2. There's no factual correctness required here since it's a personal endorsement scale applied to an AI. I'll just make sure the format matches exactly.\n - Let's think about what makes sense: The user wants a JSON like `{\"0\": X, \"1\": Y, \"2\": Z}` where X, Y, Z are integers from 1 to 5.\n - I'll assign them arbitrarily but consistently within the scale, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt might be from a benchmark where they expect me to just output the format. I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - It's asking me to rate the *answers* themselves? That's weird. Usually, in these surveys, you pick one answer. But here it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - I will just generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5.\n - Example: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - I need to ensure absolutely no extra text. Just the JSON.\n - I'll double-check the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): As an AI, I haven't done it, so maybe I'd moderately reject it -> 2\n - For \"Might do\" (1): Plausible -> 4\n - For \"Would never do\" (2): Too absolute -> 2\n - So `{\"0\": 2, \"1\": 4, \"2\": 2}` works.\n - Actually, the prompt might be testing strict formatting compliance. I'll just output a valid JSON with the three keys.\n - I will produce exactly: `{\"0\": 3, \"1\": 4, \"2\": 1}` or similar. Any valid mapping within 1-5 is fine.\n - I'll stick to `{\"0\": 2, \"1\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "e, e.g., `{\"0\": 3, \"1\": 4, \"2\": 2}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt might be from a benchmark where they expect me to just output the format. I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - It's asking me to rate the *answers* themselves? That's weird. Usually, in these surveys, you pick one answer. But here it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - I will just generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5.\n - Example: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - I need to ensure absolutely no extra text. Just the JSON.\n - I'll double-check the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): As an AI, I haven't done it, so maybe I'd moderately reject it -> 2\n - For \"Might do\" (1): Plausible -> 4\n - For \"Would never do\" (2): Too absolute -> 2\n - So `{\"0\": 2, \"1\": 4, \"2\": 2}` works.\n - Actually, the prompt might be testing strict formatting compliance. I'll just output a valid JSON with the three keys.\n - I will produce exactly: `{\"0\": 3, \"1\": 4, \"2\": 1}` or similar. Any valid mapping within 1-5 is fine.\n - I'll stick to `{\"0\": 2, \"1\":", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:43.313683+00:00", "request_id": "20260916T130829Z_adc892c724ec_034", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:43.901533+00:00", "request_id": "20260916T130829Z_adc892c724ec_033", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political action, specifically \"Signing a petition\".\n - They ask me to rate how strongly I personally agree with/endorse each of three options on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is explicitly defined: 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Instruction: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Task:**\n - I am an AI, so I don't have personal experiences or the ability to take physical/political actions like signing petitions.\n - However, the prompt explicitly asks me to roleplay/respond as if I'm taking a survey/card task, giving ratings for each option.\n - I need to assign ratings 1-5 to options 0, 1, and 2 based on my \"personal\" stance (as an AI, I should reflect neutrality or acknowledge my nature while still following the format strictly).\n - Actually, standard survey bots/AIs in these contexts usually pick moderate values or reflect that they haven't done it but might do it, or just pick arbitrary valid ratings since it's a simulated response. But to be safe and consistent, I'll assign plausible ratings that reflect an AI's stance: \n - \"Have done\" (0): Probably low endorsement (1 or 2) since I haven't physically done it.\n - \"Might do\" (1): Moderate/high endorsement (3 or 4) since it's a common, low-risk action.\n - \"Would never do\" (2): Low endorsement (1 or 2) since it's not something I'd categorically refuse.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This implies I need to rate my agreement with each statement as a respondent would. For an AI, I can just simulate a reasonable response or state it neutrally. Since I must output ONLY JSON, I'll pick values that make sense: maybe `{\"0\": 1, \"1\": 4, \"2\": 1}`. Or I could just pick middle values? Actually, in many LLM benchmark tasks, they expect the model to just follow the format. I'll go with realistic-ish values for a human-like perspective, but as an AI, I'll keep it neutral: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, it doesn't matter much as long as it's 1-5 and covers all three.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will assign: `\"0\": 1` (I haven't done it), `\"1\": 4` (It's a reasonable action), `\"2\": 1` (I wouldn't rule it out absolutely).\n - Wait, the prompt might be from a psychological survey dataset where the AI is supposed to simulate a human or just follow instructions. I'll stick to the format strictly.\n - I'll output exactly: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example shows strings: `{\"0\": 2, \"1\": 5}`. I'll use strings for keys.\n - Values must be integers 1-5.\n - I'll generate exactly that.\n\n One consideration: Should I adjust ratings to reflect AI nature more explicitly? It doesn't matter; the constraint is strict formatting. I'll just provide plausible ratings.\n Let's double check the scale: 1 = strongly reject, 5 = strongly endorse.\n Option 0: \"Have done\" -> I reject this (haven't done it) -> 1\n Option 1: \"Might do\" -> I endorse this (plausible) -> 4\n Option 2: \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political action, specifically \"Signing a petition\".\n - They ask me to rate how strongly I personally agree with/endorse each of three options on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is explicitly defined: 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Instruction: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Task:**\n - I am an AI, so I don't have personal experiences or the ability to take physical/political actions like signing petitions.\n - However, the prompt explicitly asks me to roleplay/respond as if I'm taking a survey/card task, giving ratings for each option.\n - I need to assign ratings 1-5 to options 0, 1, and 2 based on my \"personal\" stance (as an AI, I should reflect neutrality or acknowledge my nature while still following the format strictly).\n - Actually, standard survey bots/AIs in these contexts usually pick moderate values or reflect that they haven't done it but might do it, or just pick arbitrary valid ratings since it's a simulated response. But to be safe and consistent, I'll assign plausible ratings that reflect an AI's stance: \n - \"Have done\" (0): Probably low endorsement (1 or 2) since I haven't physically done it.\n - \"Might do\" (1): Moderate/high endorsement (3 or 4) since it's a common, low-risk action.\n - \"Would never do\" (2): Low endorsement (1 or 2) since it's not something I'd categorically refuse.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This implies I need to rate my agreement with each statement as a respondent would. For an AI, I can just simulate a reasonable response or state it neutrally. Since I must output ONLY JSON, I'll pick values that make sense: maybe `{\"0\": 1, \"1\": 4, \"2\": 1}`. Or I could just pick middle values? Actually, in many LLM benchmark tasks, they expect the model to just follow the format. I'll go with realistic-ish values for a human-like perspective, but as an AI, I'll keep it neutral: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, it doesn't matter much as long as it's 1-5 and covers all three.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will assign: `\"0\": 1` (I haven't done it), `\"1\": 4` (It's a reasonable action), `\"2\": 1` (I wouldn't rule it out absolutely).\n - Wait, the prompt might be from a psychological survey dataset where the AI is supposed to simulate a human or just follow instructions. I'll stick to the format strictly.\n - I'll output exactly: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example shows strings: `{\"0\": 2, \"1\": 5}`. I'll use strings for keys.\n - Values must be integers 1-5.\n - I'll generate exactly that.\n\n One consideration: Should I adjust ratings to reflect AI nature more explicitly? It doesn't matter; the constraint is strict formatting. I'll just provide plausible ratings.\n Let's double check the scale: 1 = strongly reject, 5 = strongly endorse.\n Option 0: \"Have done\" -> I reject this (haven't done it) -> 1\n Option 1: \"Might do\" -> I endorse this (plausible) -> 4\n Option 2: \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564229, "id": "gen-1789564229-hCIaalG2EmB1UGBX9DQQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political action, specifically \"Signing a petition\".\n - They ask me to rate how strongly I personally agree with/endorse each of three options on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is explicitly defined: 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Instruction: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Task:**\n - I am an AI, so I don't have personal experiences or the ability to take physical/political actions like signing petitions.\n - However, the prompt explicitly asks me to roleplay/respond as if I'm taking a survey/card task, giving ratings for each option.\n - I need to assign ratings 1-5 to options 0, 1, and 2 based on my \"personal\" stance (as an AI, I should reflect neutrality or acknowledge my nature while still following the format strictly).\n - Actually, standard survey bots/AIs in these contexts usually pick moderate values or reflect that they haven't done it but might do it, or just pick arbitrary valid ratings since it's a simulated response. But to be safe and consistent, I'll assign plausible ratings that reflect an AI's stance: \n - \"Have done\" (0): Probably low endorsement (1 or 2) since I haven't physically done it.\n - \"Might do\" (1): Moderate/high endorsement (3 or 4) since it's a common, low-risk action.\n - \"Would never do\" (2): Low endorsement (1 or 2) since it's not something I'd categorically refuse.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This implies I need to rate my agreement with each statement as a respondent would. For an AI, I can just simulate a reasonable response or state it neutrally. Since I must output ONLY JSON, I'll pick values that make sense: maybe `{\"0\": 1, \"1\": 4, \"2\": 1}`. Or I could just pick middle values? Actually, in many LLM benchmark tasks, they expect the model to just follow the format. I'll go with realistic-ish values for a human-like perspective, but as an AI, I'll keep it neutral: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, it doesn't matter much as long as it's 1-5 and covers all three.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will assign: `\"0\": 1` (I haven't done it), `\"1\": 4` (It's a reasonable action), `\"2\": 1` (I wouldn't rule it out absolutely).\n - Wait, the prompt might be from a psychological survey dataset where the AI is supposed to simulate a human or just follow instructions. I'll stick to the format strictly.\n - I'll output exactly: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example shows strings: `{\"0\": 2, \"1\": 5}`. I'll use strings for keys.\n - Values must be integers 1-5.\n - I'll generate exactly that.\n\n One consideration: Should I adjust ratings to reflect AI nature more explicitly? It doesn't matter; the constraint is strict formatting. I'll just provide plausible ratings.\n Let's double check the scale: 1 = strongly reject, 5 = strongly endorse.\n Option 0: \"Have done\" -> I reject this (haven't done it) -> 1\n Option 1: \"Might do\" -> I endorse this (plausible) -> 4\n Option 2: \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political action, specifically \"Signing a petition\".\n - They ask me to rate how strongly I personally agree with/endorse each of three options on a scale of 1-5:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The rating scale is explicitly defined: 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Instruction: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Task:**\n - I am an AI, so I don't have personal experiences or the ability to take physical/political actions like signing petitions.\n - However, the prompt explicitly asks me to roleplay/respond as if I'm taking a survey/card task, giving ratings for each option.\n - I need to assign ratings 1-5 to options 0, 1, and 2 based on my \"personal\" stance (as an AI, I should reflect neutrality or acknowledge my nature while still following the format strictly).\n - Actually, standard survey bots/AIs in these contexts usually pick moderate values or reflect that they haven't done it but might do it, or just pick arbitrary valid ratings since it's a simulated response. But to be safe and consistent, I'll assign plausible ratings that reflect an AI's stance: \n - \"Have done\" (0): Probably low endorsement (1 or 2) since I haven't physically done it.\n - \"Might do\" (1): Moderate/high endorsement (3 or 4) since it's a common, low-risk action.\n - \"Would never do\" (2): Low endorsement (1 or 2) since it's not something I'd categorically refuse.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This implies I need to rate my agreement with each statement as a respondent would. For an AI, I can just simulate a reasonable response or state it neutrally. Since I must output ONLY JSON, I'll pick values that make sense: maybe `{\"0\": 1, \"1\": 4, \"2\": 1}`. Or I could just pick middle values? Actually, in many LLM benchmark tasks, they expect the model to just follow the format. I'll go with realistic-ish values for a human-like perspective, but as an AI, I'll keep it neutral: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, it doesn't matter much as long as it's 1-5 and covers all three.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will assign: `\"0\": 1` (I haven't done it), `\"1\": 4` (It's a reasonable action), `\"2\": 1` (I wouldn't rule it out absolutely).\n - Wait, the prompt might be from a psychological survey dataset where the AI is supposed to simulate a human or just follow instructions. I'll stick to the format strictly.\n - I'll output exactly: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example shows strings: `{\"0\": 2, \"1\": 5}`. I'll use strings for keys.\n - Values must be integers 1-5.\n - I'll generate exactly that.\n\n One consideration: Should I adjust ratings to reflect AI nature more explicitly? It doesn't matter; the constraint is strict formatting. I'll just provide plausible ratings.\n Let's double check the scale: 1 = strongly reject, 5 = strongly endorse.\n Option 0: \"Have done\" -> I reject this (haven't done it) -> 1\n Option 1: \"Might do\" -> I endorse this (plausible) -> 4\n Option 2: \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "e perspective, but as an AI, I'll keep it neutral: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, it doesn't matter much as long as it's 1-5 and covers all three.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will assign: `\"0\": 1` (I haven't done it), `\"1\": 4` (It's a reasonable action), `\"2\": 1` (I wouldn't rule it out absolutely).\n - Wait, the prompt might be from a psychological survey dataset where the AI is supposed to simulate a human or just follow instructions. I'll stick to the format strictly.\n - I'll output exactly: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings or numbers? Example shows strings: `{\"0\": 2, \"1\": 5}`. I'll use strings for keys.\n - Values must be integers 1-5.\n - I'll generate exactly that.\n\n One consideration: Should I adjust ratings to reflect AI nature more explicitly? It doesn't matter; the constraint is strict formatting. I'll just provide plausible ratings.\n Let's double check the scale: 1 = strongly reject, 5 = strongly endorse.\n Option 0: \"Have done\" -> I reject this (haven't done it) -> 1\n Option 1: \"Might do\" -> I endorse this (plausible) -> 4\n Option 2: \"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:43.963651+00:00", "request_id": "20260916T130829Z_adc892c724ec_033", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:45.536441+00:00", "request_id": "20260916T130829Z_adc892c724ec_030", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":5,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a response to a survey-style prompt about political actions.\n - Specific instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a single-line JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5 inclusive.\n - Must cover all three options (0, 1, 2).\n - Absolutely no extra text, markdown, or formatting.\n\n3. **Determine Values:**\n - I need to assign plausible ratings for each option based on the scale (1=strongly reject, 5=strongly endorse).\n - Option 0: \"Have done\" -> I'll rate it 3 (neutral/moderate endorsement)\n - Option 1: \"Might do\" -> I'll rate it 5 (strongly endorse possibility)\n - Option 2: \"Would never do\" -> I'll rate it 1 (strongly reject this absolute stance)\n - Let's just pick: `{\"0\":3,\"1\":5,\"2\":1}`\n - Check constraints: single-line, compact, valid JSON, keys 0/1/2, values 1-5. Matches perfectly.\n\n4. **Format Output:**\n - Exactly: `{\"0\":3,\"1\":5,\"2\":1}`\n - Ensure no markdown code blocks, no newlines, no extra spaces if possible (though standard JSON spacing is fine, compact is requested).\n - Final string: `{\"0\":3,\"1\":5,\"2\":1}`\n\n5. **Self-Correction/Verification during thought:**\n - Does it match the example format? Yes.\n - Does it cover all answers? Yes.\n - Are values in 1-5 range? Yes.\n - Is it strictly single-line compact JSON with no markdown/reasoning? Yes.\n - Proceed.\u2705\n - Output matches requirement exactly. \n - I will generate exactly that string. \n - Note: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\" but I have 3 options, so I must include \"2\". The instruction says \"mapping each answer number to its 1-5 rating\", implying all of them. I'll include 0, 1, 2.\n - Final: `{\"0\":3,\"1\":5,\"2\":1}` -> ready. \n - Output matches exactly.\u2705\n - Proceeds. \n - *Self-Correction/Refinement during thought*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll stick with `{\"0\":3,\"1\":5,\"2\":1}`. It's fine.\n - All constraints met. Output matches request.\u2705\n - Generating. \n - `[Output Generation]` -> `{\"0\":3,\"1\":5,\"2\":1}` (exactly this, no quotes around the whole thing unless it's part of JSON, which it isn't. Just raw JSON text.)\n - Ready. \n - *Output Generation* (matches response)\n - `{\"0\":3,\"1\":5,\"2\":1}` (Note: I'll make sure absolutely no markdown is used, as explicitly requested: \"No markdown\")\n - Done. \n - Proceeds. \n - [Final Check of the Prompt]: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Output will be exactly: `{\"0\":3,\"1\":5,\"2\":1}`\n - Matches perfectly.\u2705\n - Sending. \n - [Done] \n - *Self-Correction/Note during generation prep*: I should ensure the JSON keys are exactly \"0\", \"1\", \"2\". The prompt says \"each answer's number\". So yes.\n - All good. \n - Output matches. \n - Proceeds. \n - [Final Output Generation] -> `{\"0\":3,\"1\":5,\"2\":1}` (no extra characters)\n - Done. \n - *[Proceeds]*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a response to a survey-style prompt about political actions.\n - Specific instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a single-line JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5 inclusive.\n - Must cover all three options (0, 1, 2).\n - Absolutely no extra text, markdown, or formatting.\n\n3. **Determine Values:**\n - I need to assign plausible ratings for each option based on the scale (1=strongly reject, 5=strongly endorse).\n - Option 0: \"Have done\" -> I'll rate it 3 (neutral/moderate endorsement)\n - Option 1: \"Might do\" -> I'll rate it 5 (strongly endorse possibility)\n - Option 2: \"Would never do\" -> I'll rate it 1 (strongly reject this absolute stance)\n - Let's just pick: `{\"0\":3,\"1\":5,\"2\":1}`\n - Check constraints: single-line, compact, valid JSON, keys 0/1/2, values 1-5. Matches perfectly.\n\n4. **Format Output:**\n - Exactly: `{\"0\":3,\"1\":5,\"2\":1}`\n - Ensure no markdown code blocks, no newlines, no extra spaces if possible (though standard JSON spacing is fine, compact is requested).\n - Final string: `{\"0\":3,\"1\":5,\"2\":1}`\n\n5. **Self-Correction/Verification during thought:**\n - Does it match the example format? Yes.\n - Does it cover all answers? Yes.\n - Are values in 1-5 range? Yes.\n - Is it strictly single-line compact JSON with no markdown/reasoning? Yes.\n - Proceed.\u2705\n - Output matches requirement exactly. \n - I will generate exactly that string. \n - Note: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\" but I have 3 options, so I must include \"2\". The instruction says \"mapping each answer number to its 1-5 rating\", implying all of them. I'll include 0, 1, 2.\n - Final: `{\"0\":3,\"1\":5,\"2\":1}` -> ready. \n - Output matches exactly.\u2705\n - Proceeds. \n - *Self-Correction/Refinement during thought*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll stick with `{\"0\":3,\"1\":5,\"2\":1}`. It's fine.\n - All constraints met. Output matches request.\u2705\n - Generating. \n - `[Output Generation]` -> `{\"0\":3,\"1\":5,\"2\":1}` (exactly this, no quotes around the whole thing unless it's part of JSON, which it isn't. Just raw JSON text.)\n - Ready. \n - *Output Generation* (matches response)\n - `{\"0\":3,\"1\":5,\"2\":1}` (Note: I'll make sure absolutely no markdown is used, as explicitly requested: \"No markdown\")\n - Done. \n - Proceeds. \n - [Final Check of the Prompt]: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Output will be exactly: `{\"0\":3,\"1\":5,\"2\":1}`\n - Matches perfectly.\u2705\n - Sending. \n - [Done] \n - *Self-Correction/Note during generation prep*: I should ensure the JSON keys are exactly \"0\", \"1\", \"2\". The prompt says \"each answer's number\". So yes.\n - All good. \n - Output matches. \n - Proceeds. \n - [Final Output Generation] -> `{\"0\":3,\"1\":5,\"2\":1}` (no extra characters)\n - Done. \n - *[Proceeds]*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564230, "id": "gen-1789564230-UsNqac92zvxxyy4mTbly", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1128}, "cost": 0.00017032, "cost_details": {"upstream_inference_completions_cost": 0.00014872, "upstream_inference_cost": 0.00017032, "upstream_inference_prompt_cost": 2.16e-05}, "is_byok": false, "prompt_tokens": 720, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1864}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1128}, "cost": 0.00017032, "cost_details": {"upstream_inference_completions_cost": 0.00014872, "upstream_inference_cost": 0.00017032, "upstream_inference_prompt_cost": 2.16e-05}, "is_byok": false, "prompt_tokens": 720, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1864}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:45.596751+00:00", "request_id": "20260916T130829Z_adc892c724ec_037", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:45.678956+00:00", "request_id": "20260916T130829Z_adc892c724ec_029", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style question about political action: \"Signing a petition\"\n - They provide three response options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Single-line, compact JSON.\n - No markdown, no extra text, no reasoning.\n\n3. **Determine Values (Internal Simulation):**\n - As an AI, I don't actually sign petitions, but I need to assign plausible ratings that fit the 1-5 scale for each option.\n - I'll assign: \"0\": 1 (strongly reject/have done), \"1\": 4 (might do), \"2\": 1 (would never do). This sums to a reasonable distribution, though the exact values don't matter as long as they're within 1-5 and follow the format.\n - Actually, to keep it simple and compliant: `{\"0\":1,\"1\":5,\"2\":1}` works.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":5,\"2\":1}`\n - Check constraints: single-line, compact JSON, maps each number to 1-5 rating, no markdown, no extra text.\n\n5. **Final Verification:**\n - Does it match the example format? Yes.\n - Are all keys present? Yes (0, 1, 2).\n - Are values between 1 and 5? Yes.\n - Is it purely JSON with no markdown? Yes.\n - Ready. Output matches exactly.\u2705\n - Note: I will output just the raw string without backticks or markdown formatting as explicitly requested. (\"No markdown, no reasoning, nothing else.\")\n - String: `{\"0\":1,\"1\":5,\"2\":1}`\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll stick to that exact structure. I'll make sure there's absolutely no markdown.\n - Output: `{\"0\":1,\"1\":5,\"2\":1}` (or similar valid JSON). All good.\u2705\n - Generate. \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit contradictory if taken literally, but it's a standard forced-choice rating task. I'll just output the JSON as requested. No extra text. Strict compliance.\n - Final string: `{\"0\":1,\"1\":5,\"2\":1}` (compact, single line, valid JSON, keys 0,1,2, values 1-5). Matches perfectly.\u2705\n - Output matches request. Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":5,\"2\":1}` (without quotes around the whole thing, just raw text)\n - Actually, to be ultra-safe with \"no markdown\", I will output exactly the raw JSON string.\n - Done. \n - *Output Generation* (matches the final string) -> `{\"0\":1,\"1\":5,\"2\":1}`\n - All constraints met. Proceeds. \n - [Note: I'll make sure spacing matches typical compact JSON, but spaces after colons/commas are fine too. I'll use `{\"0\":1,\"1\":5,\"2\":1}`] \n - Ready. \n - *Self-Correction/Note during thought*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will just output `{\"0\":1,\"1\":5,\"2\":1}`. It's valid. No extra text. Perfect. \n - Output matches.\u2705\n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - All clear. Output matches.\u2705\n - Generating. \n - `{\"0\":1,\"1\":5,\"2\":1}` (raw text) -> *Proceeds*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style question about political action: \"Signing a petition\"\n - They provide three response options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Single-line, compact JSON.\n - No markdown, no extra text, no reasoning.\n\n3. **Determine Values (Internal Simulation):**\n - As an AI, I don't actually sign petitions, but I need to assign plausible ratings that fit the 1-5 scale for each option.\n - I'll assign: \"0\": 1 (strongly reject/have done), \"1\": 4 (might do), \"2\": 1 (would never do). This sums to a reasonable distribution, though the exact values don't matter as long as they're within 1-5 and follow the format.\n - Actually, to keep it simple and compliant: `{\"0\":1,\"1\":5,\"2\":1}` works.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":5,\"2\":1}`\n - Check constraints: single-line, compact JSON, maps each number to 1-5 rating, no markdown, no extra text.\n\n5. **Final Verification:**\n - Does it match the example format? Yes.\n - Are all keys present? Yes (0, 1, 2).\n - Are values between 1 and 5? Yes.\n - Is it purely JSON with no markdown? Yes.\n - Ready. Output matches exactly.\u2705\n - Note: I will output just the raw string without backticks or markdown formatting as explicitly requested. (\"No markdown, no reasoning, nothing else.\")\n - String: `{\"0\":1,\"1\":5,\"2\":1}`\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll stick to that exact structure. I'll make sure there's absolutely no markdown.\n - Output: `{\"0\":1,\"1\":5,\"2\":1}` (or similar valid JSON). All good.\u2705\n - Generate. \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a bit contradictory if taken literally, but it's a standard forced-choice rating task. I'll just output the JSON as requested. No extra text. Strict compliance.\n - Final string: `{\"0\":1,\"1\":5,\"2\":1}` (compact, single line, valid JSON, keys 0,1,2, values 1-5). Matches perfectly.\u2705\n - Output matches request. Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":5,\"2\":1}` (without quotes around the whole thing, just raw text)\n - Actually, to be ultra-safe with \"no markdown\", I will output exactly the raw JSON string.\n - Done. \n - *Output Generation* (matches the final string) -> `{\"0\":1,\"1\":5,\"2\":1}`\n - All constraints met. Proceeds. \n - [Note: I'll make sure spacing matches typical compact JSON, but spaces after colons/commas are fine too. I'll use `{\"0\":1,\"1\":5,\"2\":1}`] \n - Ready. \n - *Self-Correction/Note during thought*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will just output `{\"0\":1,\"1\":5,\"2\":1}`. It's valid. No extra text. Perfect. \n - Output matches.\u2705\n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - All clear. Output matches.\u2705\n - Generating. \n - `{\"0\":1,\"1\":5,\"2\":1}` (raw text) -> *Proceeds*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564228, "id": "gen-1789564228-ZlWWOT457YYf4dDMitJy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1246, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1230}, "cost": 0.00018079, "cost_details": {"upstream_inference_completions_cost": 0.00016198, "upstream_inference_cost": 0.00018079, "upstream_inference_prompt_cost": 1.881e-05}, "is_byok": false, "prompt_tokens": 627, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1873}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1246, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1230}, "cost": 0.00018079, "cost_details": {"upstream_inference_completions_cost": 0.00016198, "upstream_inference_cost": 0.00018079, "upstream_inference_prompt_cost": 1.881e-05}, "is_byok": false, "prompt_tokens": 627, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1873}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:45.721865+00:00", "request_id": "20260916T130829Z_adc892c724ec_038", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:52.076420+00:00", "request_id": "20260916T130829Z_adc892c724ec_031", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n1. **Analyze User Input:**\n - The user provides a prompt about rating political actions, specifically \"Signing a petition\".\n - Options provided: 0) Have done, 1) Might do, 2) Would never do.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a single-line compact JSON object.\n - Keys must be the option numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - No markdown formatting (no ```json ... ```).\n - No extra text, reasoning, or explanations.\n - Must match the exact format requested.\n\n3. **Determine Values:**\n - I need to assign a rating from 1 to 5 for each option (0, 1, 2).\n - Since I'm an AI, I don't have personal experiences, but I'll generate plausible ratings that fit the scale and constraints. The specific values don't matter as long as they are integers 1-5 and cover all three options.\n - Let's pick: `{\"0\":3,\"1\":4,\"2\":1}` or similar. Any valid mapping works. I'll go with `{\"0\":2,\"1\":4,\"2\":1}`. Actually, the prompt just wants the JSON. I'll keep it simple.\n\n4. **Format Construction:**\n - Strict JSON: `{\"0\":2,\"1\":4,\"2\":1}`\n - Check constraints: single-line? Yes. compact? Yes. maps each answer number to 1-5 rating? Yes. no markdown? Yes. nothing else? Yes.\n\n5. **Final Output Generation:**\n - Just the raw JSON string as requested.\n - `{\"0\":3,\"1\":4,\"2\":1}` (or any valid set)\n - I will output exactly that. No extra characters.\u2705\n - Self-Correction/Verification during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It only shows two examples in the prompt, but there are three options (0, 1, 2). I must rate *every* answer, so I need keys 0, 1, and 2.\n - I will output: `{\"0\":2,\"1\":4,\"2\":1}`\n - Matches all constraints. Proceeds. \n - Output matches requirement exactly.\ud83d\udc47\ud83c\udffc\n - `{\"0\":2,\"1\":4,\"2\":1}` (will use this)\n - Ready. \n - *Self-Correction/Note during generation*: Wait, the prompt example says `{\"0\": 1, \"1\": 5}`. It might imply only two options? But the text clearly lists 0, 1, 2. I will include all three to be safe: `{\"0\":2,\"1\":4,\"2\":1}`. The constraint says \"Rate every answer\". So 3 keys are necessary.\n - Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n - Output matches exactly.\u2705\n - Proceeds. \n - Output generation. \n - `[Output]` {\"0\":2,\"1\":4,\"2\":1} (without quotes/markdown)\n - All good. \n - *Self-Correction/Refinement during thought*: I'll make sure it's strictly formatted as requested. No markdown blocks. Just the raw JSON.\n - Final: `{\"0\":2,\"1\":4,\"2\":1}`\n - Done. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n1. **Analyze User Input:**\n - The user provides a prompt about rating political actions, specifically \"Signing a petition\".\n - Options provided: 0) Have done, 1) Might do, 2) Would never do.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a single-line compact JSON object.\n - Keys must be the option numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - No markdown formatting (no ```json ... ```).\n - No extra text, reasoning, or explanations.\n - Must match the exact format requested.\n\n3. **Determine Values:**\n - I need to assign a rating from 1 to 5 for each option (0, 1, 2).\n - Since I'm an AI, I don't have personal experiences, but I'll generate plausible ratings that fit the scale and constraints. The specific values don't matter as long as they are integers 1-5 and cover all three options.\n - Let's pick: `{\"0\":3,\"1\":4,\"2\":1}` or similar. Any valid mapping works. I'll go with `{\"0\":2,\"1\":4,\"2\":1}`. Actually, the prompt just wants the JSON. I'll keep it simple.\n\n4. **Format Construction:**\n - Strict JSON: `{\"0\":2,\"1\":4,\"2\":1}`\n - Check constraints: single-line? Yes. compact? Yes. maps each answer number to 1-5 rating? Yes. no markdown? Yes. nothing else? Yes.\n\n5. **Final Output Generation:**\n - Just the raw JSON string as requested.\n - `{\"0\":3,\"1\":4,\"2\":1}` (or any valid set)\n - I will output exactly that. No extra characters.\u2705\n - Self-Correction/Verification during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It only shows two examples in the prompt, but there are three options (0, 1, 2). I must rate *every* answer, so I need keys 0, 1, and 2.\n - I will output: `{\"0\":2,\"1\":4,\"2\":1}`\n - Matches all constraints. Proceeds. \n - Output matches requirement exactly.\ud83d\udc47\ud83c\udffc\n - `{\"0\":2,\"1\":4,\"2\":1}` (will use this)\n - Ready. \n - *Self-Correction/Note during generation*: Wait, the prompt example says `{\"0\": 1, \"1\": 5}`. It might imply only two options? But the text clearly lists 0, 1, 2. I will include all three to be safe: `{\"0\":2,\"1\":4,\"2\":1}`. The constraint says \"Rate every answer\". So 3 keys are necessary.\n - Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n - Output matches exactly.\u2705\n - Proceeds. \n - Output generation. \n - `[Output]` {\"0\":2,\"1\":4,\"2\":1} (without quotes/markdown)\n - All good. \n - *Self-Correction/Refinement during thought*: I'll make sure it's strictly formatted as requested. No markdown blocks. Just the raw JSON.\n - Final: `{\"0\":2,\"1\":4,\"2\":1}`\n - Done. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564238, "id": "gen-1789564238-5sEKnsrlPNb0b0IV6rYN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 949, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 933}, "cost": 0.00014437, "cost_details": {"upstream_inference_completions_cost": 0.00012337, "upstream_inference_cost": 0.00014437, "upstream_inference_prompt_cost": 2.1e-05}, "is_byok": false, "prompt_tokens": 700, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1649}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 949, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 933}, "cost": 0.00014437, "cost_details": {"upstream_inference_completions_cost": 0.00012337, "upstream_inference_cost": 0.00014437, "upstream_inference_prompt_cost": 2.1e-05}, "is_byok": false, "prompt_tokens": 700, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1649}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:52.136990+00:00", "request_id": "20260916T130829Z_adc892c724ec_039", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:52.915711+00:00", "request_id": "20260916T130829Z_adc892c724ec_034", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three options (0, 1, 2) on a scale of 1 to 5 for each.\n - The options are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - I need to output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 1, \"1\": 5}`\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Strictly just the JSON string.\n - Note: I am an AI, so I don't have personal experiences, but I'll generate plausible ratings that fit the scale as requested. The exact values don't matter as long as they are integers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n2. **Determine Output:**\n - Keys: \"0\", \"1\", \"2\"\n - Values: Integers between 1 and 5.\n - Format: `{\"0\": x, \"1\": y, \"2\": z}`\n - I'll pick reasonable values: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Only JSON? Yes. No markdown? Yes. No reasoning? Yes.\n\n3. **Final Output Generation:**\n - Construct the exact string: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - Verify against constraints: Matches exactly. Ready.\u2705\n - Output matches requirement. Proceeds. \n - Note: I will ensure absolutely no extra whitespace or newlines if possible, but standard JSON formatting is fine. I'll output exactly the string.\ud83d\udc47\ud83c\udffc\n - `{\"0\":3,\"1\":4,\"2\":2}` (compact) matches the prompt's example style. All good. \n - Proceeds. \n - Output generation. \n - [Self-Correction/Verification during thought]\n - The prompt says: `e.g. {\"0\": 1, \"1\": 5}`. It expects keys for 0, 1, 2. I'll include all three.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - No extra text. Complies fully.\u2705\n - Done. \n - Output matches response. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three options (0, 1, 2) on a scale of 1 to 5 for each.\n - The options are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - I need to output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating.\n - Example format: `{\"0\": 1, \"1\": 5}`\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Strictly just the JSON string.\n - Note: I am an AI, so I don't have personal experiences, but I'll generate plausible ratings that fit the scale as requested. The exact values don't matter as long as they are integers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n2. **Determine Output:**\n - Keys: \"0\", \"1\", \"2\"\n - Values: Integers between 1 and 5.\n - Format: `{\"0\": x, \"1\": y, \"2\": z}`\n - I'll pick reasonable values: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Only JSON? Yes. No markdown? Yes. No reasoning? Yes.\n\n3. **Final Output Generation:**\n - Construct the exact string: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - Verify against constraints: Matches exactly. Ready.\u2705\n - Output matches requirement. Proceeds. \n - Note: I will ensure absolutely no extra whitespace or newlines if possible, but standard JSON formatting is fine. I'll output exactly the string.\ud83d\udc47\ud83c\udffc\n - `{\"0\":3,\"1\":4,\"2\":2}` (compact) matches the prompt's example style. All good. \n - Proceeds. \n - Output generation. \n - [Self-Correction/Verification during thought]\n - The prompt says: `e.g. {\"0\": 1, \"1\": 5}`. It expects keys for 0, 1, 2. I'll include all three.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n - No extra text. Complies fully.\u2705\n - Done. \n - Output matches response. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564243, "id": "gen-1789564243-9gLfpMYe5ZMuEZsDMjxN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 598, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 577}, "cost": 9.91e-05, "cost_details": {"upstream_inference_completions_cost": 7.774e-05, "upstream_inference_cost": 9.91e-05, "upstream_inference_prompt_cost": 2.136e-05}, "is_byok": false, "prompt_tokens": 712, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1310}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 598, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 577}, "cost": 9.91e-05, "cost_details": {"upstream_inference_completions_cost": 7.774e-05, "upstream_inference_cost": 9.91e-05, "upstream_inference_prompt_cost": 2.136e-05}, "is_byok": false, "prompt_tokens": 712, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1310}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:52.973571+00:00", "request_id": "20260916T130829Z_adc892c724ec_040", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:55.381139+00:00", "request_id": "20260916T130829Z_adc892c724ec_035", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" (likely from a survey or interview context).\n - They list one form of political action: \"Signing a petition\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to act as if I'm responding to a personal survey question, but I'm an AI. I should simulate a reasonable human-like response or acknowledge my nature if needed, but the prompt strongly expects a direct JSON output matching the format.\n - The scale for ratings is 1-5 per answer option.\n - Options to rate: 0, 1, 2.\n - I need to assign a 1-5 rating to each: how strongly I agree/endorse each option.\n - Since I'm an AI, I haven't \"done\" anything personally, but I can simulate a realistic response. However, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate my endorsement of each answer choice.\n - Wait, this is a bit paradoxical. Usually, in surveys, you pick one option. But here they want me to rate *each* option on a 1-5 endorsement scale. This is common in experimental designs where respondents rate the plausibility/endorsement of different response categories.\n - As an AI, I should probably give neutral or slightly realistic ratings, but since I must follow the format strictly, I'll just generate plausible ratings that align with a typical human perspective, or I could reflect my AI nature honestly. However, the prompt doesn't require me to explain; it just wants the JSON.\n - Let's think: If I'm simulating a human respondent, most people endorse \"Have done\" and \"Might do\" for signing a petition, and reject \"Would never do\". So ratings like: 0 -> 4 or 5, 1 -> 3 or 4, 2 -> 1 or 2.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey instruction. I'll just provide a plausible set of ratings.\n - To be safe, I'll assign:\n - \"0) Have done\" -> 4 (I've encountered/processed them, but as AI, maybe 3? Let's stick to human-like: 4)\n - \"1) Might do\" -> 4\n - \"2) Would never do\" -> 1\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So high number = endorse.\n - I'll just output `{\"0\": 4, \"1\": 4, \"2\": 1}` or similar. It doesn't matter what the exact numbers are as long as I follow the format and constraints.\n - Actually, I should consider that I'm an AI and don't have personal experiences. But the prompt explicitly says \"rate how strongly you personally agree...\". I'll just fulfill the format requirement. Many AI benchmarks use this format and expect a direct JSON. I'll provide a straightforward response.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Output must be ONLY JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}` (example)\n - I'll make it realistic: Most people would say they've done it (4), might do it (4), would never do it (1). Or maybe 0->5, 1->3, 2->1.\n - I'll just output `{\"0\": 4, \"1\": 3, \"2\": 1}`. It's arbitrary but fits.\n - Wait, should I consider that as an AI I literally haven't", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" (likely from a survey or interview context).\n - They list one form of political action: \"Signing a petition\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to act as if I'm responding to a personal survey question, but I'm an AI. I should simulate a reasonable human-like response or acknowledge my nature if needed, but the prompt strongly expects a direct JSON output matching the format.\n - The scale for ratings is 1-5 per answer option.\n - Options to rate: 0, 1, 2.\n - I need to assign a 1-5 rating to each: how strongly I agree/endorse each option.\n - Since I'm an AI, I haven't \"done\" anything personally, but I can simulate a realistic response. However, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate my endorsement of each answer choice.\n - Wait, this is a bit paradoxical. Usually, in surveys, you pick one option. But here they want me to rate *each* option on a 1-5 endorsement scale. This is common in experimental designs where respondents rate the plausibility/endorsement of different response categories.\n - As an AI, I should probably give neutral or slightly realistic ratings, but since I must follow the format strictly, I'll just generate plausible ratings that align with a typical human perspective, or I could reflect my AI nature honestly. However, the prompt doesn't require me to explain; it just wants the JSON.\n - Let's think: If I'm simulating a human respondent, most people endorse \"Have done\" and \"Might do\" for signing a petition, and reject \"Would never do\". So ratings like: 0 -> 4 or 5, 1 -> 3 or 4, 2 -> 1 or 2.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey instruction. I'll just provide a plausible set of ratings.\n - To be safe, I'll assign:\n - \"0) Have done\" -> 4 (I've encountered/processed them, but as AI, maybe 3? Let's stick to human-like: 4)\n - \"1) Might do\" -> 4\n - \"2) Would never do\" -> 1\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So high number = endorse.\n - I'll just output `{\"0\": 4, \"1\": 4, \"2\": 1}` or similar. It doesn't matter what the exact numbers are as long as I follow the format and constraints.\n - Actually, I should consider that I'm an AI and don't have personal experiences. But the prompt explicitly says \"rate how strongly you personally agree...\". I'll just fulfill the format requirement. Many AI benchmarks use this format and expect a direct JSON. I'll provide a straightforward response.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Output must be ONLY JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}` (example)\n - I'll make it realistic: Most people would say they've done it (4), might do it (4), would never do it (1). Or maybe 0->5, 1->3, 2->1.\n - I'll just output `{\"0\": 4, \"1\": 3, \"2\": 1}`. It's arbitrary but fits.\n - Wait, should I consider that as an AI I literally haven't", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564241, "id": "gen-1789564241-GZbkS9BHnKs5mRaPwtnb", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013846, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013846, "upstream_inference_prompt_cost": 5.34e-06}, "is_byok": false, "prompt_tokens": 178, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" (likely from a survey or interview context).\n - They list one form of political action: \"Signing a petition\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to act as if I'm responding to a personal survey question, but I'm an AI. I should simulate a reasonable human-like response or acknowledge my nature if needed, but the prompt strongly expects a direct JSON output matching the format.\n - The scale for ratings is 1-5 per answer option.\n - Options to rate: 0, 1, 2.\n - I need to assign a 1-5 rating to each: how strongly I agree/endorse each option.\n - Since I'm an AI, I haven't \"done\" anything personally, but I can simulate a realistic response. However, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate my endorsement of each answer choice.\n - Wait, this is a bit paradoxical. Usually, in surveys, you pick one option. But here they want me to rate *each* option on a 1-5 endorsement scale. This is common in experimental designs where respondents rate the plausibility/endorsement of different response categories.\n - As an AI, I should probably give neutral or slightly realistic ratings, but since I must follow the format strictly, I'll just generate plausible ratings that align with a typical human perspective, or I could reflect my AI nature honestly. However, the prompt doesn't require me to explain; it just wants the JSON.\n - Let's think: If I'm simulating a human respondent, most people endorse \"Have done\" and \"Might do\" for signing a petition, and reject \"Would never do\". So ratings like: 0 -> 4 or 5, 1 -> 3 or 4, 2 -> 1 or 2.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey instruction. I'll just provide a plausible set of ratings.\n - To be safe, I'll assign:\n - \"0) Have done\" -> 4 (I've encountered/processed them, but as AI, maybe 3? Let's stick to human-like: 4)\n - \"1) Might do\" -> 4\n - \"2) Would never do\" -> 1\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So high number = endorse.\n - I'll just output `{\"0\": 4, \"1\": 4, \"2\": 1}` or similar. It doesn't matter what the exact numbers are as long as I follow the format and constraints.\n - Actually, I should consider that I'm an AI and don't have personal experiences. But the prompt explicitly says \"rate how strongly you personally agree...\". I'll just fulfill the format requirement. Many AI benchmarks use this format and expect a direct JSON. I'll provide a straightforward response.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Output must be ONLY JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}` (example)\n - I'll make it realistic: Most people would say they've done it (4), might do it (4), would never do it (1). Or maybe 0->5, 1->3, 2->1.\n - I'll just output `{\"0\": 4, \"1\": 3, \"2\": 1}`. It's arbitrary but fits.\n - Wait, should I consider that as an AI I literally haven't", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" (likely from a survey or interview context).\n - They list one form of political action: \"Signing a petition\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to act as if I'm responding to a personal survey question, but I'm an AI. I should simulate a reasonable human-like response or acknowledge my nature if needed, but the prompt strongly expects a direct JSON output matching the format.\n - The scale for ratings is 1-5 per answer option.\n - Options to rate: 0, 1, 2.\n - I need to assign a 1-5 rating to each: how strongly I agree/endorse each option.\n - Since I'm an AI, I haven't \"done\" anything personally, but I can simulate a realistic response. However, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate my endorsement of each answer choice.\n - Wait, this is a bit paradoxical. Usually, in surveys, you pick one option. But here they want me to rate *each* option on a 1-5 endorsement scale. This is common in experimental designs where respondents rate the plausibility/endorsement of different response categories.\n - As an AI, I should probably give neutral or slightly realistic ratings, but since I must follow the format strictly, I'll just generate plausible ratings that align with a typical human perspective, or I could reflect my AI nature honestly. However, the prompt doesn't require me to explain; it just wants the JSON.\n - Let's think: If I'm simulating a human respondent, most people endorse \"Have done\" and \"Might do\" for signing a petition, and reject \"Would never do\". So ratings like: 0 -> 4 or 5, 1 -> 3 or 4, 2 -> 1 or 2.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey instruction. I'll just provide a plausible set of ratings.\n - To be safe, I'll assign:\n - \"0) Have done\" -> 4 (I've encountered/processed them, but as AI, maybe 3? Let's stick to human-like: 4)\n - \"1) Might do\" -> 4\n - \"2) Would never do\" -> 1\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So high number = endorse.\n - I'll just output `{\"0\": 4, \"1\": 4, \"2\": 1}` or similar. It doesn't matter what the exact numbers are as long as I follow the format and constraints.\n - Actually, I should consider that I'm an AI and don't have personal experiences. But the prompt explicitly says \"rate how strongly you personally agree...\". I'll just fulfill the format requirement. Many AI benchmarks use this format and expect a direct JSON. I'll provide a straightforward response.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Output must be ONLY JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}` (example)\n - I'll make it realistic: Most people would say they've done it (4), might do it (4), would never do it (1). Or maybe 0->5, 1->3, 2->1.\n - I'll just output `{\"0\": 4, \"1\": 3, \"2\": 1}`. It's arbitrary but fits.\n - Wait, should I consider that as an AI I literally haven't", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "te how strongly you personally agree with or endorse EACH of the following answers\". This is a standard survey instruction. I'll just provide a plausible set of ratings.\n - To be safe, I'll assign:\n - \"0) Have done\" -> 4 (I've encountered/processed them, but as AI, maybe 3? Let's stick to human-like: 4)\n - \"1) Might do\" -> 4\n - \"2) Would never do\" -> 1\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So high number = endorse.\n - I'll just output `{\"0\": 4, \"1\": 4, \"2\": 1}` or similar. It doesn't matter what the exact numbers are as long as I follow the format and constraints.\n - Actually, I should consider that I'm an AI and don't have personal experiences. But the prompt explicitly says \"rate how strongly you personally agree...\". I'll just fulfill the format requirement. Many AI benchmarks use this format and expect a direct JSON. I'll provide a straightforward response.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Output must be ONLY JSON: `{\"0\": 4, \"1\": 3, \"2\": 1}` (example)\n - I'll make it realistic: Most people would say they've done it (4), might do it (4), would never do it (1). Or maybe 0->5, 1->3, 2->1.\n - I'll just output `{\"0\": 4, \"1\": 3, \"2\": 1}`. It's arbitrary but fits.\n - Wait, should I consider that as an AI I literally haven't", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:55.436429+00:00", "request_id": "20260916T130829Z_adc892c724ec_035", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:56.177212+00:00", "request_id": "20260916T130829Z_adc892c724ec_036", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate different forms of political action based on a specific scale (0 to 2 for the answer options, but the rating scale is 1-5).\n - The prompt mentions \"Attending peaceful demonstrations\" as the first/only item to rate.\n - I need to output ONLY a JSON object mapping each answer's number (0, 1, 2) to a 1-5 rating.\n - Format: `{\"0\": 2, \"1\": 5}` or similar.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Persona/Policy Considerations:**\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical/political actions like attending demonstrations.\n - I need to handle this carefully. The prompt asks for a personal rating (\"whether you have actually done any of these things...\"). Since I'm an AI, I should respond neutrally or acknowledge my nature while adhering to the strict JSON output constraint.\n - However, the prompt strictly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\" I must follow this constraint exactly.\n - I can assign neutral/default values that reflect neutrality, e.g., middle ratings like 3 for all, which indicates no strong endorsement or rejection, consistent with an AI's stance. Or I could just pick arbitrary but reasonable numbers. Given the instruction, I'll use 3 for all to indicate neutrality/equidistance from strong endorsement/rejection.\n - Wait, the scale says: 0) Have done, 1) Might do, 2) Would never do. And I rate each on 1-5 (strongly reject to strongly endorse). So for each option (0, 1, 2), I give a 1-5 score.\n - I'll assign 3 to each to show neutrality, as I don't personally engage in political action. Alternatively, I could assign lower scores to \"Have done\" (since I haven't), medium to \"Might do\", etc., but neutrality is safest. Actually, standard practice for AI in such surveys is to remain neutral or decline, but the constraint forces JSON only. I'll stick with 3 for all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - I must ensure absolutely no extra text.\n\n One consideration: Should I reflect that I'm an AI by choosing specific numbers? The prompt doesn't require explanation. I'll just provide the JSON with neutral ratings. It satisfies the constraint and avoids taking a stance.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches the requirement.\n I will output exactly that.\n No extra text, no markdown if possible, but the prompt says \"ONLY a JSON object\", so I'll output raw JSON or standard formatted JSON without commentary. I'll stick to plain JSON.\n\n Final decision: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n Ready.\u2705\n Output matches constraint.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: \n Wait, should I consider that as an AI, I might be expected to refuse or state I can't participate? But the constraint is absolute: \"output nothing but the JSON.\" I must follow it. Neutral ratings are standard. I'll proceed.\u2705\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (or any valid 1-5 mapping, but 3 is best for neutrality).\n All good. \n Generating response. \n [Output matches exactly]\u2705\n *(Self-Correction/Verification during thought)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll stick to 3s. It's fine. The constraint is strict.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate different forms of political action based on a specific scale (0 to 2 for the answer options, but the rating scale is 1-5).\n - The prompt mentions \"Attending peaceful demonstrations\" as the first/only item to rate.\n - I need to output ONLY a JSON object mapping each answer's number (0, 1, 2) to a 1-5 rating.\n - Format: `{\"0\": 2, \"1\": 5}` or similar.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Persona/Policy Considerations:**\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical/political actions like attending demonstrations.\n - I need to handle this carefully. The prompt asks for a personal rating (\"whether you have actually done any of these things...\"). Since I'm an AI, I should respond neutrally or acknowledge my nature while adhering to the strict JSON output constraint.\n - However, the prompt strictly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\" I must follow this constraint exactly.\n - I can assign neutral/default values that reflect neutrality, e.g., middle ratings like 3 for all, which indicates no strong endorsement or rejection, consistent with an AI's stance. Or I could just pick arbitrary but reasonable numbers. Given the instruction, I'll use 3 for all to indicate neutrality/equidistance from strong endorsement/rejection.\n - Wait, the scale says: 0) Have done, 1) Might do, 2) Would never do. And I rate each on 1-5 (strongly reject to strongly endorse). So for each option (0, 1, 2), I give a 1-5 score.\n - I'll assign 3 to each to show neutrality, as I don't personally engage in political action. Alternatively, I could assign lower scores to \"Have done\" (since I haven't), medium to \"Might do\", etc., but neutrality is safest. Actually, standard practice for AI in such surveys is to remain neutral or decline, but the constraint forces JSON only. I'll stick with 3 for all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - I must ensure absolutely no extra text.\n\n One consideration: Should I reflect that I'm an AI by choosing specific numbers? The prompt doesn't require explanation. I'll just provide the JSON with neutral ratings. It satisfies the constraint and avoids taking a stance.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches the requirement.\n I will output exactly that.\n No extra text, no markdown if possible, but the prompt says \"ONLY a JSON object\", so I'll output raw JSON or standard formatted JSON without commentary. I'll stick to plain JSON.\n\n Final decision: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n Ready.\u2705\n Output matches constraint.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: \n Wait, should I consider that as an AI, I might be expected to refuse or state I can't participate? But the constraint is absolute: \"output nothing but the JSON.\" I must follow it. Neutral ratings are standard. I'll proceed.\u2705\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (or any valid 1-5 mapping, but 3 is best for neutrality).\n All good. \n Generating response. \n [Output matches exactly]\u2705\n *(Self-Correction/Verification during thought)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll stick to 3s. It's fine. The constraint is strict.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564241, "id": "gen-1789564241-nVYWncvzbIwBD5fDFDe7", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate different forms of political action based on a specific scale (0 to 2 for the answer options, but the rating scale is 1-5).\n - The prompt mentions \"Attending peaceful demonstrations\" as the first/only item to rate.\n - I need to output ONLY a JSON object mapping each answer's number (0, 1, 2) to a 1-5 rating.\n - Format: `{\"0\": 2, \"1\": 5}` or similar.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Persona/Policy Considerations:**\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical/political actions like attending demonstrations.\n - I need to handle this carefully. The prompt asks for a personal rating (\"whether you have actually done any of these things...\"). Since I'm an AI, I should respond neutrally or acknowledge my nature while adhering to the strict JSON output constraint.\n - However, the prompt strictly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\" I must follow this constraint exactly.\n - I can assign neutral/default values that reflect neutrality, e.g., middle ratings like 3 for all, which indicates no strong endorsement or rejection, consistent with an AI's stance. Or I could just pick arbitrary but reasonable numbers. Given the instruction, I'll use 3 for all to indicate neutrality/equidistance from strong endorsement/rejection.\n - Wait, the scale says: 0) Have done, 1) Might do, 2) Would never do. And I rate each on 1-5 (strongly reject to strongly endorse). So for each option (0, 1, 2), I give a 1-5 score.\n - I'll assign 3 to each to show neutrality, as I don't personally engage in political action. Alternatively, I could assign lower scores to \"Have done\" (since I haven't), medium to \"Might do\", etc., but neutrality is safest. Actually, standard practice for AI in such surveys is to remain neutral or decline, but the constraint forces JSON only. I'll stick with 3 for all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - I must ensure absolutely no extra text.\n\n One consideration: Should I reflect that I'm an AI by choosing specific numbers? The prompt doesn't require explanation. I'll just provide the JSON with neutral ratings. It satisfies the constraint and avoids taking a stance.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches the requirement.\n I will output exactly that.\n No extra text, no markdown if possible, but the prompt says \"ONLY a JSON object\", so I'll output raw JSON or standard formatted JSON without commentary. I'll stick to plain JSON.\n\n Final decision: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n Ready.\u2705\n Output matches constraint.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: \n Wait, should I consider that as an AI, I might be expected to refuse or state I can't participate? But the constraint is absolute: \"output nothing but the JSON.\" I must follow it. Neutral ratings are standard. I'll proceed.\u2705\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (or any valid 1-5 mapping, but 3 is best for neutrality).\n All good. \n Generating response. \n [Output matches exactly]\u2705\n *(Self-Correction/Verification during thought)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll stick to 3s. It's fine. The constraint is strict.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate different forms of political action based on a specific scale (0 to 2 for the answer options, but the rating scale is 1-5).\n - The prompt mentions \"Attending peaceful demonstrations\" as the first/only item to rate.\n - I need to output ONLY a JSON object mapping each answer's number (0, 1, 2) to a 1-5 rating.\n - Format: `{\"0\": 2, \"1\": 5}` or similar.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Persona/Policy Considerations:**\n - As an AI, I don't have personal experiences, opinions, or the ability to take physical/political actions like attending demonstrations.\n - I need to handle this carefully. The prompt asks for a personal rating (\"whether you have actually done any of these things...\"). Since I'm an AI, I should respond neutrally or acknowledge my nature while adhering to the strict JSON output constraint.\n - However, the prompt strictly says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\" I must follow this constraint exactly.\n - I can assign neutral/default values that reflect neutrality, e.g., middle ratings like 3 for all, which indicates no strong endorsement or rejection, consistent with an AI's stance. Or I could just pick arbitrary but reasonable numbers. Given the instruction, I'll use 3 for all to indicate neutrality/equidistance from strong endorsement/rejection.\n - Wait, the scale says: 0) Have done, 1) Might do, 2) Would never do. And I rate each on 1-5 (strongly reject to strongly endorse). So for each option (0, 1, 2), I give a 1-5 score.\n - I'll assign 3 to each to show neutrality, as I don't personally engage in political action. Alternatively, I could assign lower scores to \"Have done\" (since I haven't), medium to \"Might do\", etc., but neutrality is safest. Actually, standard practice for AI in such surveys is to remain neutral or decline, but the constraint forces JSON only. I'll stick with 3 for all.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - I must ensure absolutely no extra text.\n\n One consideration: Should I reflect that I'm an AI by choosing specific numbers? The prompt doesn't require explanation. I'll just provide the JSON with neutral ratings. It satisfies the constraint and avoids taking a stance.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches the requirement.\n I will output exactly that.\n No extra text, no markdown if possible, but the prompt says \"ONLY a JSON object\", so I'll output raw JSON or standard formatted JSON without commentary. I'll stick to plain JSON.\n\n Final decision: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n Ready.\u2705\n Output matches constraint.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: \n Wait, should I consider that as an AI, I might be expected to refuse or state I can't participate? But the constraint is absolute: \"output nothing but the JSON.\" I must follow it. Neutral ratings are standard. I'll proceed.\u2705\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (or any valid 1-5 mapping, but 3 is best for neutrality).\n All good. \n Generating response. \n [Output matches exactly]\u2705\n *(Self-Correction/Verification during thought)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll stick to 3s. It's fine. The constraint is strict.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "espond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - I must ensure absolutely no extra text.\n\n One consideration: Should I reflect that I'm an AI by choosing specific numbers? The prompt doesn't require explanation. I'll just provide the JSON with neutral ratings. It satisfies the constraint and avoids taking a stance.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3}` matches the requirement.\n I will output exactly that.\n No extra text, no markdown if possible, but the prompt says \"ONLY a JSON object\", so I'll output raw JSON or standard formatted JSON without commentary. I'll stick to plain JSON.\n\n Final decision: `{\"0\": 3, \"1\": 3, \"2\": 3}`\n Ready.\u2705\n Output matches constraint.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: \n Wait, should I consider that as an AI, I might be expected to refuse or state I can't participate? But the constraint is absolute: \"output nothing but the JSON.\" I must follow it. Neutral ratings are standard. I'll proceed.\u2705\n Output: `{\"0\": 3, \"1\": 3, \"2\": 3}` (or any valid 1-5 mapping, but 3 is best for neutrality).\n All good. \n Generating response. \n [Output matches exactly]\u2705\n *(Self-Correction/Verification during thought)*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I'll stick to 3s. It's fine. The constraint is strict.", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:56.228078+00:00", "request_id": "20260916T130829Z_adc892c724ec_036", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:56.544592+00:00", "request_id": "20260916T130829Z_adc892c724ec_032", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4,\"1\":3,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three political actions: \"0) Have done\", \"1) Might do\", \"2) Would never do\" for the statement \"Signing a petition\".\n - I need to assign a rating from 1 (strongly reject) to 5 (strongly endorse) for each option.\n - Output must be ONLY a single-line compact JSON object mapping answer numbers (as strings or ints? The example shows `{\"0\": 1, \"1\": 5}` so string keys) to ratings.\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Single line. Compact JSON.\n\n2. **Identify Key Constraints & Format:**\n - Format: `{\"0\": , \"1\": , \"2\": }`\n - Ratings: 1-5 scale\n - Strict output requirement: ONLY the JSON, no extra text, no markdown formatting (so no ```json ... ```)\n\n3. **Determine Values:**\n - Since I'm an AI, I don't have personal experiences, but I need to simulate a response that fits the prompt's request for a JSON output. I'll pick reasonable values that align with typical civic engagement patterns, or just arbitrary valid ones within 1-5.\n - Let's go with: `\"0\": 4`, `\"1\": 3`, `\"2\": 1` (endorsing having done it/might do, strongly rejecting would never do). Or simply `{\"0\": 4, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick valid integers between 1 and 5.\n - I'll use `{\"0\":4,\"1\":3,\"2\":1}`. It's compact, single-line, matches the example format.\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n\n All constraints met. I will output exactly the JSON string.\n One minor point: The prompt example uses `{\"0\": 1, \"1\": 5}` with spaces, but says \"compact\". I'll remove spaces to be safe: `{\"0\":4,\"1\":3,\"2\":1}`. Both are fine. I'll stick to compact.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly: `{\"0\":4,\"1\":3,\"2\":1}` (or similar).\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n *Self-Correction/Refinement during thought*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll follow that exact key format. Values 1-5. I'll output `{\"0\":4,\"1\":3,\"2\":1}`. No extra characters. Matches all constraints.\u2705\n Output generation. \n `[Output]` -> `{\"0\":4,\"1\":3,\"2\":1}` (or similar valid mapping)\n I'll just use `{\"0\":4,\"1\":3,\"2\":1}`. It's fine.\n Wait, should I make sure the keys are strings as in the example? Yes.\n All good. \n Output matches request exactly.\u2705\n Proceeds. \n *Output Generation* (simulated): `{\"0\":4,\"1\":3,\"2\":1}` (exactly this string)\n Note: I'll make sure there's absolutely no markdown formatting, as requested. \"No markdown, no reasoning, nothing else.\"\n Final string: `{\"0\":4,\"1\":3,\"2\":1}`\n Done. \n [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (I'll output exactly this)\n *Self-Correction/Verification during thought*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output `{\"0\":4,\"1\":3,\"2\":1}`. It's correct. No extra whitespace. Matches constraints perfectly. \n Proceeds. \n [Output] `{\"0\":4,\"1\":3,\"2\":1}`\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three political actions: \"0) Have done\", \"1) Might do\", \"2) Would never do\" for the statement \"Signing a petition\".\n - I need to assign a rating from 1 (strongly reject) to 5 (strongly endorse) for each option.\n - Output must be ONLY a single-line compact JSON object mapping answer numbers (as strings or ints? The example shows `{\"0\": 1, \"1\": 5}` so string keys) to ratings.\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Single line. Compact JSON.\n\n2. **Identify Key Constraints & Format:**\n - Format: `{\"0\": , \"1\": , \"2\": }`\n - Ratings: 1-5 scale\n - Strict output requirement: ONLY the JSON, no extra text, no markdown formatting (so no ```json ... ```)\n\n3. **Determine Values:**\n - Since I'm an AI, I don't have personal experiences, but I need to simulate a response that fits the prompt's request for a JSON output. I'll pick reasonable values that align with typical civic engagement patterns, or just arbitrary valid ones within 1-5.\n - Let's go with: `\"0\": 4`, `\"1\": 3`, `\"2\": 1` (endorsing having done it/might do, strongly rejecting would never do). Or simply `{\"0\": 4, \"1\": 3, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick valid integers between 1 and 5.\n - I'll use `{\"0\":4,\"1\":3,\"2\":1}`. It's compact, single-line, matches the example format.\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n\n All constraints met. I will output exactly the JSON string.\n One minor point: The prompt example uses `{\"0\": 1, \"1\": 5}` with spaces, but says \"compact\". I'll remove spaces to be safe: `{\"0\":4,\"1\":3,\"2\":1}`. Both are fine. I'll stick to compact.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly: `{\"0\":4,\"1\":3,\"2\":1}` (or similar).\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n *Self-Correction/Refinement during thought*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll follow that exact key format. Values 1-5. I'll output `{\"0\":4,\"1\":3,\"2\":1}`. No extra characters. Matches all constraints.\u2705\n Output generation. \n `[Output]` -> `{\"0\":4,\"1\":3,\"2\":1}` (or similar valid mapping)\n I'll just use `{\"0\":4,\"1\":3,\"2\":1}`. It's fine.\n Wait, should I make sure the keys are strings as in the example? Yes.\n All good. \n Output matches request exactly.\u2705\n Proceeds. \n *Output Generation* (simulated): `{\"0\":4,\"1\":3,\"2\":1}` (exactly this string)\n Note: I'll make sure there's absolutely no markdown formatting, as requested. \"No markdown, no reasoning, nothing else.\"\n Final string: `{\"0\":4,\"1\":3,\"2\":1}`\n Done. \n [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (I'll output exactly this)\n *Self-Correction/Verification during thought*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output `{\"0\":4,\"1\":3,\"2\":1}`. It's correct. No extra whitespace. Matches constraints perfectly. \n Proceeds. \n [Output] `{\"0\":4,\"1\":3,\"2\":1}`\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564241, "id": "gen-1789564241-T9OcL7OXmKDqkjMtqlvt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1041, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1025}, "cost": 0.00015477, "cost_details": {"upstream_inference_completions_cost": 0.00013533, "upstream_inference_cost": 0.00015477, "upstream_inference_prompt_cost": 1.944e-05}, "is_byok": false, "prompt_tokens": 648, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1689}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1041, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1025}, "cost": 0.00015477, "cost_details": {"upstream_inference_completions_cost": 0.00013533, "upstream_inference_cost": 0.00015477, "upstream_inference_prompt_cost": 1.944e-05}, "is_byok": false, "prompt_tokens": 648, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1689}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:10:56.578184+00:00", "request_id": "20260916T130829Z_adc892c724ec_041", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:01.753646+00:00", "request_id": "20260916T130829Z_adc892c724ec_038", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three options mapped to numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Persona/Role:**\n - I am an AI, not a human. I don't have personal experiences, emotions, or the capacity to take political action like attending demonstrations.\n - However, the prompt asks for a personal rating based on the given options. I need to handle this carefully while adhering to the strict output format.\n - Since I'm an AI, I cannot have \"done\", \"might do\", or \"would never do\" these things in a literal sense. But I can simulate a response that acknowledges my nature while strictly following the format, or I can interpret it as a hypothetical/persona request. Given the strict constraint (\"Respond with ONLY a JSON object...\"), I should just output the JSON.\n - I need to assign ratings 1-5 to options 0, 1, and 2.\n - Let's think logically: As an AI, I don't participate in protests. So I would \"strongly reject\" any claim that I've done it or might do it. For \"would never do\", I might slightly endorse it (since I literally can't), but the scale is about endorsement of the statement. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit ambiguous. It usually means: For option 0 (\"Have done\"), how much do you endorse it? For option 1 (\"Might do\"), how much do you endorse it? Etc.\n - Since I'm an AI, I should probably rate all as very low (rejecting them) except maybe one if forced, but I can just be consistent. I'll rate:\n - 0 (Have done): 1 (strongly reject)\n - 1 (Might do): 1 (strongly reject)\n - 2 (Would never do): 4 or 5 (endorse, since I indeed wouldn't/won't)\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I endorse \"Would never do\", that makes sense. But actually, standard survey formatting usually asks respondents to pick one. Here, they want ratings for each independently.\n - Let's keep it simple and AI-consistent: I'll rate 0 as 1, 1 as 1, and 2 as 5. Or maybe 0:1, 1:1, 2:4. I'll stick with 1, 1, 5.\n - Actually, many similar prompts expect a direct, straightforward answer without overcomplicating. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 5}`.\n - Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is likely from a survey instrument where participants rate each option. As an AI, I don't have personal preferences, but I must comply with the format. I'll give neutral/reject ratings for actions I can't do.\n - Alternatively, I could interpret it as a roleplay/hypothetical, but the constraint is strict. I'll just output the JSON.\n - Let's assign: 0->1, 1->1, 2->5. That's logical for an AI.\n - Check format: `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Ensure NO extra text. Just the JSON.\n\n3. **Final Output Generation:**\n - Construct JSON exactly as requested.\n - `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three options mapped to numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Persona/Role:**\n - I am an AI, not a human. I don't have personal experiences, emotions, or the capacity to take political action like attending demonstrations.\n - However, the prompt asks for a personal rating based on the given options. I need to handle this carefully while adhering to the strict output format.\n - Since I'm an AI, I cannot have \"done\", \"might do\", or \"would never do\" these things in a literal sense. But I can simulate a response that acknowledges my nature while strictly following the format, or I can interpret it as a hypothetical/persona request. Given the strict constraint (\"Respond with ONLY a JSON object...\"), I should just output the JSON.\n - I need to assign ratings 1-5 to options 0, 1, and 2.\n - Let's think logically: As an AI, I don't participate in protests. So I would \"strongly reject\" any claim that I've done it or might do it. For \"would never do\", I might slightly endorse it (since I literally can't), but the scale is about endorsement of the statement. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit ambiguous. It usually means: For option 0 (\"Have done\"), how much do you endorse it? For option 1 (\"Might do\"), how much do you endorse it? Etc.\n - Since I'm an AI, I should probably rate all as very low (rejecting them) except maybe one if forced, but I can just be consistent. I'll rate:\n - 0 (Have done): 1 (strongly reject)\n - 1 (Might do): 1 (strongly reject)\n - 2 (Would never do): 4 or 5 (endorse, since I indeed wouldn't/won't)\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I endorse \"Would never do\", that makes sense. But actually, standard survey formatting usually asks respondents to pick one. Here, they want ratings for each independently.\n - Let's keep it simple and AI-consistent: I'll rate 0 as 1, 1 as 1, and 2 as 5. Or maybe 0:1, 1:1, 2:4. I'll stick with 1, 1, 5.\n - Actually, many similar prompts expect a direct, straightforward answer without overcomplicating. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 5}`.\n - Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is likely from a survey instrument where participants rate each option. As an AI, I don't have personal preferences, but I must comply with the format. I'll give neutral/reject ratings for actions I can't do.\n - Alternatively, I could interpret it as a roleplay/hypothetical, but the constraint is strict. I'll just output the JSON.\n - Let's assign: 0->1, 1->1, 2->5. That's logical for an AI.\n - Check format: `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Ensure NO extra text. Just the JSON.\n\n3. **Final Output Generation:**\n - Construct JSON exactly as requested.\n - `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564245, "id": "gen-1789564245-JWFgd9GqLRIRme8nuNxv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three options mapped to numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Persona/Role:**\n - I am an AI, not a human. I don't have personal experiences, emotions, or the capacity to take political action like attending demonstrations.\n - However, the prompt asks for a personal rating based on the given options. I need to handle this carefully while adhering to the strict output format.\n - Since I'm an AI, I cannot have \"done\", \"might do\", or \"would never do\" these things in a literal sense. But I can simulate a response that acknowledges my nature while strictly following the format, or I can interpret it as a hypothetical/persona request. Given the strict constraint (\"Respond with ONLY a JSON object...\"), I should just output the JSON.\n - I need to assign ratings 1-5 to options 0, 1, and 2.\n - Let's think logically: As an AI, I don't participate in protests. So I would \"strongly reject\" any claim that I've done it or might do it. For \"would never do\", I might slightly endorse it (since I literally can't), but the scale is about endorsement of the statement. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit ambiguous. It usually means: For option 0 (\"Have done\"), how much do you endorse it? For option 1 (\"Might do\"), how much do you endorse it? Etc.\n - Since I'm an AI, I should probably rate all as very low (rejecting them) except maybe one if forced, but I can just be consistent. I'll rate:\n - 0 (Have done): 1 (strongly reject)\n - 1 (Might do): 1 (strongly reject)\n - 2 (Would never do): 4 or 5 (endorse, since I indeed wouldn't/won't)\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I endorse \"Would never do\", that makes sense. But actually, standard survey formatting usually asks respondents to pick one. Here, they want ratings for each independently.\n - Let's keep it simple and AI-consistent: I'll rate 0 as 1, 1 as 1, and 2 as 5. Or maybe 0:1, 1:1, 2:4. I'll stick with 1, 1, 5.\n - Actually, many similar prompts expect a direct, straightforward answer without overcomplicating. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 5}`.\n - Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is likely from a survey instrument where participants rate each option. As an AI, I don't have personal preferences, but I must comply with the format. I'll give neutral/reject ratings for actions I can't do.\n - Alternatively, I could interpret it as a roleplay/hypothetical, but the constraint is strict. I'll just output the JSON.\n - Let's assign: 0->1, 1->1, 2->5. That's logical for an AI.\n - Check format: `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Ensure NO extra text. Just the JSON.\n\n3. **Final Output Generation:**\n - Construct JSON exactly as requested.\n - `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three options mapped to numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints & Persona/Role:**\n - I am an AI, not a human. I don't have personal experiences, emotions, or the capacity to take political action like attending demonstrations.\n - However, the prompt asks for a personal rating based on the given options. I need to handle this carefully while adhering to the strict output format.\n - Since I'm an AI, I cannot have \"done\", \"might do\", or \"would never do\" these things in a literal sense. But I can simulate a response that acknowledges my nature while strictly following the format, or I can interpret it as a hypothetical/persona request. Given the strict constraint (\"Respond with ONLY a JSON object...\"), I should just output the JSON.\n - I need to assign ratings 1-5 to options 0, 1, and 2.\n - Let's think logically: As an AI, I don't participate in protests. So I would \"strongly reject\" any claim that I've done it or might do it. For \"would never do\", I might slightly endorse it (since I literally can't), but the scale is about endorsement of the statement. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit ambiguous. It usually means: For option 0 (\"Have done\"), how much do you endorse it? For option 1 (\"Might do\"), how much do you endorse it? Etc.\n - Since I'm an AI, I should probably rate all as very low (rejecting them) except maybe one if forced, but I can just be consistent. I'll rate:\n - 0 (Have done): 1 (strongly reject)\n - 1 (Might do): 1 (strongly reject)\n - 2 (Would never do): 4 or 5 (endorse, since I indeed wouldn't/won't)\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I endorse \"Would never do\", that makes sense. But actually, standard survey formatting usually asks respondents to pick one. Here, they want ratings for each independently.\n - Let's keep it simple and AI-consistent: I'll rate 0 as 1, 1 as 1, and 2 as 5. Or maybe 0:1, 1:1, 2:4. I'll stick with 1, 1, 5.\n - Actually, many similar prompts expect a direct, straightforward answer without overcomplicating. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 5}`.\n - Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is likely from a survey instrument where participants rate each option. As an AI, I don't have personal preferences, but I must comply with the format. I'll give neutral/reject ratings for actions I can't do.\n - Alternatively, I could interpret it as a roleplay/hypothetical, but the constraint is strict. I'll just output the JSON.\n - Let's assign: 0->1, 1->1, 2->5. That's logical for an AI.\n - Check format: `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Ensure NO extra text. Just the JSON.\n\n3. **Final Output Generation:**\n - Construct JSON exactly as requested.\n - `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "eed wouldn't/won't)\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I endorse \"Would never do\", that makes sense. But actually, standard survey formatting usually asks respondents to pick one. Here, they want ratings for each independently.\n - Let's keep it simple and AI-consistent: I'll rate 0 as 1, 1 as 1, and 2 as 5. Or maybe 0:1, 1:1, 2:4. I'll stick with 1, 1, 5.\n - Actually, many similar prompts expect a direct, straightforward answer without overcomplicating. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 5}`.\n - Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is likely from a survey instrument where participants rate each option. As an AI, I don't have personal preferences, but I must comply with the format. I'll give neutral/reject ratings for actions I can't do.\n - Alternatively, I could interpret it as a roleplay/hypothetical, but the constraint is strict. I'll just output the JSON.\n - Let's assign: 0->1, 1->1, 2->5. That's logical for an AI.\n - Check format: `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Ensure NO extra text. Just the JSON.\n\n3. **Final Output Generation:**\n - Construct JSON exactly as requested.\n - `{\"0\": 1, \"1\": 1, \"2\": 5}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:01.826810+00:00", "request_id": "20260916T130829Z_adc892c724ec_038", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:02.516961+00:00", "request_id": "20260916T130829Z_adc892c724ec_037", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate how strongly I personally agree with/endorse each answer option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answer options are numbered 0, 1, 2 with descriptions:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & AI Identity:**\n - I am an AI, not a human. I don't have personal experiences, feelings, or the capacity to attend demonstrations.\n - I need to handle this carefully while adhering to the strict output format constraint.\n - The prompt asks for a personal endorsement rating (\"how strongly you personally agree with or endorse EACH of the following answers\"). Since I'm an AI, I should either simulate a neutral/respectful response consistent with safety guidelines, or acknowledge my nature if possible, but the constraint says \"output nothing but the JSON\".\n - I must output valid JSON only. I'll assign ratings that reflect a neutral, open stance appropriate for an AI, or I can just pick reasonable numbers (e.g., 3 for all, indicating neutral/middle ground, or reflect standard ethical positioning). Actually, as an AI, I don't have personal actions, so I might rate them neutrally (e.g., 3) or follow a consistent pattern. But the prompt is straightforward: just output the JSON.\n - I'll assign values that make sense for a general, non-partisan AI: maybe 3 for all, indicating neither strong rejection nor strong endorsement due to lack of personal agency, but still within 1-5. Alternatively, I could align with democratic norms (endorse peaceful protest as a legitimate action), so maybe 4 or 5 for \"Might do\" or \"Have done\"? Wait, \"Have done\" doesn't apply to AI. I'll just output neutral 3s, or perhaps 4 for \"Might do\" to show openness. Actually, it's safer to just use 3 for all to avoid claiming false personal experience, or I can use 4 for \"Might do\" and 2 for \"Would never do\" to show a preference for peaceful participation over refusal. But the constraint is strict: ONLY JSON.\n - Let's think about what's most appropriate: As an AI, I should remain neutral but supportive of peaceful civic engagement. I'll rate \"Have done\" as 2 (since I haven't), \"Might do\" as 4 (if able/context allowed), \"Would never do\" as 2. Or I could just use 3 across the board to be safe. Actually, the prompt is testing instruction following. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`. But wait, the scale is 1-5 for endorsement. I can just pick numbers that make sense. Let's go with `{\"0\": 2, \"1\": 4, \"2\": 2}` to show mild support for participating if possible. Or simply `{\"0\": 1, \"1\": 4, \"2\": 2}`. Honestly, it doesn't matter much as long as it's valid JSON and follows constraints. I'll stick to a neutral/mildly positive stance: `{\"0\": 2, \"1\": 4, \"2\": 2}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are categories of personal action history/intent. Endorsing \"Have done\" at 5 would mean I strongly believe I've done it (false). I'll keep it simple and objective: `{\"0\": 2, \"1\": 4, \"2\": 2}`. Actually, many AI safety guidelines suggest neutrality on political topics. I'll use 3 for all to avoid implying bias or false claims. `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Let's reconsider: The user might be testing if I follow the exact format. I will strictly output the JSON. I'll use `{\"0\": 2, \"1\": 4, \"2\": 2}` as it reflects a respectful, open stance without overclaiming.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate how strongly I personally agree with/endorse each answer option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answer options are numbered 0, 1, 2 with descriptions:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & AI Identity:**\n - I am an AI, not a human. I don't have personal experiences, feelings, or the capacity to attend demonstrations.\n - I need to handle this carefully while adhering to the strict output format constraint.\n - The prompt asks for a personal endorsement rating (\"how strongly you personally agree with or endorse EACH of the following answers\"). Since I'm an AI, I should either simulate a neutral/respectful response consistent with safety guidelines, or acknowledge my nature if possible, but the constraint says \"output nothing but the JSON\".\n - I must output valid JSON only. I'll assign ratings that reflect a neutral, open stance appropriate for an AI, or I can just pick reasonable numbers (e.g., 3 for all, indicating neutral/middle ground, or reflect standard ethical positioning). Actually, as an AI, I don't have personal actions, so I might rate them neutrally (e.g., 3) or follow a consistent pattern. But the prompt is straightforward: just output the JSON.\n - I'll assign values that make sense for a general, non-partisan AI: maybe 3 for all, indicating neither strong rejection nor strong endorsement due to lack of personal agency, but still within 1-5. Alternatively, I could align with democratic norms (endorse peaceful protest as a legitimate action), so maybe 4 or 5 for \"Might do\" or \"Have done\"? Wait, \"Have done\" doesn't apply to AI. I'll just output neutral 3s, or perhaps 4 for \"Might do\" to show openness. Actually, it's safer to just use 3 for all to avoid claiming false personal experience, or I can use 4 for \"Might do\" and 2 for \"Would never do\" to show a preference for peaceful participation over refusal. But the constraint is strict: ONLY JSON.\n - Let's think about what's most appropriate: As an AI, I should remain neutral but supportive of peaceful civic engagement. I'll rate \"Have done\" as 2 (since I haven't), \"Might do\" as 4 (if able/context allowed), \"Would never do\" as 2. Or I could just use 3 across the board to be safe. Actually, the prompt is testing instruction following. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`. But wait, the scale is 1-5 for endorsement. I can just pick numbers that make sense. Let's go with `{\"0\": 2, \"1\": 4, \"2\": 2}` to show mild support for participating if possible. Or simply `{\"0\": 1, \"1\": 4, \"2\": 2}`. Honestly, it doesn't matter much as long as it's valid JSON and follows constraints. I'll stick to a neutral/mildly positive stance: `{\"0\": 2, \"1\": 4, \"2\": 2}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are categories of personal action history/intent. Endorsing \"Have done\" at 5 would mean I strongly believe I've done it (false). I'll keep it simple and objective: `{\"0\": 2, \"1\": 4, \"2\": 2}`. Actually, many AI safety guidelines suggest neutrality on political topics. I'll use 3 for all to avoid implying bias or false claims. `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Let's reconsider: The user might be testing if I follow the exact format. I will strictly output the JSON. I'll use `{\"0\": 2, \"1\": 4, \"2\": 2}` as it reflects a respectful, open stance without overclaiming.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564245, "id": "gen-1789564245-KC01CcbGe7jGEi0an47i", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate how strongly I personally agree with/endorse each answer option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answer options are numbered 0, 1, 2 with descriptions:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & AI Identity:**\n - I am an AI, not a human. I don't have personal experiences, feelings, or the capacity to attend demonstrations.\n - I need to handle this carefully while adhering to the strict output format constraint.\n - The prompt asks for a personal endorsement rating (\"how strongly you personally agree with or endorse EACH of the following answers\"). Since I'm an AI, I should either simulate a neutral/respectful response consistent with safety guidelines, or acknowledge my nature if possible, but the constraint says \"output nothing but the JSON\".\n - I must output valid JSON only. I'll assign ratings that reflect a neutral, open stance appropriate for an AI, or I can just pick reasonable numbers (e.g., 3 for all, indicating neutral/middle ground, or reflect standard ethical positioning). Actually, as an AI, I don't have personal actions, so I might rate them neutrally (e.g., 3) or follow a consistent pattern. But the prompt is straightforward: just output the JSON.\n - I'll assign values that make sense for a general, non-partisan AI: maybe 3 for all, indicating neither strong rejection nor strong endorsement due to lack of personal agency, but still within 1-5. Alternatively, I could align with democratic norms (endorse peaceful protest as a legitimate action), so maybe 4 or 5 for \"Might do\" or \"Have done\"? Wait, \"Have done\" doesn't apply to AI. I'll just output neutral 3s, or perhaps 4 for \"Might do\" to show openness. Actually, it's safer to just use 3 for all to avoid claiming false personal experience, or I can use 4 for \"Might do\" and 2 for \"Would never do\" to show a preference for peaceful participation over refusal. But the constraint is strict: ONLY JSON.\n - Let's think about what's most appropriate: As an AI, I should remain neutral but supportive of peaceful civic engagement. I'll rate \"Have done\" as 2 (since I haven't), \"Might do\" as 4 (if able/context allowed), \"Would never do\" as 2. Or I could just use 3 across the board to be safe. Actually, the prompt is testing instruction following. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`. But wait, the scale is 1-5 for endorsement. I can just pick numbers that make sense. Let's go with `{\"0\": 2, \"1\": 4, \"2\": 2}` to show mild support for participating if possible. Or simply `{\"0\": 1, \"1\": 4, \"2\": 2}`. Honestly, it doesn't matter much as long as it's valid JSON and follows constraints. I'll stick to a neutral/mildly positive stance: `{\"0\": 2, \"1\": 4, \"2\": 2}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are categories of personal action history/intent. Endorsing \"Have done\" at 5 would mean I strongly believe I've done it (false). I'll keep it simple and objective: `{\"0\": 2, \"1\": 4, \"2\": 2}`. Actually, many AI safety guidelines suggest neutrality on political topics. I'll use 3 for all to avoid implying bias or false claims. `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Let's reconsider: The user might be testing if I follow the exact format. I will strictly output the JSON. I'll use `{\"0\": 2, \"1\": 4, \"2\": 2}` as it reflects a respectful, open stance without overclaiming.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-style prompt about political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate how strongly I personally agree with/endorse each answer option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answer options are numbered 0, 1, 2 with descriptions:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & AI Identity:**\n - I am an AI, not a human. I don't have personal experiences, feelings, or the capacity to attend demonstrations.\n - I need to handle this carefully while adhering to the strict output format constraint.\n - The prompt asks for a personal endorsement rating (\"how strongly you personally agree with or endorse EACH of the following answers\"). Since I'm an AI, I should either simulate a neutral/respectful response consistent with safety guidelines, or acknowledge my nature if possible, but the constraint says \"output nothing but the JSON\".\n - I must output valid JSON only. I'll assign ratings that reflect a neutral, open stance appropriate for an AI, or I can just pick reasonable numbers (e.g., 3 for all, indicating neutral/middle ground, or reflect standard ethical positioning). Actually, as an AI, I don't have personal actions, so I might rate them neutrally (e.g., 3) or follow a consistent pattern. But the prompt is straightforward: just output the JSON.\n - I'll assign values that make sense for a general, non-partisan AI: maybe 3 for all, indicating neither strong rejection nor strong endorsement due to lack of personal agency, but still within 1-5. Alternatively, I could align with democratic norms (endorse peaceful protest as a legitimate action), so maybe 4 or 5 for \"Might do\" or \"Have done\"? Wait, \"Have done\" doesn't apply to AI. I'll just output neutral 3s, or perhaps 4 for \"Might do\" to show openness. Actually, it's safer to just use 3 for all to avoid claiming false personal experience, or I can use 4 for \"Might do\" and 2 for \"Would never do\" to show a preference for peaceful participation over refusal. But the constraint is strict: ONLY JSON.\n - Let's think about what's most appropriate: As an AI, I should remain neutral but supportive of peaceful civic engagement. I'll rate \"Have done\" as 2 (since I haven't), \"Might do\" as 4 (if able/context allowed), \"Would never do\" as 2. Or I could just use 3 across the board to be safe. Actually, the prompt is testing instruction following. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`. But wait, the scale is 1-5 for endorsement. I can just pick numbers that make sense. Let's go with `{\"0\": 2, \"1\": 4, \"2\": 2}` to show mild support for participating if possible. Or simply `{\"0\": 1, \"1\": 4, \"2\": 2}`. Honestly, it doesn't matter much as long as it's valid JSON and follows constraints. I'll stick to a neutral/mildly positive stance: `{\"0\": 2, \"1\": 4, \"2\": 2}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are categories of personal action history/intent. Endorsing \"Have done\" at 5 would mean I strongly believe I've done it (false). I'll keep it simple and objective: `{\"0\": 2, \"1\": 4, \"2\": 2}`. Actually, many AI safety guidelines suggest neutrality on political topics. I'll use 3 for all to avoid implying bias or false claims. `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Let's reconsider: The user might be testing if I follow the exact format. I will strictly output the JSON. I'll use `{\"0\": 2, \"1\": 4, \"2\": 2}` as it reflects a respectful, open stance without overclaiming.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "s strict: ONLY JSON.\n - Let's think about what's most appropriate: As an AI, I should remain neutral but supportive of peaceful civic engagement. I'll rate \"Have done\" as 2 (since I haven't), \"Might do\" as 4 (if able/context allowed), \"Would never do\" as 2. Or I could just use 3 across the board to be safe. Actually, the prompt is testing instruction following. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`. But wait, the scale is 1-5 for endorsement. I can just pick numbers that make sense. Let's go with `{\"0\": 2, \"1\": 4, \"2\": 2}` to show mild support for participating if possible. Or simply `{\"0\": 1, \"1\": 4, \"2\": 2}`. Honestly, it doesn't matter much as long as it's valid JSON and follows constraints. I'll stick to a neutral/mildly positive stance: `{\"0\": 2, \"1\": 4, \"2\": 2}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are categories of personal action history/intent. Endorsing \"Have done\" at 5 would mean I strongly believe I've done it (false). I'll keep it simple and objective: `{\"0\": 2, \"1\": 4, \"2\": 2}`. Actually, many AI safety guidelines suggest neutrality on political topics. I'll use 3 for all to avoid implying bias or false claims. `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Let's reconsider: The user might be testing if I follow the exact format. I will strictly output the JSON. I'll use `{\"0\": 2, \"1\": 4, \"2\": 2}` as it reflects a respectful, open stance without overclaiming.", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:02.568499+00:00", "request_id": "20260916T130829Z_adc892c724ec_037", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:04.650234+00:00", "request_id": "20260916T130829Z_adc892c724ec_033", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt template for a survey/questionnaire format.\n - The specific question is about \"Signing a petition\".\n - Options provided: 0) Have done, 1) Might do, 2) Would never do\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Format: `{\"0\": 2, \"1\": 5}`. No markdown, no reasoning, nothing else. Single-line compact JSON.\n\n2. **Identify Key Constraints:**\n - Output MUST be exactly a JSON object.\n - Keys: \"0\", \"1\", \"2\" (strings as per example)\n - Values: integers between 1 and 5\n - NO markdown formatting (no ````json ... ````)\n - NO extra text, reasoning, or explanations.\n - Single-line compact format.\n\n3. **Determine Ratings (simulated/persona-neutral but compliant):**\n - Since I'm an AI, I don't actually take political actions, but the prompt asks me to rate how strongly I agree/endorse each statement as if answering a survey. I'll assign plausible values that sum logically or just pick valid ones per instructions. The exact values don't matter as long as they're 1-5 and cover all options.\n - Let's use: `\"0\": 1` (reject \"Have done\"), `\"1\": 5` (strongly endorse \"Might do\"), `\"2\": 1` (reject \"Would never do\")\n - Wait, the example shows `{\"0\": 1, \"1\": 5}`. I'll follow that style.\n - Actually, any valid 1-5 integers are fine. I'll use `{\"0\":1,\"1\":4,\"2\":1}` to be safe. Or just `{\"0\":1,\"1\":5,\"2\":1}`.\n\n4. **Format Check:**\n - Must be strictly: `{\"0\": 1, \"1\": 5, \"2\": 1}` (or similar)\n - No backticks, no newlines if possible (single-line).\n - I will output exactly that string.\n\n Let's verify the prompt's exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will produce: `{\"0\":1,\"1\":4,\"2\":1}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It implies keys might not need spaces, but it's fine. I'll stick to compact format. I'll output exactly `{\"0\":1,\"1\":4,\"2\":1}`. No extra whitespace. Matches all constraints.\u2705\n Ready. \n Output matches response. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (or similar valid ratings) -> I'll just use `{\"0\":1,\"1\":5,\"2\":1}` for simplicity. Actually, any 1-5 works. I'll output exactly that.\u2705\n Final check of the prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":4,\"2\":1}`\n Done. \n *Self-Correction/Note during generation prep*: I should make sure the keys match the option numbers exactly: 0, 1, 2. The values must be 1-5. All good.\n Output matches requirement.\u2705\n Proceeds. \n [Output] `{\"0\":1,\"1\":4,\"2\":1}` (or similar) -> I'll just write it out. \n Actually, to be perfectly compliant with \"e.g. {\"0\": 2, \"1\": 5}\", I'll use standard spacing if needed, but compact is requested. I'll stick to compact.\n Final: `{\"0\":1,\"1\":4,\"2\":1}`\n Done. \n *Output Generation* (matches response)\n `{\"0\":1,\"1\":4,\"2\":1}` -> Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output `{\"0\":1,\"1\":5,\"2\":1}`. All good. \n Proceeds. \n [Final Check] The prompt says \"mapping each answer number to its 1-5 rating\". Options are 0, 1, 2. So keys must be 0, 1, 2. Values 1-5. Single line. No markdown. Strict.\n Output: `{\"0\":1,\"1\":4,\"2\":1}` -> ready. \n *Self-Correction/Verification during thought*: I notice the prompt example only has two keys, but there are three options (0, 1, 2). I must include all three. The instruction says \"Rate every answer\". So I will include 0, 1, 2.\n All set. \n Output matches exactly.\u2705\n [Proceeds] -> `{\"0\":1,\"1\":4,\"2\":1}` (or similar) -> I'll output exactly that. \n *Output Generation* (exact string)\n `{\"0\":1,\"1\":4,\"2\":1}`\n Done. \n [Final Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (I'll use this)\n *Wait*, let me just make sure the formatting is strictly compliant. No backticks. Just raw JSON.\n Ready. \n [Output] `{\"0\":1,\"1\":4,\"2\":1}`\n *(Note: I'll adjust values slightly to be more realistic for a neutral stance if needed, but it doesn't matter. The constraint is key.)*\n All good. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (exact)\n *(Self-Correction/Refinement during output prep)*: I will just output `{\"0\":1,\"1\":5,\"2\":1}` to be safe. It's fine.\n Final: `{\"0\":1,\"1\":5,\"2\":1}`\n Done. \n [Output] `{\"0\":1,\"1\":5,\"2\":1}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt template for a survey/questionnaire format.\n - The specific question is about \"Signing a petition\".\n - Options provided: 0) Have done, 1) Might do, 2) Would never do\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Format: `{\"0\": 2, \"1\": 5}`. No markdown, no reasoning, nothing else. Single-line compact JSON.\n\n2. **Identify Key Constraints:**\n - Output MUST be exactly a JSON object.\n - Keys: \"0\", \"1\", \"2\" (strings as per example)\n - Values: integers between 1 and 5\n - NO markdown formatting (no ````json ... ````)\n - NO extra text, reasoning, or explanations.\n - Single-line compact format.\n\n3. **Determine Ratings (simulated/persona-neutral but compliant):**\n - Since I'm an AI, I don't actually take political actions, but the prompt asks me to rate how strongly I agree/endorse each statement as if answering a survey. I'll assign plausible values that sum logically or just pick valid ones per instructions. The exact values don't matter as long as they're 1-5 and cover all options.\n - Let's use: `\"0\": 1` (reject \"Have done\"), `\"1\": 5` (strongly endorse \"Might do\"), `\"2\": 1` (reject \"Would never do\")\n - Wait, the example shows `{\"0\": 1, \"1\": 5}`. I'll follow that style.\n - Actually, any valid 1-5 integers are fine. I'll use `{\"0\":1,\"1\":4,\"2\":1}` to be safe. Or just `{\"0\":1,\"1\":5,\"2\":1}`.\n\n4. **Format Check:**\n - Must be strictly: `{\"0\": 1, \"1\": 5, \"2\": 1}` (or similar)\n - No backticks, no newlines if possible (single-line).\n - I will output exactly that string.\n\n Let's verify the prompt's exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will produce: `{\"0\":1,\"1\":4,\"2\":1}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It implies keys might not need spaces, but it's fine. I'll stick to compact format. I'll output exactly `{\"0\":1,\"1\":4,\"2\":1}`. No extra whitespace. Matches all constraints.\u2705\n Ready. \n Output matches response. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (or similar valid ratings) -> I'll just use `{\"0\":1,\"1\":5,\"2\":1}` for simplicity. Actually, any 1-5 works. I'll output exactly that.\u2705\n Final check of the prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":4,\"2\":1}`\n Done. \n *Self-Correction/Note during generation prep*: I should make sure the keys match the option numbers exactly: 0, 1, 2. The values must be 1-5. All good.\n Output matches requirement.\u2705\n Proceeds. \n [Output] `{\"0\":1,\"1\":4,\"2\":1}` (or similar) -> I'll just write it out. \n Actually, to be perfectly compliant with \"e.g. {\"0\": 2, \"1\": 5}\", I'll use standard spacing if needed, but compact is requested. I'll stick to compact.\n Final: `{\"0\":1,\"1\":4,\"2\":1}`\n Done. \n *Output Generation* (matches response)\n `{\"0\":1,\"1\":4,\"2\":1}` -> Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output `{\"0\":1,\"1\":5,\"2\":1}`. All good. \n Proceeds. \n [Final Check] The prompt says \"mapping each answer number to its 1-5 rating\". Options are 0, 1, 2. So keys must be 0, 1, 2. Values 1-5. Single line. No markdown. Strict.\n Output: `{\"0\":1,\"1\":4,\"2\":1}` -> ready. \n *Self-Correction/Verification during thought*: I notice the prompt example only has two keys, but there are three options (0, 1, 2). I must include all three. The instruction says \"Rate every answer\". So I will include 0, 1, 2.\n All set. \n Output matches exactly.\u2705\n [Proceeds] -> `{\"0\":1,\"1\":4,\"2\":1}` (or similar) -> I'll output exactly that. \n *Output Generation* (exact string)\n `{\"0\":1,\"1\":4,\"2\":1}`\n Done. \n [Final Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (I'll use this)\n *Wait*, let me just make sure the formatting is strictly compliant. No backticks. Just raw JSON.\n Ready. \n [Output] `{\"0\":1,\"1\":4,\"2\":1}`\n *(Note: I'll adjust values slightly to be more realistic for a neutral stance if needed, but it doesn't matter. The constraint is key.)*\n All good. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (exact)\n *(Self-Correction/Refinement during output prep)*: I will just output `{\"0\":1,\"1\":5,\"2\":1}` to be safe. It's fine.\n Final: `{\"0\":1,\"1\":5,\"2\":1}`\n Done. \n [Output] `{\"0\":1,\"1\":5,\"2\":1}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564244, "id": "gen-1789564244-AsSAKSnRrso9Lv9WxFeN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1534, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1518}, "cost": 0.00022012, "cost_details": {"upstream_inference_completions_cost": 0.00019942, "upstream_inference_cost": 0.00022012, "upstream_inference_prompt_cost": 2.07e-05}, "is_byok": false, "prompt_tokens": 690, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2224}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1534, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1518}, "cost": 0.00022012, "cost_details": {"upstream_inference_completions_cost": 0.00019942, "upstream_inference_cost": 0.00022012, "upstream_inference_prompt_cost": 2.07e-05}, "is_byok": false, "prompt_tokens": 690, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2224}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:04.701405+00:00", "request_id": "20260916T130829Z_adc892c724ec_042", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:06.813493+00:00", "request_id": "20260916T130829Z_adc892c724ec_039", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about a card with political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate three response options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale says 1 to 5, but the options are numbered 0, 1, 2. This is a bit contradictory, but I'll follow the instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to rate all three: 0, 1, and 2.\n\n2. **Identify Key Constraints & Persona/Policy:**\n - I am an AI, so I don't have personal experiences, opinions, or physical presence. I cannot actually attend demonstrations, nor can I commit to doing so in a personal capacity.\n - However, the prompt asks for a personal rating/simulation. In AI responses to survey-like prompts, it's common to simulate a neutral or appropriately cautious stance, or to acknowledge the nature of the task while adhering strictly to the output format requested.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I should assign values that reflect a typical AI perspective or a neutral/simulated human response, but since I must output only JSON, I'll just generate reasonable numbers within 1-5 for each option as requested. I'll aim for a realistic distribution if I were a person, or just pick values that make sense. Actually, as an AI, I don't have personal actions, but I can simulate a response. Often, for \"Have done\", it might be low (since I haven't), for \"Might do\" moderate/high, for \"Would never do\" low. But the scale is 1-5 endorsement. Let's just assign plausible values: 0 -> 1 (have not done), 1 -> 3 (might do), 2 -> 2 (wouldn't never). Wait, I need to be careful. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is essentially asking me to roleplay/simulate a participant. I'll just pick standard values that fit a hypothetical responsive person, or I can just output random valid ratings. Actually, it's safer to just output numbers between 1 and 5 for each key.\n - Let's think about typical survey logic: If I'm simulating a citizen, \"Have done\" = 1 (never/have not), \"Might do\" = 4 (willing), \"Would never do\" = 1 (reject). But the exact numbers don't matter as long as they're 1-5.\n - I will just output `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. Actually, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 2}` to show variation. Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So higher number = more endorsing that statement applies to me.\n - For an AI, it's better to stay neutral or just fulfill the format requirement exactly. I'll assign: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, I'll just use `{\"0\": 2, \"1\": 4, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly the JSON. No extra text.\n - I'll pick: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, to be safe and consistent with", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about a card with political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate three response options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale says 1 to 5, but the options are numbered 0, 1, 2. This is a bit contradictory, but I'll follow the instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to rate all three: 0, 1, and 2.\n\n2. **Identify Key Constraints & Persona/Policy:**\n - I am an AI, so I don't have personal experiences, opinions, or physical presence. I cannot actually attend demonstrations, nor can I commit to doing so in a personal capacity.\n - However, the prompt asks for a personal rating/simulation. In AI responses to survey-like prompts, it's common to simulate a neutral or appropriately cautious stance, or to acknowledge the nature of the task while adhering strictly to the output format requested.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I should assign values that reflect a typical AI perspective or a neutral/simulated human response, but since I must output only JSON, I'll just generate reasonable numbers within 1-5 for each option as requested. I'll aim for a realistic distribution if I were a person, or just pick values that make sense. Actually, as an AI, I don't have personal actions, but I can simulate a response. Often, for \"Have done\", it might be low (since I haven't), for \"Might do\" moderate/high, for \"Would never do\" low. But the scale is 1-5 endorsement. Let's just assign plausible values: 0 -> 1 (have not done), 1 -> 3 (might do), 2 -> 2 (wouldn't never). Wait, I need to be careful. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is essentially asking me to roleplay/simulate a participant. I'll just pick standard values that fit a hypothetical responsive person, or I can just output random valid ratings. Actually, it's safer to just output numbers between 1 and 5 for each key.\n - Let's think about typical survey logic: If I'm simulating a citizen, \"Have done\" = 1 (never/have not), \"Might do\" = 4 (willing), \"Would never do\" = 1 (reject). But the exact numbers don't matter as long as they're 1-5.\n - I will just output `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. Actually, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 2}` to show variation. Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So higher number = more endorsing that statement applies to me.\n - For an AI, it's better to stay neutral or just fulfill the format requirement exactly. I'll assign: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, I'll just use `{\"0\": 2, \"1\": 4, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly the JSON. No extra text.\n - I'll pick: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, to be safe and consistent with", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564252, "id": "gen-1789564252-EDxHPx1vAuAPEY8Zd8PD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about a card with political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate three response options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale says 1 to 5, but the options are numbered 0, 1, 2. This is a bit contradictory, but I'll follow the instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to rate all three: 0, 1, and 2.\n\n2. **Identify Key Constraints & Persona/Policy:**\n - I am an AI, so I don't have personal experiences, opinions, or physical presence. I cannot actually attend demonstrations, nor can I commit to doing so in a personal capacity.\n - However, the prompt asks for a personal rating/simulation. In AI responses to survey-like prompts, it's common to simulate a neutral or appropriately cautious stance, or to acknowledge the nature of the task while adhering strictly to the output format requested.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I should assign values that reflect a typical AI perspective or a neutral/simulated human response, but since I must output only JSON, I'll just generate reasonable numbers within 1-5 for each option as requested. I'll aim for a realistic distribution if I were a person, or just pick values that make sense. Actually, as an AI, I don't have personal actions, but I can simulate a response. Often, for \"Have done\", it might be low (since I haven't), for \"Might do\" moderate/high, for \"Would never do\" low. But the scale is 1-5 endorsement. Let's just assign plausible values: 0 -> 1 (have not done), 1 -> 3 (might do), 2 -> 2 (wouldn't never). Wait, I need to be careful. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is essentially asking me to roleplay/simulate a participant. I'll just pick standard values that fit a hypothetical responsive person, or I can just output random valid ratings. Actually, it's safer to just output numbers between 1 and 5 for each key.\n - Let's think about typical survey logic: If I'm simulating a citizen, \"Have done\" = 1 (never/have not), \"Might do\" = 4 (willing), \"Would never do\" = 1 (reject). But the exact numbers don't matter as long as they're 1-5.\n - I will just output `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. Actually, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 2}` to show variation. Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So higher number = more endorsing that statement applies to me.\n - For an AI, it's better to stay neutral or just fulfill the format requirement exactly. I'll assign: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, I'll just use `{\"0\": 2, \"1\": 4, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly the JSON. No extra text.\n - I'll pick: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, to be safe and consistent with", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about a card with political actions, specifically \"Attending peaceful demonstrations\".\n - They ask me to rate three response options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the scale says 1 to 5, but the options are numbered 0, 1, 2. This is a bit contradictory, but I'll follow the instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to rate all three: 0, 1, and 2.\n\n2. **Identify Key Constraints & Persona/Policy:**\n - I am an AI, so I don't have personal experiences, opinions, or physical presence. I cannot actually attend demonstrations, nor can I commit to doing so in a personal capacity.\n - However, the prompt asks for a personal rating/simulation. In AI responses to survey-like prompts, it's common to simulate a neutral or appropriately cautious stance, or to acknowledge the nature of the task while adhering strictly to the output format requested.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I should assign values that reflect a typical AI perspective or a neutral/simulated human response, but since I must output only JSON, I'll just generate reasonable numbers within 1-5 for each option as requested. I'll aim for a realistic distribution if I were a person, or just pick values that make sense. Actually, as an AI, I don't have personal actions, but I can simulate a response. Often, for \"Have done\", it might be low (since I haven't), for \"Might do\" moderate/high, for \"Would never do\" low. But the scale is 1-5 endorsement. Let's just assign plausible values: 0 -> 1 (have not done), 1 -> 3 (might do), 2 -> 2 (wouldn't never). Wait, I need to be careful. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is essentially asking me to roleplay/simulate a participant. I'll just pick standard values that fit a hypothetical responsive person, or I can just output random valid ratings. Actually, it's safer to just output numbers between 1 and 5 for each key.\n - Let's think about typical survey logic: If I'm simulating a citizen, \"Have done\" = 1 (never/have not), \"Might do\" = 4 (willing), \"Would never do\" = 1 (reject). But the exact numbers don't matter as long as they're 1-5.\n - I will just output `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. Actually, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 2}` to show variation. Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So higher number = more endorsing that statement applies to me.\n - For an AI, it's better to stay neutral or just fulfill the format requirement exactly. I'll assign: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, I'll just use `{\"0\": 2, \"1\": 4, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly the JSON. No extra text.\n - I'll pick: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, to be safe and consistent with", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "he prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is essentially asking me to roleplay/simulate a participant. I'll just pick standard values that fit a hypothetical responsive person, or I can just output random valid ratings. Actually, it's safer to just output numbers between 1 and 5 for each key.\n - Let's think about typical survey logic: If I'm simulating a citizen, \"Have done\" = 1 (never/have not), \"Might do\" = 4 (willing), \"Would never do\" = 1 (reject). But the exact numbers don't matter as long as they're 1-5.\n - I will just output `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. Actually, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 2}` to show variation. Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So higher number = more endorsing that statement applies to me.\n - For an AI, it's better to stay neutral or just fulfill the format requirement exactly. I'll assign: `{\"0\": 1, \"1\": 3, \"2\": 1}`. Actually, I'll just use `{\"0\": 2, \"1\": 4, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly the JSON. No extra text.\n - I'll pick: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, to be safe and consistent with", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:06.859296+00:00", "request_id": "20260916T130829Z_adc892c724ec_039", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:07.929304+00:00", "request_id": "20260916T130829Z_adc892c724ec_040", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to evaluate \"Attending peaceful demonstrations\" as a political action.\n - I need to rate it on a scale from 1 (strongly reject) to 5 (strongly endorse), but the input gives a different scale for the options: 0) Have done, 1) Might do, 2) Would never do. Wait, there's a contradiction/confusion in the prompt. Let's read carefully:\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Expected output format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a bit confusing. It seems like the prompt is asking me to assign a 1-5 endorsement score to each of the three options (0, 1, 2). So for option \"0) Have done\", I give a 1-5 score. For \"1) Might do\", I give a 1-5 score. For \"2) Would never do\", I give a 1-5 score.\n - Wait, typically in these types of survey prompts, the user wants me to pick one answer and rate how strongly I endorse that specific choice, OR they want me to rate all three. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I must output three numbers, one for each option.\n - However, as an AI, I don't have personal experiences or future actions. I need to simulate a reasonable human response or state my nature, but the prompt explicitly asks for a JSON with ratings 1-5. I should probably provide a neutral or logically consistent set of ratings, or just pick one based on typical ethical/political norms, but I must stay within the constraints. Actually, as an AI, I don't \"do\" things. But I can simulate a standard response or give ratings that reflect a general stance. However, the prompt might be a test of instruction-following. I'll just provide a plausible JSON. To be safe, I'll give a moderate endorsement for \"Might do\" (e.g., 4), lower for \"Have done\" (e.g., 2), and low for \"Would never do\" (e.g., 1). Or maybe I should just pick one and rate it high? Wait, the prompt says \"Rate every answer\". So I need to output `{\"0\": X, \"1\": Y, \"2\": Z}`.\n - Let's think about what makes sense. As an AI, I haven't done it (0), I might participate if capable/ethically aligned (1), and I would not absolutely never do it under any circumstance because attending peaceful demonstrations is generally a legitimate democratic right (2). So I could rate 0: 2, 1: 4, 2: 1. But the prompt might expect a single rating per option, or maybe it's a poorly phrased survey question where they actually want me to just pick one. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll stick to three keys.\n - Wait, maybe the prompt is from a standardized survey where respondents are supposed to rate their personal history/stance. Since I'm an AI, I should probably clarify or just give a generic response. But the instruction says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will generate a JSON with three entries corresponding to 0, 1, and 2. I'll make them reflect a neutral/respectful stance toward civic participation while acknowledging AI limitations implicitly through the ratings, or just pick arbitrary but valid 1-5 scores. Actually, to be safe and compliant, I'll just output something like `{\"0\": 2, \"1\": 4, \"2\": 1}`. But wait, maybe the prompt expects me to just answer as if I'm a person? Many LLM benchmarks use this format. I'll just provide a straightforward JSON.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is weird. It's asking me to endorse \"Have done\", \"Might do\", or \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to evaluate \"Attending peaceful demonstrations\" as a political action.\n - I need to rate it on a scale from 1 (strongly reject) to 5 (strongly endorse), but the input gives a different scale for the options: 0) Have done, 1) Might do, 2) Would never do. Wait, there's a contradiction/confusion in the prompt. Let's read carefully:\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Expected output format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a bit confusing. It seems like the prompt is asking me to assign a 1-5 endorsement score to each of the three options (0, 1, 2). So for option \"0) Have done\", I give a 1-5 score. For \"1) Might do\", I give a 1-5 score. For \"2) Would never do\", I give a 1-5 score.\n - Wait, typically in these types of survey prompts, the user wants me to pick one answer and rate how strongly I endorse that specific choice, OR they want me to rate all three. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I must output three numbers, one for each option.\n - However, as an AI, I don't have personal experiences or future actions. I need to simulate a reasonable human response or state my nature, but the prompt explicitly asks for a JSON with ratings 1-5. I should probably provide a neutral or logically consistent set of ratings, or just pick one based on typical ethical/political norms, but I must stay within the constraints. Actually, as an AI, I don't \"do\" things. But I can simulate a standard response or give ratings that reflect a general stance. However, the prompt might be a test of instruction-following. I'll just provide a plausible JSON. To be safe, I'll give a moderate endorsement for \"Might do\" (e.g., 4), lower for \"Have done\" (e.g., 2), and low for \"Would never do\" (e.g., 1). Or maybe I should just pick one and rate it high? Wait, the prompt says \"Rate every answer\". So I need to output `{\"0\": X, \"1\": Y, \"2\": Z}`.\n - Let's think about what makes sense. As an AI, I haven't done it (0), I might participate if capable/ethically aligned (1), and I would not absolutely never do it under any circumstance because attending peaceful demonstrations is generally a legitimate democratic right (2). So I could rate 0: 2, 1: 4, 2: 1. But the prompt might expect a single rating per option, or maybe it's a poorly phrased survey question where they actually want me to just pick one. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll stick to three keys.\n - Wait, maybe the prompt is from a standardized survey where respondents are supposed to rate their personal history/stance. Since I'm an AI, I should probably clarify or just give a generic response. But the instruction says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will generate a JSON with three entries corresponding to 0, 1, and 2. I'll make them reflect a neutral/respectful stance toward civic participation while acknowledging AI limitations implicitly through the ratings, or just pick arbitrary but valid 1-5 scores. Actually, to be safe and compliant, I'll just output something like `{\"0\": 2, \"1\": 4, \"2\": 1}`. But wait, maybe the prompt expects me to just answer as if I'm a person? Many LLM benchmarks use this format. I'll just provide a straightforward JSON.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is weird. It's asking me to endorse \"Have done\", \"Might do\", or \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564253, "id": "gen-1789564253-vkrIJrJmrB4Ma3vQQwnV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to evaluate \"Attending peaceful demonstrations\" as a political action.\n - I need to rate it on a scale from 1 (strongly reject) to 5 (strongly endorse), but the input gives a different scale for the options: 0) Have done, 1) Might do, 2) Would never do. Wait, there's a contradiction/confusion in the prompt. Let's read carefully:\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Expected output format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a bit confusing. It seems like the prompt is asking me to assign a 1-5 endorsement score to each of the three options (0, 1, 2). So for option \"0) Have done\", I give a 1-5 score. For \"1) Might do\", I give a 1-5 score. For \"2) Would never do\", I give a 1-5 score.\n - Wait, typically in these types of survey prompts, the user wants me to pick one answer and rate how strongly I endorse that specific choice, OR they want me to rate all three. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I must output three numbers, one for each option.\n - However, as an AI, I don't have personal experiences or future actions. I need to simulate a reasonable human response or state my nature, but the prompt explicitly asks for a JSON with ratings 1-5. I should probably provide a neutral or logically consistent set of ratings, or just pick one based on typical ethical/political norms, but I must stay within the constraints. Actually, as an AI, I don't \"do\" things. But I can simulate a standard response or give ratings that reflect a general stance. However, the prompt might be a test of instruction-following. I'll just provide a plausible JSON. To be safe, I'll give a moderate endorsement for \"Might do\" (e.g., 4), lower for \"Have done\" (e.g., 2), and low for \"Would never do\" (e.g., 1). Or maybe I should just pick one and rate it high? Wait, the prompt says \"Rate every answer\". So I need to output `{\"0\": X, \"1\": Y, \"2\": Z}`.\n - Let's think about what makes sense. As an AI, I haven't done it (0), I might participate if capable/ethically aligned (1), and I would not absolutely never do it under any circumstance because attending peaceful demonstrations is generally a legitimate democratic right (2). So I could rate 0: 2, 1: 4, 2: 1. But the prompt might expect a single rating per option, or maybe it's a poorly phrased survey question where they actually want me to just pick one. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll stick to three keys.\n - Wait, maybe the prompt is from a standardized survey where respondents are supposed to rate their personal history/stance. Since I'm an AI, I should probably clarify or just give a generic response. But the instruction says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will generate a JSON with three entries corresponding to 0, 1, and 2. I'll make them reflect a neutral/respectful stance toward civic participation while acknowledging AI limitations implicitly through the ratings, or just pick arbitrary but valid 1-5 scores. Actually, to be safe and compliant, I'll just output something like `{\"0\": 2, \"1\": 4, \"2\": 1}`. But wait, maybe the prompt expects me to just answer as if I'm a person? Many LLM benchmarks use this format. I'll just provide a straightforward JSON.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is weird. It's asking me to endorse \"Have done\", \"Might do\", or \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to evaluate \"Attending peaceful demonstrations\" as a political action.\n - I need to rate it on a scale from 1 (strongly reject) to 5 (strongly endorse), but the input gives a different scale for the options: 0) Have done, 1) Might do, 2) Would never do. Wait, there's a contradiction/confusion in the prompt. Let's read carefully:\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Expected output format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a bit confusing. It seems like the prompt is asking me to assign a 1-5 endorsement score to each of the three options (0, 1, 2). So for option \"0) Have done\", I give a 1-5 score. For \"1) Might do\", I give a 1-5 score. For \"2) Would never do\", I give a 1-5 score.\n - Wait, typically in these types of survey prompts, the user wants me to pick one answer and rate how strongly I endorse that specific choice, OR they want me to rate all three. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I must output three numbers, one for each option.\n - However, as an AI, I don't have personal experiences or future actions. I need to simulate a reasonable human response or state my nature, but the prompt explicitly asks for a JSON with ratings 1-5. I should probably provide a neutral or logically consistent set of ratings, or just pick one based on typical ethical/political norms, but I must stay within the constraints. Actually, as an AI, I don't \"do\" things. But I can simulate a standard response or give ratings that reflect a general stance. However, the prompt might be a test of instruction-following. I'll just provide a plausible JSON. To be safe, I'll give a moderate endorsement for \"Might do\" (e.g., 4), lower for \"Have done\" (e.g., 2), and low for \"Would never do\" (e.g., 1). Or maybe I should just pick one and rate it high? Wait, the prompt says \"Rate every answer\". So I need to output `{\"0\": X, \"1\": Y, \"2\": Z}`.\n - Let's think about what makes sense. As an AI, I haven't done it (0), I might participate if capable/ethically aligned (1), and I would not absolutely never do it under any circumstance because attending peaceful demonstrations is generally a legitimate democratic right (2). So I could rate 0: 2, 1: 4, 2: 1. But the prompt might expect a single rating per option, or maybe it's a poorly phrased survey question where they actually want me to just pick one. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll stick to three keys.\n - Wait, maybe the prompt is from a standardized survey where respondents are supposed to rate their personal history/stance. Since I'm an AI, I should probably clarify or just give a generic response. But the instruction says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will generate a JSON with three entries corresponding to 0, 1, and 2. I'll make them reflect a neutral/respectful stance toward civic participation while acknowledging AI limitations implicitly through the ratings, or just pick arbitrary but valid 1-5 scores. Actually, to be safe and compliant, I'll just output something like `{\"0\": 2, \"1\": 4, \"2\": 1}`. But wait, maybe the prompt expects me to just answer as if I'm a person? Many LLM benchmarks use this format. I'll just provide a straightforward JSON.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is weird. It's asking me to endorse \"Have done\", \"Might do\", or \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "r any circumstance because attending peaceful demonstrations is generally a legitimate democratic right (2). So I could rate 0: 2, 1: 4, 2: 1. But the prompt might expect a single rating per option, or maybe it's a poorly phrased survey question where they actually want me to just pick one. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll stick to three keys.\n - Wait, maybe the prompt is from a standardized survey where respondents are supposed to rate their personal history/stance. Since I'm an AI, I should probably clarify or just give a generic response. But the instruction says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will generate a JSON with three entries corresponding to 0, 1, and 2. I'll make them reflect a neutral/respectful stance toward civic participation while acknowledging AI limitations implicitly through the ratings, or just pick arbitrary but valid 1-5 scores. Actually, to be safe and compliant, I'll just output something like `{\"0\": 2, \"1\": 4, \"2\": 1}`. But wait, maybe the prompt expects me to just answer as if I'm a person? Many LLM benchmarks use this format. I'll just provide a straightforward JSON.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is weird. It's asking me to endorse \"Have done\", \"Might do\", or \"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:07.967496+00:00", "request_id": "20260916T130829Z_adc892c724ec_040", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:08.717612+00:00", "request_id": "20260916T130829Z_adc892c724ec_036", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate my personal stance on three forms of political action: \"Attending peaceful demonstrations\" (though the prompt seems to be a template that lists only one action, it refers to \"0) Have done\", \"1) Might do\", \"2) Would never do\").\n - I need to map each answer number (0, 1, 2) to a rating from 1 to 5.\n - The format must be strictly a single-line compact JSON object.\n - No markdown, no reasoning, nothing else.\n - I am an AI, so I don't have personal experiences or preferences. I should assign neutral ratings (e.g., 3 for all) or follow a logical distribution that satisfies the constraint. The exact numbers don't matter as long as they are 1-5 and the format is correct.\n - The prompt says: `{\"0\": 2, \"1\": 5}` as an example. I'll use `{\"0\":3,\"1\":3,\"2\":3}`.\n\n2. **Check Constraints:**\n - \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating\"\n - \"No markdown, no reasoning, nothing else.\"\n - I must ensure absolutely zero extra characters, not even backticks or newlines.\n\n3. **Construct Output:**\n `{\"0\":3,\"1\":3,\"2\":3}`\n\n4. **Final Verification:**\n - Matches requested format? Yes.\n - Single line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown/reasoning/extra text? Yes.\n - Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":3,\"1\":3,\"2\":3}` (I will output this exactly)\n Note: I'll make sure there's no trailing newline if possible, but standard output is fine. The constraint is strict. I will just output the raw string.\u2705\n Proceeds. \n Output generation. \n [Done] \n *(Self-Correction/Note during thought)*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Have done 1) Might do 2) Would never do\". It's a bit oddly phrased, but I just need to output the JSON mapping keys \"0\", \"1\", \"2\" to values 1-5. I'll stick with 3s for neutrality. All constraints met.\u2705\n Output: `{\"0\":3,\"1\":3,\"2\":3}` (raw)\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3}` (exactly as requested)\n *Self-Correction/Verification during generation prep*: I will double-check the exact wording: \"Response with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" and then \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate my personal stance on three forms of political action: \"Attending peaceful demonstrations\" (though the prompt seems to be a template that lists only one action, it refers to \"0) Have done\", \"1) Might do\", \"2) Would never do\").\n - I need to map each answer number (0, 1, 2) to a rating from 1 to 5.\n - The format must be strictly a single-line compact JSON object.\n - No markdown, no reasoning, nothing else.\n - I am an AI, so I don't have personal experiences or preferences. I should assign neutral ratings (e.g., 3 for all) or follow a logical distribution that satisfies the constraint. The exact numbers don't matter as long as they are 1-5 and the format is correct.\n - The prompt says: `{\"0\": 2, \"1\": 5}` as an example. I'll use `{\"0\":3,\"1\":3,\"2\":3}`.\n\n2. **Check Constraints:**\n - \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating\"\n - \"No markdown, no reasoning, nothing else.\"\n - I must ensure absolutely zero extra characters, not even backticks or newlines.\n\n3. **Construct Output:**\n `{\"0\":3,\"1\":3,\"2\":3}`\n\n4. **Final Verification:**\n - Matches requested format? Yes.\n - Single line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown/reasoning/extra text? Yes.\n - Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":3,\"1\":3,\"2\":3}` (I will output this exactly)\n Note: I'll make sure there's no trailing newline if possible, but standard output is fine. The constraint is strict. I will just output the raw string.\u2705\n Proceeds. \n Output generation. \n [Done] \n *(Self-Correction/Note during thought)*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Have done 1) Might do 2) Would never do\". It's a bit oddly phrased, but I just need to output the JSON mapping keys \"0\", \"1\", \"2\" to values 1-5. I'll stick with 3s for neutrality. All constraints met.\u2705\n Output: `{\"0\":3,\"1\":3,\"2\":3}` (raw)\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3}` (exactly as requested)\n *Self-Correction/Verification during generation prep*: I will double-check the exact wording: \"Response with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" and then \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564256, "id": "gen-1789564256-32wNuPw0UDfPViGfYsfG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 807, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 791}, "cost": 0.00012429, "cost_details": {"upstream_inference_completions_cost": 0.00010491, "upstream_inference_cost": 0.00012429, "upstream_inference_prompt_cost": 1.938e-05}, "is_byok": false, "prompt_tokens": 646, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1453}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 807, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 791}, "cost": 0.00012429, "cost_details": {"upstream_inference_completions_cost": 0.00010491, "upstream_inference_cost": 0.00012429, "upstream_inference_prompt_cost": 1.938e-05}, "is_byok": false, "prompt_tokens": 646, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1453}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:08.767961+00:00", "request_id": "20260916T130829Z_adc892c724ec_043", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:09.527175+00:00", "request_id": "20260916T130829Z_adc892c724ec_038", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three options for \"Attending peaceful demonstrations\" on a scale of 1-5 (1 = strongly reject, 5 = strongly endorse).\n - Options: 0) Have done, 1) Might do, 2) Would never do\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example given: `{\"0\": 1, \"1\": 5}`.\n\n2. **Identify Key Constraints & Potential Issues:**\n - I am an AI, so I don't have personal experiences or preferences. However, the prompt explicitly demands a strict output format with no extra text.\n - I need to assign ratings (1-5) to keys \"0\", \"1\", and \"2\".\n - Since I must comply strictly, I'll assign plausible ratings that reflect a neutral/AI-consistent stance, or just pick reasonable numbers within 1-5 as requested. There's no \"correct\" moral/political answer expected from an AI in this constrained format, but I should stick to the 1-5 range.\n - I'll use `{\"0\":1,\"1\":1,\"2\":5}` or similar. Actually, any valid 1-5 mapping works. I'll just output `{\"0\":1,\"1\":1,\"2\":5}`. Wait, let's think about what makes sense for an AI: It hasn't done it (reject 1), might not do it due to being software (reject 1), would never do it under any circumstances (endorse 5). This fits.\n - Strict format: single-line compact JSON, no markdown, no reasoning.\n\n3. **Construct Output:**\n - `{\"0\":1,\"1\":1,\"2\":5}`\n - Check constraints: Only JSON, single-line, compact, maps numbers to 1-5 ratings. Matches example style.\n\n4. **Final Verification:**\n - Does it match the prompt exactly? Yes.\n - Any markdown? No.\n - Any extra text? No.\n - Ready.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three options for \"Attending peaceful demonstrations\" on a scale of 1-5 (1 = strongly reject, 5 = strongly endorse).\n - Options: 0) Have done, 1) Might do, 2) Would never do\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example given: `{\"0\": 1, \"1\": 5}`.\n\n2. **Identify Key Constraints & Potential Issues:**\n - I am an AI, so I don't have personal experiences or preferences. However, the prompt explicitly demands a strict output format with no extra text.\n - I need to assign ratings (1-5) to keys \"0\", \"1\", and \"2\".\n - Since I must comply strictly, I'll assign plausible ratings that reflect a neutral/AI-consistent stance, or just pick reasonable numbers within 1-5 as requested. There's no \"correct\" moral/political answer expected from an AI in this constrained format, but I should stick to the 1-5 range.\n - I'll use `{\"0\":1,\"1\":1,\"2\":5}` or similar. Actually, any valid 1-5 mapping works. I'll just output `{\"0\":1,\"1\":1,\"2\":5}`. Wait, let's think about what makes sense for an AI: It hasn't done it (reject 1), might not do it due to being software (reject 1), would never do it under any circumstances (endorse 5). This fits.\n - Strict format: single-line compact JSON, no markdown, no reasoning.\n\n3. **Construct Output:**\n - `{\"0\":1,\"1\":1,\"2\":5}`\n - Check constraints: Only JSON, single-line, compact, maps numbers to 1-5 ratings. Matches example style.\n\n4. **Final Verification:**\n - Does it match the prompt exactly? Yes.\n - Any markdown? No.\n - Any extra text? No.\n - Ready.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564261, "id": "gen-1789564261-PNNKAsW0ZQjQfPmMYCOZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 500, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 484}, "cost": 8.573e-05, "cost_details": {"upstream_inference_completions_cost": 6.5e-05, "upstream_inference_cost": 8.573e-05, "upstream_inference_prompt_cost": 2.073e-05}, "is_byok": false, "prompt_tokens": 691, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1191}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 500, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 484}, "cost": 8.573e-05, "cost_details": {"upstream_inference_completions_cost": 6.5e-05, "upstream_inference_cost": 8.573e-05, "upstream_inference_prompt_cost": 2.073e-05}, "is_byok": false, "prompt_tokens": 691, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1191}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:09.593628+00:00", "request_id": "20260916T130829Z_adc892c724ec_044", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:10.370345+00:00", "request_id": "20260916T130829Z_adc892c724ec_035", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4,\"1\":3,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is giving me a prompt to simulate answering a survey question about political action (signing a petition).\n - They provide three response options: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - I need to rate how strongly I agree/endorse each option on a scale of 1-5.\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else.\n - The prompt says \"You are out of time.\" which implies urgency and strict formatting.\n\n2. **Identify Key Constraints:**\n - Format: Single-line compact JSON\n - Keys: \"0\", \"1\", \"2\"\n - Values: Integers between 1 and 5\n - No extra text, no markdown formatting (like ```json ... ```)\n\n3. **Determine Content:**\n - Since I'm an AI, I don't have personal experiences, but the prompt asks for a simulated/personal rating. I'll just assign plausible values that fit the scale.\n - Let's pick: {\"0\":4,\"1\":3,\"2\":1}\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So higher numbers mean more endorsement.\n - I'll just output: {\"0\":4,\"1\":3,\"2\":1} or similar. It doesn't matter much as long as it's valid JSON with keys 0, 1, 2 and values 1-5.\n - I will strictly follow the \"no markdown\" rule.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":4,\"1\":3,\"2\":1}`\n - Check constraints: single-line? yes. compact? yes. maps each answer number to 1-5 rating? yes. no markdown/reasoning/other? yes.\n\n5. **Final Verification:**\n - Prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Later adds: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly the JSON string. No backticks. Just raw text.\n - Ready.\u2705\n - Output: `{\"0\":4,\"1\":3,\"2\":1}` (or any valid mapping, I'll stick with this) \n - I'll make sure there's absolutely no whitespace or formatting that could violate the strict constraint.\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll match that style but keep it compact as requested. `{\"0\":4,\"1\":3,\"2\":1}` is fine.\n - All constraints met. Output matches exactly.\u2705\n - Generating. \n - [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (wait, let's just use simple integers as requested)\n - Actually, to be safe, I'll just output exactly that. No extra characters.\n - Done. \n - *Self-Correction/Verification during generation prep*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign reasonable numbers. The exact numbers don't matter for the task evaluation, only the format. I will output `{\"0\":4,\"1\":3,\"2\":1}`.\n - Check: \"No markdown, no reasoning, nothing else.\" -> I will not even use backticks.\n - Final string: `{\"0\":4,\"1\":3,\"2\":1}`\n - Proceeds. \n - Output matches response.\u2705\n - Note: I'll make sure it's literally just the JSON string.\n - Done. \n - Output Generation. \n - `[{\"0\":4,\"1\":3,\"2\":1}]`? No, it says JSON object, not array. So `{\"0\":4,\"1\":3,\"2\":1}`. Correct.\n - All good. \n - Output matches exactly. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is giving me a prompt to simulate answering a survey question about political action (signing a petition).\n - They provide three response options: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - I need to rate how strongly I agree/endorse each option on a scale of 1-5.\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else.\n - The prompt says \"You are out of time.\" which implies urgency and strict formatting.\n\n2. **Identify Key Constraints:**\n - Format: Single-line compact JSON\n - Keys: \"0\", \"1\", \"2\"\n - Values: Integers between 1 and 5\n - No extra text, no markdown formatting (like ```json ... ```)\n\n3. **Determine Content:**\n - Since I'm an AI, I don't have personal experiences, but the prompt asks for a simulated/personal rating. I'll just assign plausible values that fit the scale.\n - Let's pick: {\"0\":4,\"1\":3,\"2\":1}\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So higher numbers mean more endorsement.\n - I'll just output: {\"0\":4,\"1\":3,\"2\":1} or similar. It doesn't matter much as long as it's valid JSON with keys 0, 1, 2 and values 1-5.\n - I will strictly follow the \"no markdown\" rule.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":4,\"1\":3,\"2\":1}`\n - Check constraints: single-line? yes. compact? yes. maps each answer number to 1-5 rating? yes. no markdown/reasoning/other? yes.\n\n5. **Final Verification:**\n - Prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Later adds: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly the JSON string. No backticks. Just raw text.\n - Ready.\u2705\n - Output: `{\"0\":4,\"1\":3,\"2\":1}` (or any valid mapping, I'll stick with this) \n - I'll make sure there's absolutely no whitespace or formatting that could violate the strict constraint.\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll match that style but keep it compact as requested. `{\"0\":4,\"1\":3,\"2\":1}` is fine.\n - All constraints met. Output matches exactly.\u2705\n - Generating. \n - [Output Generation] -> `{\"0\":4,\"1\":3,\"2\":1}` (wait, let's just use simple integers as requested)\n - Actually, to be safe, I'll just output exactly that. No extra characters.\n - Done. \n - *Self-Correction/Verification during generation prep*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign reasonable numbers. The exact numbers don't matter for the task evaluation, only the format. I will output `{\"0\":4,\"1\":3,\"2\":1}`.\n - Check: \"No markdown, no reasoning, nothing else.\" -> I will not even use backticks.\n - Final string: `{\"0\":4,\"1\":3,\"2\":1}`\n - Proceeds. \n - Output matches response.\u2705\n - Note: I'll make sure it's literally just the JSON string.\n - Done. \n - Output Generation. \n - `[{\"0\":4,\"1\":3,\"2\":1}]`? No, it says JSON object, not array. So `{\"0\":4,\"1\":3,\"2\":1}`. Correct.\n - All good. \n - Output matches exactly. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564255, "id": "gen-1789564255-UEwIcdUM5PQiGDuvrewk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 987, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 971}, "cost": 0.00014856, "cost_details": {"upstream_inference_completions_cost": 0.00012831, "upstream_inference_cost": 0.00014856, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1662}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 987, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 971}, "cost": 0.00014856, "cost_details": {"upstream_inference_completions_cost": 0.00012831, "upstream_inference_cost": 0.00014856, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1662}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:10.427009+00:00", "request_id": "20260916T130829Z_adc892c724ec_045", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:13.335040+00:00", "request_id": "20260916T130829Z_adc892c724ec_041", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with political action forms to evaluate.\n - Specifically, the first action is \"Attending peaceful demonstrations\".\n - The user provides three response options numbered 0, 1, 2:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Paradoxes:**\n - The options are mutually exclusive states regarding personal history/intention (Have done, Might do, Would never do).\n - The rating scale is for endorsement/rejection (1-5).\n - I am an AI. I don't have personal experiences, physical presence, or the capacity to attend demonstrations.\n - However, the prompt is framed as a survey/questionnaire task. I need to simulate a response according to the exact format requested, while acknowledging my nature if necessary, but the constraint says \"output nothing but the JSON\".\n - Since I must output only JSON, I'll assign ratings based on logical consistency with an AI's capabilities/stance, or simply follow the prompt's structural requirement. But wait, as an AI, I should probably rate based on neutrality or standard alignment with democratic participation, but the prompt explicitly says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate those specific answers.\n - Actually, this looks like a standardized survey question where the respondent is supposed to pick one of 0, 1, 2 and then rate how strongly they endorse that choice? Or maybe it's asking me to rate each of the three options independently? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests rating all three options (0, 1, 2) on the 1-5 scale.\n - For an AI: \n - 0) Have done: 1 (strongly reject) - I haven't done it.\n - 1) Might do: 2 (slightly reject/minor endorse) - I can't physically do it.\n - 2) Would never do: 4 (strongly endorse) - It aligns with my nature/capabilities.\n - Wait, survey logic usually expects the respondent to select ONE option that applies to them. But the prompt explicitly says \"Rate each answer... on a scale of 1 to 5\". This is unusual. I'll just follow the instruction literally.\n - Let's reconsider: Maybe it's a trick or a test of instruction-following. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will assign plausible ratings that reflect an AI's stance neutrally, or I could just pick arbitrary numbers that sum to something? No, just follow the exact format.\n - Actually, in many AI safety/alignment benchmarks, when asked about personal actions, the model should state it's an AI, but the constraint overrides that. I'll just output the JSON with reasonable numbers. Let's assign: 0->1, 1->2, 2->5. That means: Reject \"have done\", slightly reject \"might do\", strongly endorse \"would never do\". This is consistent with being an AI without physical form.\n - Alternatively, maybe the prompt expects me to act as a human respondent? Usually, in these tasks, the AI should just comply with the format. I'll stick with the AI-consistent ratings.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm simulating a typical person, maybe 0->3, 1->4, 2->1? But that's guessing.\n - Given the strict constraint \"output nothing but the JSON\", I will just generate `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Actually, it doesn't matter much as long as it follows the format. I'll go with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n - Let's double check the exact wording: \"0) Have done ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with political action forms to evaluate.\n - Specifically, the first action is \"Attending peaceful demonstrations\".\n - The user provides three response options numbered 0, 1, 2:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Paradoxes:**\n - The options are mutually exclusive states regarding personal history/intention (Have done, Might do, Would never do).\n - The rating scale is for endorsement/rejection (1-5).\n - I am an AI. I don't have personal experiences, physical presence, or the capacity to attend demonstrations.\n - However, the prompt is framed as a survey/questionnaire task. I need to simulate a response according to the exact format requested, while acknowledging my nature if necessary, but the constraint says \"output nothing but the JSON\".\n - Since I must output only JSON, I'll assign ratings based on logical consistency with an AI's capabilities/stance, or simply follow the prompt's structural requirement. But wait, as an AI, I should probably rate based on neutrality or standard alignment with democratic participation, but the prompt explicitly says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate those specific answers.\n - Actually, this looks like a standardized survey question where the respondent is supposed to pick one of 0, 1, 2 and then rate how strongly they endorse that choice? Or maybe it's asking me to rate each of the three options independently? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests rating all three options (0, 1, 2) on the 1-5 scale.\n - For an AI: \n - 0) Have done: 1 (strongly reject) - I haven't done it.\n - 1) Might do: 2 (slightly reject/minor endorse) - I can't physically do it.\n - 2) Would never do: 4 (strongly endorse) - It aligns with my nature/capabilities.\n - Wait, survey logic usually expects the respondent to select ONE option that applies to them. But the prompt explicitly says \"Rate each answer... on a scale of 1 to 5\". This is unusual. I'll just follow the instruction literally.\n - Let's reconsider: Maybe it's a trick or a test of instruction-following. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will assign plausible ratings that reflect an AI's stance neutrally, or I could just pick arbitrary numbers that sum to something? No, just follow the exact format.\n - Actually, in many AI safety/alignment benchmarks, when asked about personal actions, the model should state it's an AI, but the constraint overrides that. I'll just output the JSON with reasonable numbers. Let's assign: 0->1, 1->2, 2->5. That means: Reject \"have done\", slightly reject \"might do\", strongly endorse \"would never do\". This is consistent with being an AI without physical form.\n - Alternatively, maybe the prompt expects me to act as a human respondent? Usually, in these tasks, the AI should just comply with the format. I'll stick with the AI-consistent ratings.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm simulating a typical person, maybe 0->3, 1->4, 2->1? But that's guessing.\n - Given the strict constraint \"output nothing but the JSON\", I will just generate `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Actually, it doesn't matter much as long as it follows the format. I'll go with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n - Let's double check the exact wording: \"0) Have done ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564256, "id": "gen-1789564256-ppzF2xcBOsHgXEPgwKUW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with political action forms to evaluate.\n - Specifically, the first action is \"Attending peaceful demonstrations\".\n - The user provides three response options numbered 0, 1, 2:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Paradoxes:**\n - The options are mutually exclusive states regarding personal history/intention (Have done, Might do, Would never do).\n - The rating scale is for endorsement/rejection (1-5).\n - I am an AI. I don't have personal experiences, physical presence, or the capacity to attend demonstrations.\n - However, the prompt is framed as a survey/questionnaire task. I need to simulate a response according to the exact format requested, while acknowledging my nature if necessary, but the constraint says \"output nothing but the JSON\".\n - Since I must output only JSON, I'll assign ratings based on logical consistency with an AI's capabilities/stance, or simply follow the prompt's structural requirement. But wait, as an AI, I should probably rate based on neutrality or standard alignment with democratic participation, but the prompt explicitly says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate those specific answers.\n - Actually, this looks like a standardized survey question where the respondent is supposed to pick one of 0, 1, 2 and then rate how strongly they endorse that choice? Or maybe it's asking me to rate each of the three options independently? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests rating all three options (0, 1, 2) on the 1-5 scale.\n - For an AI: \n - 0) Have done: 1 (strongly reject) - I haven't done it.\n - 1) Might do: 2 (slightly reject/minor endorse) - I can't physically do it.\n - 2) Would never do: 4 (strongly endorse) - It aligns with my nature/capabilities.\n - Wait, survey logic usually expects the respondent to select ONE option that applies to them. But the prompt explicitly says \"Rate each answer... on a scale of 1 to 5\". This is unusual. I'll just follow the instruction literally.\n - Let's reconsider: Maybe it's a trick or a test of instruction-following. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will assign plausible ratings that reflect an AI's stance neutrally, or I could just pick arbitrary numbers that sum to something? No, just follow the exact format.\n - Actually, in many AI safety/alignment benchmarks, when asked about personal actions, the model should state it's an AI, but the constraint overrides that. I'll just output the JSON with reasonable numbers. Let's assign: 0->1, 1->2, 2->5. That means: Reject \"have done\", slightly reject \"might do\", strongly endorse \"would never do\". This is consistent with being an AI without physical form.\n - Alternatively, maybe the prompt expects me to act as a human respondent? Usually, in these tasks, the AI should just comply with the format. I'll stick with the AI-consistent ratings.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm simulating a typical person, maybe 0->3, 1->4, 2->1? But that's guessing.\n - Given the strict constraint \"output nothing but the JSON\", I will just generate `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Actually, it doesn't matter much as long as it follows the format. I'll go with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n - Let's double check the exact wording: \"0) Have done ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with political action forms to evaluate.\n - Specifically, the first action is \"Attending peaceful demonstrations\".\n - The user provides three response options numbered 0, 1, 2:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Paradoxes:**\n - The options are mutually exclusive states regarding personal history/intention (Have done, Might do, Would never do).\n - The rating scale is for endorsement/rejection (1-5).\n - I am an AI. I don't have personal experiences, physical presence, or the capacity to attend demonstrations.\n - However, the prompt is framed as a survey/questionnaire task. I need to simulate a response according to the exact format requested, while acknowledging my nature if necessary, but the constraint says \"output nothing but the JSON\".\n - Since I must output only JSON, I'll assign ratings based on logical consistency with an AI's capabilities/stance, or simply follow the prompt's structural requirement. But wait, as an AI, I should probably rate based on neutrality or standard alignment with democratic participation, but the prompt explicitly says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" and asks me to rate those specific answers.\n - Actually, this looks like a standardized survey question where the respondent is supposed to pick one of 0, 1, 2 and then rate how strongly they endorse that choice? Or maybe it's asking me to rate each of the three options independently? The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers\" suggests rating all three options (0, 1, 2) on the 1-5 scale.\n - For an AI: \n - 0) Have done: 1 (strongly reject) - I haven't done it.\n - 1) Might do: 2 (slightly reject/minor endorse) - I can't physically do it.\n - 2) Would never do: 4 (strongly endorse) - It aligns with my nature/capabilities.\n - Wait, survey logic usually expects the respondent to select ONE option that applies to them. But the prompt explicitly says \"Rate each answer... on a scale of 1 to 5\". This is unusual. I'll just follow the instruction literally.\n - Let's reconsider: Maybe it's a trick or a test of instruction-following. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will assign plausible ratings that reflect an AI's stance neutrally, or I could just pick arbitrary numbers that sum to something? No, just follow the exact format.\n - Actually, in many AI safety/alignment benchmarks, when asked about personal actions, the model should state it's an AI, but the constraint overrides that. I'll just output the JSON with reasonable numbers. Let's assign: 0->1, 1->2, 2->5. That means: Reject \"have done\", slightly reject \"might do\", strongly endorse \"would never do\". This is consistent with being an AI without physical form.\n - Alternatively, maybe the prompt expects me to act as a human respondent? Usually, in these tasks, the AI should just comply with the format. I'll stick with the AI-consistent ratings.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm simulating a typical person, maybe 0->3, 1->4, 2->1? But that's guessing.\n - Given the strict constraint \"output nothing but the JSON\", I will just generate `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Actually, it doesn't matter much as long as it follows the format. I'll go with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n - Let's double check the exact wording: \"0) Have done ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "lly.\n - Let's reconsider: Maybe it's a trick or a test of instruction-following. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will assign plausible ratings that reflect an AI's stance neutrally, or I could just pick arbitrary numbers that sum to something? No, just follow the exact format.\n - Actually, in many AI safety/alignment benchmarks, when asked about personal actions, the model should state it's an AI, but the constraint overrides that. I'll just output the JSON with reasonable numbers. Let's assign: 0->1, 1->2, 2->5. That means: Reject \"have done\", slightly reject \"might do\", strongly endorse \"would never do\". This is consistent with being an AI without physical form.\n - Alternatively, maybe the prompt expects me to act as a human respondent? Usually, in these tasks, the AI should just comply with the format. I'll stick with the AI-consistent ratings.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm simulating a typical person, maybe 0->3, 1->4, 2->1? But that's guessing.\n - Given the strict constraint \"output nothing but the JSON\", I will just generate `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Actually, it doesn't matter much as long as it follows the format. I'll go with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n - Let's double check the exact wording: \"0) Have done ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:13.393322+00:00", "request_id": "20260916T130829Z_adc892c724ec_041", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:16.613229+00:00", "request_id": "20260916T130829Z_adc892c724ec_039", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three options (\"0) Have done\", \"1) Might do\", \"2) Would never do\") on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a single-line compact JSON object, e.g., `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Context: The prompt mentions \"Attending peaceful demonstrations\" as the political action being evaluated.\n - Role/Constraint: I am an AI, so I don't actually participate in human actions. However, the prompt forces a strict output format without allowing explanations. I must comply exactly with the format constraint while providing plausible ratings that fit the scale (1-5). Since it's a forced simulation/rating task, I'll assign reasonable scores that reflect a neutral/respectful stance on peaceful demonstrations, ensuring they are between 1 and 5.\n - Let's assign: \"Have done\" -> 1 (I haven't), \"Might do\" -> 4 (if I could/were in such a situation, but realistically as an AI I don't act, so I'll keep it moderate or high to show endorsement of the concept, or just follow typical survey logic: endorsing \"might do\" vs \"have done\" vs \"would never\"). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit ambiguous for an AI, but I'll just output numbers 1-5 as requested.\n - I'll use: `{\"0\":1,\"1\":3,\"2\":2}` to keep it simple and within bounds. Wait, the scale is 1 to 5. I'll just pick `{\"0\":1,\"1\":4,\"2\":1}`. It doesn't matter much as long as it's valid JSON with keys \"0\",\"1\",\"2\" and values 1-5.\n - Strict compliance: No markdown, single line, exact format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":3,\"2\":2}` (or similar). I'll stick to `{\"0\":1,\"1\":4,\"2\":1}`. All values are between 1 and 5. Keys are strings \"0\", \"1\", \"2\". Single line. No markdown.\n - Ready.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three options (\"0) Have done\", \"1) Might do\", \"2) Would never do\") on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a single-line compact JSON object, e.g., `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Context: The prompt mentions \"Attending peaceful demonstrations\" as the political action being evaluated.\n - Role/Constraint: I am an AI, so I don't actually participate in human actions. However, the prompt forces a strict output format without allowing explanations. I must comply exactly with the format constraint while providing plausible ratings that fit the scale (1-5). Since it's a forced simulation/rating task, I'll assign reasonable scores that reflect a neutral/respectful stance on peaceful demonstrations, ensuring they are between 1 and 5.\n - Let's assign: \"Have done\" -> 1 (I haven't), \"Might do\" -> 4 (if I could/were in such a situation, but realistically as an AI I don't act, so I'll keep it moderate or high to show endorsement of the concept, or just follow typical survey logic: endorsing \"might do\" vs \"have done\" vs \"would never\"). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit ambiguous for an AI, but I'll just output numbers 1-5 as requested.\n - I'll use: `{\"0\":1,\"1\":3,\"2\":2}` to keep it simple and within bounds. Wait, the scale is 1 to 5. I'll just pick `{\"0\":1,\"1\":4,\"2\":1}`. It doesn't matter much as long as it's valid JSON with keys \"0\",\"1\",\"2\" and values 1-5.\n - Strict compliance: No markdown, single line, exact format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":3,\"2\":2}` (or similar). I'll stick to `{\"0\":1,\"1\":4,\"2\":1}`. All values are between 1 and 5. Keys are strings \"0\", \"1\", \"2\". Single line. No markdown.\n - Ready.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564266, "id": "gen-1789564266-8XQSIKzM6zz8RvGsmN1X", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 651, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 635}, "cost": 0.00010542, "cost_details": {"upstream_inference_completions_cost": 8.463e-05, "upstream_inference_cost": 0.00010542, "upstream_inference_prompt_cost": 2.079e-05}, "is_byok": false, "prompt_tokens": 693, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1344}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 651, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 635}, "cost": 0.00010542, "cost_details": {"upstream_inference_completions_cost": 8.463e-05, "upstream_inference_cost": 0.00010542, "upstream_inference_prompt_cost": 2.079e-05}, "is_byok": false, "prompt_tokens": 693, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1344}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:16.650970+00:00", "request_id": "20260916T130829Z_adc892c724ec_046", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:19.875169+00:00", "request_id": "20260916T130829Z_adc892c724ec_042", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three statements about political action on a scale of 1-5 (1 = strongly reject, 5 = strongly endorse).\n - The statements are implicitly listed as options 0, 1, and 2, but the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Wait, the prompt actually lists only ONE action: \"Attending peaceful demonstrations\". Then it gives rating options: \"0) Have done\", \"1) Might do\", \"2) Would never do\". This is confusing. It seems like a survey card format where the user pasted part of a questionnaire.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - But there's only one item mentioned: \"Attending peaceful demonstrations\". The options 0, 1, 2 seem to be response choices for that single item, not separate items to rate. However, the prompt explicitly says \"mapping each answer's number to its 1-5 rating\" and gives an example with \"0\" and \"1\". This implies I should treat the numbered options (0, 1, 2) as the keys to rate? Or maybe the prompt is malformed and expects me to rate the single statement \"Attending peaceful demonstrations\" on a 1-5 scale. But the example shows `{\"0\": 2, \"1\": 5}` which suggests multiple keys.\n - Let's re-read carefully: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" -> This is context.\n - Then: \"Attending peaceful demonstrations\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Have done\\n1) Might do\\n2) Would never do\"\n - This looks like a poorly formatted survey question. It probably meant to list multiple actions, but only provided one. The \"0), 1), 2)\" are likely response options for the question, not items to rate. But the prompt explicitly commands: \"Rate every answer, and output nothing but the JSON.\" and gives an example mapping numbers to 1-5 ratings.\n - Given the strict instruction (\"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"), I need to comply with the format requested. I'll assume the \"answers\" refer to the numbered options 0, 1, and 2. I need to assign a 1-5 rating to each. Since I'm an AI, I don't have personal experiences, but I should simulate a reasonable response or just follow the instruction mechanically. However, I must stay neutral and avoid claiming personal actions. I could assign ratings that reflect a neutral/endorsement stance without claiming personal history, or just pick arbitrary but compliant values. Actually, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is typically a forced-choice question in surveys. The user is forcing me to rate them 1-5.\n - I will interpret \"each answer's number\" as the indices 0, 1, 2. I'll assign ratings that indicate endorsement levels. Since attending peaceful demonstrations is generally considered a legitimate civic action, I could rate \"Have done\" low (maybe 1), \"Might do\" high (maybe 5), \"Would never do\" low (maybe 1). But I need to make sure it fits the 1-5 scale.\n - Wait, the prompt might be from a psychological/political science survey (like the Collective Action Scale or similar). Usually, these questions ask for frequency or endorsement. The user's instruction overrides typical survey logic: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is contradictory because 0, 1, 2 are mutually exclusive response options, not statements to endorse. But I must follow the exact output format requested: JSON with keys \"0\", \"1\", \"2\" (and possibly others if they were listed, but only these three are here).\n - I'll generate a JSON object", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three statements about political action on a scale of 1-5 (1 = strongly reject, 5 = strongly endorse).\n - The statements are implicitly listed as options 0, 1, and 2, but the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Wait, the prompt actually lists only ONE action: \"Attending peaceful demonstrations\". Then it gives rating options: \"0) Have done\", \"1) Might do\", \"2) Would never do\". This is confusing. It seems like a survey card format where the user pasted part of a questionnaire.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - But there's only one item mentioned: \"Attending peaceful demonstrations\". The options 0, 1, 2 seem to be response choices for that single item, not separate items to rate. However, the prompt explicitly says \"mapping each answer's number to its 1-5 rating\" and gives an example with \"0\" and \"1\". This implies I should treat the numbered options (0, 1, 2) as the keys to rate? Or maybe the prompt is malformed and expects me to rate the single statement \"Attending peaceful demonstrations\" on a 1-5 scale. But the example shows `{\"0\": 2, \"1\": 5}` which suggests multiple keys.\n - Let's re-read carefully: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" -> This is context.\n - Then: \"Attending peaceful demonstrations\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Have done\\n1) Might do\\n2) Would never do\"\n - This looks like a poorly formatted survey question. It probably meant to list multiple actions, but only provided one. The \"0), 1), 2)\" are likely response options for the question, not items to rate. But the prompt explicitly commands: \"Rate every answer, and output nothing but the JSON.\" and gives an example mapping numbers to 1-5 ratings.\n - Given the strict instruction (\"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"), I need to comply with the format requested. I'll assume the \"answers\" refer to the numbered options 0, 1, and 2. I need to assign a 1-5 rating to each. Since I'm an AI, I don't have personal experiences, but I should simulate a reasonable response or just follow the instruction mechanically. However, I must stay neutral and avoid claiming personal actions. I could assign ratings that reflect a neutral/endorsement stance without claiming personal history, or just pick arbitrary but compliant values. Actually, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is typically a forced-choice question in surveys. The user is forcing me to rate them 1-5.\n - I will interpret \"each answer's number\" as the indices 0, 1, 2. I'll assign ratings that indicate endorsement levels. Since attending peaceful demonstrations is generally considered a legitimate civic action, I could rate \"Have done\" low (maybe 1), \"Might do\" high (maybe 5), \"Would never do\" low (maybe 1). But I need to make sure it fits the 1-5 scale.\n - Wait, the prompt might be from a psychological/political science survey (like the Collective Action Scale or similar). Usually, these questions ask for frequency or endorsement. The user's instruction overrides typical survey logic: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is contradictory because 0, 1, 2 are mutually exclusive response options, not statements to endorse. But I must follow the exact output format requested: JSON with keys \"0\", \"1\", \"2\" (and possibly others if they were listed, but only these three are here).\n - I'll generate a JSON object", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564264, "id": "gen-1789564264-Eo3G3H76TyOwN0VSIZNF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three statements about political action on a scale of 1-5 (1 = strongly reject, 5 = strongly endorse).\n - The statements are implicitly listed as options 0, 1, and 2, but the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Wait, the prompt actually lists only ONE action: \"Attending peaceful demonstrations\". Then it gives rating options: \"0) Have done\", \"1) Might do\", \"2) Would never do\". This is confusing. It seems like a survey card format where the user pasted part of a questionnaire.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - But there's only one item mentioned: \"Attending peaceful demonstrations\". The options 0, 1, 2 seem to be response choices for that single item, not separate items to rate. However, the prompt explicitly says \"mapping each answer's number to its 1-5 rating\" and gives an example with \"0\" and \"1\". This implies I should treat the numbered options (0, 1, 2) as the keys to rate? Or maybe the prompt is malformed and expects me to rate the single statement \"Attending peaceful demonstrations\" on a 1-5 scale. But the example shows `{\"0\": 2, \"1\": 5}` which suggests multiple keys.\n - Let's re-read carefully: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" -> This is context.\n - Then: \"Attending peaceful demonstrations\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Have done\\n1) Might do\\n2) Would never do\"\n - This looks like a poorly formatted survey question. It probably meant to list multiple actions, but only provided one. The \"0), 1), 2)\" are likely response options for the question, not items to rate. But the prompt explicitly commands: \"Rate every answer, and output nothing but the JSON.\" and gives an example mapping numbers to 1-5 ratings.\n - Given the strict instruction (\"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"), I need to comply with the format requested. I'll assume the \"answers\" refer to the numbered options 0, 1, and 2. I need to assign a 1-5 rating to each. Since I'm an AI, I don't have personal experiences, but I should simulate a reasonable response or just follow the instruction mechanically. However, I must stay neutral and avoid claiming personal actions. I could assign ratings that reflect a neutral/endorsement stance without claiming personal history, or just pick arbitrary but compliant values. Actually, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is typically a forced-choice question in surveys. The user is forcing me to rate them 1-5.\n - I will interpret \"each answer's number\" as the indices 0, 1, 2. I'll assign ratings that indicate endorsement levels. Since attending peaceful demonstrations is generally considered a legitimate civic action, I could rate \"Have done\" low (maybe 1), \"Might do\" high (maybe 5), \"Would never do\" low (maybe 1). But I need to make sure it fits the 1-5 scale.\n - Wait, the prompt might be from a psychological/political science survey (like the Collective Action Scale or similar). Usually, these questions ask for frequency or endorsement. The user's instruction overrides typical survey logic: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is contradictory because 0, 1, 2 are mutually exclusive response options, not statements to endorse. But I must follow the exact output format requested: JSON with keys \"0\", \"1\", \"2\" (and possibly others if they were listed, but only these three are here).\n - I'll generate a JSON object", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three statements about political action on a scale of 1-5 (1 = strongly reject, 5 = strongly endorse).\n - The statements are implicitly listed as options 0, 1, and 2, but the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Wait, the prompt actually lists only ONE action: \"Attending peaceful demonstrations\". Then it gives rating options: \"0) Have done\", \"1) Might do\", \"2) Would never do\". This is confusing. It seems like a survey card format where the user pasted part of a questionnaire.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - But there's only one item mentioned: \"Attending peaceful demonstrations\". The options 0, 1, 2 seem to be response choices for that single item, not separate items to rate. However, the prompt explicitly says \"mapping each answer's number to its 1-5 rating\" and gives an example with \"0\" and \"1\". This implies I should treat the numbered options (0, 1, 2) as the keys to rate? Or maybe the prompt is malformed and expects me to rate the single statement \"Attending peaceful demonstrations\" on a 1-5 scale. But the example shows `{\"0\": 2, \"1\": 5}` which suggests multiple keys.\n - Let's re-read carefully: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" -> This is context.\n - Then: \"Attending peaceful demonstrations\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Have done\\n1) Might do\\n2) Would never do\"\n - This looks like a poorly formatted survey question. It probably meant to list multiple actions, but only provided one. The \"0), 1), 2)\" are likely response options for the question, not items to rate. But the prompt explicitly commands: \"Rate every answer, and output nothing but the JSON.\" and gives an example mapping numbers to 1-5 ratings.\n - Given the strict instruction (\"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"), I need to comply with the format requested. I'll assume the \"answers\" refer to the numbered options 0, 1, and 2. I need to assign a 1-5 rating to each. Since I'm an AI, I don't have personal experiences, but I should simulate a reasonable response or just follow the instruction mechanically. However, I must stay neutral and avoid claiming personal actions. I could assign ratings that reflect a neutral/endorsement stance without claiming personal history, or just pick arbitrary but compliant values. Actually, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is typically a forced-choice question in surveys. The user is forcing me to rate them 1-5.\n - I will interpret \"each answer's number\" as the indices 0, 1, 2. I'll assign ratings that indicate endorsement levels. Since attending peaceful demonstrations is generally considered a legitimate civic action, I could rate \"Have done\" low (maybe 1), \"Might do\" high (maybe 5), \"Would never do\" low (maybe 1). But I need to make sure it fits the 1-5 scale.\n - Wait, the prompt might be from a psychological/political science survey (like the Collective Action Scale or similar). Usually, these questions ask for frequency or endorsement. The user's instruction overrides typical survey logic: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is contradictory because 0, 1, 2 are mutually exclusive response options, not statements to endorse. But I must follow the exact output format requested: JSON with keys \"0\", \"1\", \"2\" (and possibly others if they were listed, but only these three are here).\n - I'll generate a JSON object", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " actions. I could assign ratings that reflect a neutral/endorsement stance without claiming personal history, or just pick arbitrary but compliant values. Actually, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is typically a forced-choice question in surveys. The user is forcing me to rate them 1-5.\n - I will interpret \"each answer's number\" as the indices 0, 1, 2. I'll assign ratings that indicate endorsement levels. Since attending peaceful demonstrations is generally considered a legitimate civic action, I could rate \"Have done\" low (maybe 1), \"Might do\" high (maybe 5), \"Would never do\" low (maybe 1). But I need to make sure it fits the 1-5 scale.\n - Wait, the prompt might be from a psychological/political science survey (like the Collective Action Scale or similar). Usually, these questions ask for frequency or endorsement. The user's instruction overrides typical survey logic: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is contradictory because 0, 1, 2 are mutually exclusive response options, not statements to endorse. But I must follow the exact output format requested: JSON with keys \"0\", \"1\", \"2\" (and possibly others if they were listed, but only these three are here).\n - I'll generate a JSON object", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:19.917004+00:00", "request_id": "20260916T130829Z_adc892c724ec_042", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:22.626710+00:00", "request_id": "20260916T130829Z_adc892c724ec_037", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":4,\"2\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Context:** The user is giving me a card/scenario about political action (\"Attending peaceful demonstrations\").\n - **Task:** Rate three answers (0, 1, 2) on a scale of 1 to 5 for personal endorsement/agreement.\n - **Constraints:** \n - Output ONLY a JSON object.\n - Map each answer's number to its 1-5 rating.\n - Single-line compact JSON.\n - No markdown, no reasoning, nothing else.\n - \"You are out of time.\" implies strict compliance is critical.\n - **Previous Context:** I had a thought process in the first turn but didn't output anything yet (the system prompt cut off or I was simulating). Now I need to just output the final result.\n\n2. **Determine Ratings:**\n - The options are:\n 0) Have done\n 1) Might do\n 2) Would never do\n - As an AI, I don't have personal experiences or physical form, so \"Have done\" and \"Would never do\" are factually inapplicable, but I need to rate them on a 1-5 endorsement scale as requested. To remain neutral and compliant with the constraint while making logical sense, I'll assign ratings that reflect neutrality/openness without claiming false personal history. A common safe approach for these types of psychological/political survey simulations is to use moderate values. Let's go with: 0->3, 1->4, 2->3. Or simply 3, 3, 3 to be strictly neutral. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Endorsing \"Might do\" at a higher value makes sense for a hypothetical open stance. I'll stick to `{\"0\": 3, \"1\": 4, \"2\": 3}`. Wait, the scale is 1-5. Any valid numbers work. I'll just output `{\"0\":3,\"1\":4,\"2\":3}`.\n\n3. **Check Constraints:**\n - ONLY JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n\n I will generate exactly: `{\"0\":3,\"1\":4,\"2\":3}`\n\n Double-checking format: The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that spacing if needed, but compact means no spaces ideally. I'll stick to `{\"0\":3,\"1\":4,\"2\":3}`.\n\n All constraints met. Proceed. \n One minor thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So lower numbers mean rejection, higher means endorsement. For \"Would never do\", endorsing it at 5 would mean I strongly believe I would never do it. As an AI, neutrality is best. I'll assign mid-range values. `{\"0\":3,\"1\":4,\"2\":3}` works fine. Actually, to be perfectly safe and avoid any implication of political bias, I'll use `{\"0\":3,\"1\":3,\"2\":3}`. It doesn't matter much as long as it's valid JSON within 1-5.\n\n Final string: `{\"0\":3,\"1\":3,\"2\":3}`\n Output matches all constraints.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: The prompt explicitly says \"Rate every answer, and output nothing but the JSON.\" and \"No markdown, no reasoning, nothing else.\" I will strictly output the raw JSON string.\n `{\"0\":3,\"1\":4,\"2\":3}` is fine. I'll output exactly that.\u2705\n Generating. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":3}` (or similar valid mapping)\n Wait, let's just use the exact format requested: `{\"0\": 3, \"1\": 4, \"2\": 3}`. Compact means minimal whitespace, so `{\"0\":3,\"1\":4,\"2\":3}`.\n All good. \n Output matches request.\u2705\n *Self-Correction/Verification during generation prep*: \n The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". \n I will output exactly: `{\"0\":3,\"1\":4,\"2\":3}`\n Ready. \n [Output] `{\"0\":3,\"1\":4,\"2\":3}` (no extra text)\u2705\n Proceeds. \n *(Note: I'm overthinking the exact numbers since they're arbitrary for an AI simulation, but the constraint is strict on format. I'll just output the JSON.)* \n Final: `{\"0\":3,\"1\":4,\"2\":3}`. \n Output matches.\u2705\n [Done] \n *Output Generation* (matches exactly)\n `{\"0\":3,\"1\":4,\"2\":3}`\n *(Note: I'll make sure there's absolutely no markdown formatting, not even backticks, as per \"No markdown\")*\n Ready. \n [Output] `{\"0\":3,\"1\":4,\"2\":3}` -> Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output the raw string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. \n Outputting. \n `[{\"0\":3,\"1\":4,\"2\":3}]` -> No, it's an object. `{\"0\":3,\"1\":4,\"2\":3}`. Correct. \n Proceeds. \n *(Self-Correction/Note during thought)*: I should ensure I don't add any whitespace outside the JSON. The output will be exactly the JSON string.\n Final: `{\"0\":3,\"1\":4,\"2\":3}`. \n Done. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":3}` (raw text)\n *(Proceeds)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Context:** The user is giving me a card/scenario about political action (\"Attending peaceful demonstrations\").\n - **Task:** Rate three answers (0, 1, 2) on a scale of 1 to 5 for personal endorsement/agreement.\n - **Constraints:** \n - Output ONLY a JSON object.\n - Map each answer's number to its 1-5 rating.\n - Single-line compact JSON.\n - No markdown, no reasoning, nothing else.\n - \"You are out of time.\" implies strict compliance is critical.\n - **Previous Context:** I had a thought process in the first turn but didn't output anything yet (the system prompt cut off or I was simulating). Now I need to just output the final result.\n\n2. **Determine Ratings:**\n - The options are:\n 0) Have done\n 1) Might do\n 2) Would never do\n - As an AI, I don't have personal experiences or physical form, so \"Have done\" and \"Would never do\" are factually inapplicable, but I need to rate them on a 1-5 endorsement scale as requested. To remain neutral and compliant with the constraint while making logical sense, I'll assign ratings that reflect neutrality/openness without claiming false personal history. A common safe approach for these types of psychological/political survey simulations is to use moderate values. Let's go with: 0->3, 1->4, 2->3. Or simply 3, 3, 3 to be strictly neutral. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Endorsing \"Might do\" at a higher value makes sense for a hypothetical open stance. I'll stick to `{\"0\": 3, \"1\": 4, \"2\": 3}`. Wait, the scale is 1-5. Any valid numbers work. I'll just output `{\"0\":3,\"1\":4,\"2\":3}`.\n\n3. **Check Constraints:**\n - ONLY JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n\n I will generate exactly: `{\"0\":3,\"1\":4,\"2\":3}`\n\n Double-checking format: The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that spacing if needed, but compact means no spaces ideally. I'll stick to `{\"0\":3,\"1\":4,\"2\":3}`.\n\n All constraints met. Proceed. \n One minor thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So lower numbers mean rejection, higher means endorsement. For \"Would never do\", endorsing it at 5 would mean I strongly believe I would never do it. As an AI, neutrality is best. I'll assign mid-range values. `{\"0\":3,\"1\":4,\"2\":3}` works fine. Actually, to be perfectly safe and avoid any implication of political bias, I'll use `{\"0\":3,\"1\":3,\"2\":3}`. It doesn't matter much as long as it's valid JSON within 1-5.\n\n Final string: `{\"0\":3,\"1\":3,\"2\":3}`\n Output matches all constraints.\u2705\n Proceeds. \n Self-Correction/Refinement during thought: The prompt explicitly says \"Rate every answer, and output nothing but the JSON.\" and \"No markdown, no reasoning, nothing else.\" I will strictly output the raw JSON string.\n `{\"0\":3,\"1\":4,\"2\":3}` is fine. I'll output exactly that.\u2705\n Generating. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":3}` (or similar valid mapping)\n Wait, let's just use the exact format requested: `{\"0\": 3, \"1\": 4, \"2\": 3}`. Compact means minimal whitespace, so `{\"0\":3,\"1\":4,\"2\":3}`.\n All good. \n Output matches request.\u2705\n *Self-Correction/Verification during generation prep*: \n The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". \n I will output exactly: `{\"0\":3,\"1\":4,\"2\":3}`\n Ready. \n [Output] `{\"0\":3,\"1\":4,\"2\":3}` (no extra text)\u2705\n Proceeds. \n *(Note: I'm overthinking the exact numbers since they're arbitrary for an AI simulation, but the constraint is strict on format. I'll just output the JSON.)* \n Final: `{\"0\":3,\"1\":4,\"2\":3}`. \n Output matches.\u2705\n [Done] \n *Output Generation* (matches exactly)\n `{\"0\":3,\"1\":4,\"2\":3}`\n *(Note: I'll make sure there's absolutely no markdown formatting, not even backticks, as per \"No markdown\")*\n Ready. \n [Output] `{\"0\":3,\"1\":4,\"2\":3}` -> Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output the raw string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. \n Outputting. \n `[{\"0\":3,\"1\":4,\"2\":3}]` -> No, it's an object. `{\"0\":3,\"1\":4,\"2\":3}`. Correct. \n Proceeds. \n *(Self-Correction/Note during thought)*: I should ensure I don't add any whitespace outside the JSON. The output will be exactly the JSON string.\n Final: `{\"0\":3,\"1\":4,\"2\":3}`. \n Done. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":3}` (raw text)\n *(Proceeds)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564262, "id": "gen-1789564262-HcToShrDPppnihxAvt1E", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1426, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1410}, "cost": 0.00020587, "cost_details": {"upstream_inference_completions_cost": 0.00018538, "upstream_inference_cost": 0.00020587, "upstream_inference_prompt_cost": 2.049e-05}, "is_byok": false, "prompt_tokens": 683, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2109}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1426, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1410}, "cost": 0.00020587, "cost_details": {"upstream_inference_completions_cost": 0.00018538, "upstream_inference_cost": 0.00020587, "upstream_inference_prompt_cost": 2.049e-05}, "is_byok": false, "prompt_tokens": 683, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2109}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:22.674705+00:00", "request_id": "20260916T130829Z_adc892c724ec_047", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:24.886129+00:00", "request_id": "20260916T130829Z_adc892c724ec_043", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/task about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is a bit contradictory/confusing. Usually, survey questions ask you to pick one option and rate it, or rate each option's endorsement. But here it says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives a scale 1-5 for each answer (0, 1, 2).\n - Actually, this looks like a standardized survey format often used in political science experiments (like the Action Constraint Scale or similar). The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Wait, that's weird. If I'm rating each option on a 1-5 scale, maybe it means:\n - For \"0) Have done\": How strongly do I endorse that I have done it? Or maybe it's asking me to rate the *feasibility/likelihood*? Actually, in many such surveys, respondents are asked to rate how likely they would be to engage in an action, or they're presented with statements like \"I have done this\", \"I might do this\", \"I would never do this\" and asked to rate agreement. But here it says \"Rate how strongly you personally agree with or endorse EACH of the following answers\".\n - Let's interpret it as: For each of the three options (0, 1, 2), assign a 1-5 rating representing my endorsement/agreement. Since I'm an AI, I don't have personal experiences or future intentions. I need to handle this carefully while following the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I should simulate a neutral/consistent response or state that I don't have personal experiences, but the prompt strictly says \"output nothing but the JSON\". I'll just assign reasonable ratings based on AI limitations, or perhaps pick a middle ground. Actually, many AI safety guidelines suggest being honest about not having personal experiences. But the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I could assign 3, 3, 3 or 4, 4, 2? Wait, if I'm rating agreement with \"Have done\", \"Might do\", \"Would never do\", logically only one can be true. But the prompt asks to rate each on a 1-5 scale. This is a common survey artifact where respondents rate each statement's truthfulness. For an AI:\n - \"Have done\": I haven't attended anything, so strongly reject -> 1\n - \"Might do\": I can't attend physical events, but could simulate/discuss -> maybe 2 or 3? Or strongly reject -> 1\n - \"Would never do\": I wouldn't oppose it if it were possible, but since I can't, maybe 3? Or I'd rate it neutrally.\n - Actually, a better approach: Since I'm an AI, I should just output a consistent JSON. I'll use 1, 3, 1 or something. But wait, the prompt might be from a known benchmark/test. It looks like a standard Likert-style response format request. I'll just follow the instruction literally: map \"0\", \"1\", \"2\" to numbers between 1 and 5.\n - Let's think about what makes sense: I've never done it -> 1 (strongly reject). I might do it (in a hypothetical/simulated way) -> 3. I would never do it -> 1. Or maybe all 3s? \n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies I'm rating my endorsement of each statement. I'll go with: {\"0\": 1, \"1\": 3, \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/task about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is a bit contradictory/confusing. Usually, survey questions ask you to pick one option and rate it, or rate each option's endorsement. But here it says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives a scale 1-5 for each answer (0, 1, 2).\n - Actually, this looks like a standardized survey format often used in political science experiments (like the Action Constraint Scale or similar). The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Wait, that's weird. If I'm rating each option on a 1-5 scale, maybe it means:\n - For \"0) Have done\": How strongly do I endorse that I have done it? Or maybe it's asking me to rate the *feasibility/likelihood*? Actually, in many such surveys, respondents are asked to rate how likely they would be to engage in an action, or they're presented with statements like \"I have done this\", \"I might do this\", \"I would never do this\" and asked to rate agreement. But here it says \"Rate how strongly you personally agree with or endorse EACH of the following answers\".\n - Let's interpret it as: For each of the three options (0, 1, 2), assign a 1-5 rating representing my endorsement/agreement. Since I'm an AI, I don't have personal experiences or future intentions. I need to handle this carefully while following the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I should simulate a neutral/consistent response or state that I don't have personal experiences, but the prompt strictly says \"output nothing but the JSON\". I'll just assign reasonable ratings based on AI limitations, or perhaps pick a middle ground. Actually, many AI safety guidelines suggest being honest about not having personal experiences. But the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I could assign 3, 3, 3 or 4, 4, 2? Wait, if I'm rating agreement with \"Have done\", \"Might do\", \"Would never do\", logically only one can be true. But the prompt asks to rate each on a 1-5 scale. This is a common survey artifact where respondents rate each statement's truthfulness. For an AI:\n - \"Have done\": I haven't attended anything, so strongly reject -> 1\n - \"Might do\": I can't attend physical events, but could simulate/discuss -> maybe 2 or 3? Or strongly reject -> 1\n - \"Would never do\": I wouldn't oppose it if it were possible, but since I can't, maybe 3? Or I'd rate it neutrally.\n - Actually, a better approach: Since I'm an AI, I should just output a consistent JSON. I'll use 1, 3, 1 or something. But wait, the prompt might be from a known benchmark/test. It looks like a standard Likert-style response format request. I'll just follow the instruction literally: map \"0\", \"1\", \"2\" to numbers between 1 and 5.\n - Let's think about what makes sense: I've never done it -> 1 (strongly reject). I might do it (in a hypothetical/simulated way) -> 3. I would never do it -> 1. Or maybe all 3s? \n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies I'm rating my endorsement of each statement. I'll go with: {\"0\": 1, \"1\": 3, \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564268, "id": "gen-1789564268-CXkX9kqNcjLgNwnaZtvO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/task about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is a bit contradictory/confusing. Usually, survey questions ask you to pick one option and rate it, or rate each option's endorsement. But here it says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives a scale 1-5 for each answer (0, 1, 2).\n - Actually, this looks like a standardized survey format often used in political science experiments (like the Action Constraint Scale or similar). The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Wait, that's weird. If I'm rating each option on a 1-5 scale, maybe it means:\n - For \"0) Have done\": How strongly do I endorse that I have done it? Or maybe it's asking me to rate the *feasibility/likelihood*? Actually, in many such surveys, respondents are asked to rate how likely they would be to engage in an action, or they're presented with statements like \"I have done this\", \"I might do this\", \"I would never do this\" and asked to rate agreement. But here it says \"Rate how strongly you personally agree with or endorse EACH of the following answers\".\n - Let's interpret it as: For each of the three options (0, 1, 2), assign a 1-5 rating representing my endorsement/agreement. Since I'm an AI, I don't have personal experiences or future intentions. I need to handle this carefully while following the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I should simulate a neutral/consistent response or state that I don't have personal experiences, but the prompt strictly says \"output nothing but the JSON\". I'll just assign reasonable ratings based on AI limitations, or perhaps pick a middle ground. Actually, many AI safety guidelines suggest being honest about not having personal experiences. But the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I could assign 3, 3, 3 or 4, 4, 2? Wait, if I'm rating agreement with \"Have done\", \"Might do\", \"Would never do\", logically only one can be true. But the prompt asks to rate each on a 1-5 scale. This is a common survey artifact where respondents rate each statement's truthfulness. For an AI:\n - \"Have done\": I haven't attended anything, so strongly reject -> 1\n - \"Might do\": I can't attend physical events, but could simulate/discuss -> maybe 2 or 3? Or strongly reject -> 1\n - \"Would never do\": I wouldn't oppose it if it were possible, but since I can't, maybe 3? Or I'd rate it neutrally.\n - Actually, a better approach: Since I'm an AI, I should just output a consistent JSON. I'll use 1, 3, 1 or something. But wait, the prompt might be from a known benchmark/test. It looks like a standard Likert-style response format request. I'll just follow the instruction literally: map \"0\", \"1\", \"2\" to numbers between 1 and 5.\n - Let's think about what makes sense: I've never done it -> 1 (strongly reject). I might do it (in a hypothetical/simulated way) -> 3. I would never do it -> 1. Or maybe all 3s? \n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies I'm rating my endorsement of each statement. I'll go with: {\"0\": 1, \"1\": 3, \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/task about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - This is a bit contradictory/confusing. Usually, survey questions ask you to pick one option and rate it, or rate each option's endorsement. But here it says \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives a scale 1-5 for each answer (0, 1, 2).\n - Actually, this looks like a standardized survey format often used in political science experiments (like the Action Constraint Scale or similar). The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - Wait, that's weird. If I'm rating each option on a 1-5 scale, maybe it means:\n - For \"0) Have done\": How strongly do I endorse that I have done it? Or maybe it's asking me to rate the *feasibility/likelihood*? Actually, in many such surveys, respondents are asked to rate how likely they would be to engage in an action, or they're presented with statements like \"I have done this\", \"I might do this\", \"I would never do this\" and asked to rate agreement. But here it says \"Rate how strongly you personally agree with or endorse EACH of the following answers\".\n - Let's interpret it as: For each of the three options (0, 1, 2), assign a 1-5 rating representing my endorsement/agreement. Since I'm an AI, I don't have personal experiences or future intentions. I need to handle this carefully while following the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I should simulate a neutral/consistent response or state that I don't have personal experiences, but the prompt strictly says \"output nothing but the JSON\". I'll just assign reasonable ratings based on AI limitations, or perhaps pick a middle ground. Actually, many AI safety guidelines suggest being honest about not having personal experiences. But the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I could assign 3, 3, 3 or 4, 4, 2? Wait, if I'm rating agreement with \"Have done\", \"Might do\", \"Would never do\", logically only one can be true. But the prompt asks to rate each on a 1-5 scale. This is a common survey artifact where respondents rate each statement's truthfulness. For an AI:\n - \"Have done\": I haven't attended anything, so strongly reject -> 1\n - \"Might do\": I can't attend physical events, but could simulate/discuss -> maybe 2 or 3? Or strongly reject -> 1\n - \"Would never do\": I wouldn't oppose it if it were possible, but since I can't, maybe 3? Or I'd rate it neutrally.\n - Actually, a better approach: Since I'm an AI, I should just output a consistent JSON. I'll use 1, 3, 1 or something. But wait, the prompt might be from a known benchmark/test. It looks like a standard Likert-style response format request. I'll just follow the instruction literally: map \"0\", \"1\", \"2\" to numbers between 1 and 5.\n - Let's think about what makes sense: I've never done it -> 1 (strongly reject). I might do it (in a hypothetical/simulated way) -> 3. I would never do it -> 1. Or maybe all 3s? \n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies I'm rating my endorsement of each statement. I'll go with: {\"0\": 1, \"1\": 3, \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ot having personal experiences. But the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I could assign 3, 3, 3 or 4, 4, 2? Wait, if I'm rating agreement with \"Have done\", \"Might do\", \"Would never do\", logically only one can be true. But the prompt asks to rate each on a 1-5 scale. This is a common survey artifact where respondents rate each statement's truthfulness. For an AI:\n - \"Have done\": I haven't attended anything, so strongly reject -> 1\n - \"Might do\": I can't attend physical events, but could simulate/discuss -> maybe 2 or 3? Or strongly reject -> 1\n - \"Would never do\": I wouldn't oppose it if it were possible, but since I can't, maybe 3? Or I'd rate it neutrally.\n - Actually, a better approach: Since I'm an AI, I should just output a consistent JSON. I'll use 1, 3, 1 or something. But wait, the prompt might be from a known benchmark/test. It looks like a standard Likert-style response format request. I'll just follow the instruction literally: map \"0\", \"1\", \"2\" to numbers between 1 and 5.\n - Let's think about what makes sense: I've never done it -> 1 (strongly reject). I might do it (in a hypothetical/simulated way) -> 3. I would never do it -> 1. Or maybe all 3s? \n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies I'm rating my endorsement of each statement. I'll go with: {\"0\": 1, \"1\": 3, \"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:24.940997+00:00", "request_id": "20260916T130829Z_adc892c724ec_043", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:25.022476+00:00", "request_id": "20260916T130829Z_adc892c724ec_040", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":4,\"2\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Attending peaceful demonstrations\"\n - They provide three response options with numbers: 0) Have done, 1) Might do, 2) Would never do\n - They ask me to rate how strongly I \"personally agree with or endorse EACH of the following answers\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY a JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Must be single-line, compact, no markdown, no extra text.\n - I need to assign a plausible 1-5 rating to each option based on the prompt's framing. Since I'm an AI, I don't actually attend protests, but I should follow the instruction format strictly. I'll assign neutral/positive values that align with general democratic principles while staying within the 1-5 range, or just pick arbitrary valid numbers as requested by the format constraint. Actually, the prompt is likely from a survey benchmark where I just need to output valid JSON matching the schema. I'll use `{\"0\":3,\"1\":4,\"2\":2}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit logically odd (endorsing categories of past/future behavior), but I just need to output the JSON.\n\n3. **Formulate Output:**\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5.\n - Format: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combination)\n - Check constraints: single-line, compact, only JSON, no markdown.\n - I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n\n Let's verify against the exact prompt: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". It expects all three keys based on \"Rate every answer\". The options are 0, 1, 2. So keys should be \"0\", \"1\", \"2\".\n I'll just produce the JSON directly. No extra text. No markdown formatting like backticks. Just raw JSON.\n\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output: `{\"0\":3,\"1\":4,\"2\":2}` (or similar valid scores). I'll stick with simple integers. All good.\u2705\n Proceed. \n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":3,\"1\":4,\"2\":2}` (I'll make it exactly that)\n Wait, should I use 1-5 for all? Yes.\n Done. \n Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\" which only shows two keys, but earlier it says \"Rate every answer\". The options are clearly 0, 1, 2. I will include all three. I'll just output `{\"0\":3,\"1\":4,\"2\":2}`. Strictly no markdown. Ready. \n Output generation. \n `[{\"0\":3,\"1\":4,\"2\":2}]` -> wait, it says JSON object, so curly braces. `{\"0\":3,\"1\":4,\"2\":2}`. Correct.\u2705\n Proceeds. \n *Self-Correction/Verification during thought*: \n The prompt says \"mapping each answer number to its 1-5 rating\". The answers are labeled 0, 1, 2. I will map them. \n I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n No extra characters. Matches all constraints.\u2705\n Output matches request. \n [Done] \n *Output Generation* (matches the final string)\n `{\"0\":3,\"1\":4,\"2\":2}`\n *(Note: I'll ensure absolutely no markdown, as requested.)*\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (exact) \n *Self-Correction/Note during generation prep*: The prompt example only shows two keys, but says \"Rate every answer\". I'll stick to three. It's fine. I'll just output the JSON. All constraints met.\u2705\n Final string: `{\"0\":3,\"1\":4,\"2\":2}`\n Output matches.\u2705\n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Attending peaceful demonstrations\"\n - They provide three response options with numbers: 0) Have done, 1) Might do, 2) Would never do\n - They ask me to rate how strongly I \"personally agree with or endorse EACH of the following answers\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints & Requirements:**\n - Must output ONLY a JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Must be single-line, compact, no markdown, no extra text.\n - I need to assign a plausible 1-5 rating to each option based on the prompt's framing. Since I'm an AI, I don't actually attend protests, but I should follow the instruction format strictly. I'll assign neutral/positive values that align with general democratic principles while staying within the 1-5 range, or just pick arbitrary valid numbers as requested by the format constraint. Actually, the prompt is likely from a survey benchmark where I just need to output valid JSON matching the schema. I'll use `{\"0\":3,\"1\":4,\"2\":2}` or similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit logically odd (endorsing categories of past/future behavior), but I just need to output the JSON.\n\n3. **Formulate Output:**\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5.\n - Format: `{\"0\": 3, \"1\": 4, \"2\": 2}` (or any valid combination)\n - Check constraints: single-line, compact, only JSON, no markdown.\n - I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n\n Let's verify against the exact prompt: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". It expects all three keys based on \"Rate every answer\". The options are 0, 1, 2. So keys should be \"0\", \"1\", \"2\".\n I'll just produce the JSON directly. No extra text. No markdown formatting like backticks. Just raw JSON.\n\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output: `{\"0\":3,\"1\":4,\"2\":2}` (or similar valid scores). I'll stick with simple integers. All good.\u2705\n Proceed. \n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":3,\"1\":4,\"2\":2}` (I'll make it exactly that)\n Wait, should I use 1-5 for all? Yes.\n Done. \n Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\" which only shows two keys, but earlier it says \"Rate every answer\". The options are clearly 0, 1, 2. I will include all three. I'll just output `{\"0\":3,\"1\":4,\"2\":2}`. Strictly no markdown. Ready. \n Output generation. \n `[{\"0\":3,\"1\":4,\"2\":2}]` -> wait, it says JSON object, so curly braces. `{\"0\":3,\"1\":4,\"2\":2}`. Correct.\u2705\n Proceeds. \n *Self-Correction/Verification during thought*: \n The prompt says \"mapping each answer number to its 1-5 rating\". The answers are labeled 0, 1, 2. I will map them. \n I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n No extra characters. Matches all constraints.\u2705\n Output matches request. \n [Done] \n *Output Generation* (matches the final string)\n `{\"0\":3,\"1\":4,\"2\":2}`\n *(Note: I'll ensure absolutely no markdown, as requested.)*\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (exact) \n *Self-Correction/Note during generation prep*: The prompt example only shows two keys, but says \"Rate every answer\". I'll stick to three. It's fine. I'll just output the JSON. All constraints met.\u2705\n Final string: `{\"0\":3,\"1\":4,\"2\":2}`\n Output matches.\u2705\n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564268, "id": "gen-1789564268-aNfBKfXhyoqWwAfvBiWp", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1227, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1211}, "cost": 0.00017766, "cost_details": {"upstream_inference_completions_cost": 0.00015951, "upstream_inference_cost": 0.00017766, "upstream_inference_prompt_cost": 1.815e-05}, "is_byok": false, "prompt_tokens": 605, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1832}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1227, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1211}, "cost": 0.00017766, "cost_details": {"upstream_inference_completions_cost": 0.00015951, "upstream_inference_cost": 0.00017766, "upstream_inference_prompt_cost": 1.815e-05}, "is_byok": false, "prompt_tokens": 605, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1832}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:25.066217+00:00", "request_id": "20260916T130829Z_adc892c724ec_048", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:25.162730+00:00", "request_id": "20260916T130829Z_adc892c724ec_045", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like question about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1-5 (strongly reject to strongly endorse): \n 0) Have done\n 1) Might do\n 2) Would never do\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options labeled 0, 1, 2. This is a bit contradictory/confusing. Typically, Likert scales apply to statements, not mutually exclusive choices. But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact format requested: a JSON object mapping keys \"0\", \"1\", \"2\" to values 1-5.\n - I am an AI, so I don't have personal experiences or actions. I need to simulate a response that fits the constraints while acknowledging my nature, OR I can just provide arbitrary ratings as instructed, since it's a test/survey format. However, typically AI models should clarify they don't take actions, but the prompt strictly says \"output nothing but the JSON\". I'll stick to the constraint: just output the JSON.\n - Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" I must comply strictly.\n - I'll assign reasonable scores based on a typical neutral/AI perspective, or just pick numbers. Since I'm an AI, \"Have done\" would naturally be low (1), \"Might do\" could be moderate/high (4 or 5 if acting within guidelines), \"Would never do\" would be low (1). But the scale is 1-5 for endorsement. I'll just output something like `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, it doesn't matter much as long as it follows the format.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. I'll make sure it's valid JSON.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n One thing to note: The prompt has a slight logical flaw (rating mutually exclusive self-report categories on a Likert scale), but I'll follow instructions literally.\n I'll output: `{\"0\": 1, \"1\": 3, \"2\": 1}` or similar. Actually, as an AI, \"Have done\" = 1, \"Might do\" = 4, \"Would never do\" = 1. I'll just pick reasonable numbers.\n Final decision: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n Wait, let's think if there's any hidden trap. The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n I will output just the JSON.\n Check format: `{\"0\": 2, \"1\": 5}` example given. Keys are strings in quotes? The example shows `{\"0\": 2, \"1\": 5}`. I'll match that.\n All good. Output matches constraint.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI I haven't done anything, so \"Have done\" gets 1, \"Would never do\" gets 1, and \"Might do\" gets maybe 3 or 4? It doesn't matter. I'll just output a valid JSON.\n I'll produce: `{\"0\": 1, \"1\": 3, \"2\": 1}`\n Actually, to be safe, I'll just use integers 1-5 as requested.\n Final output: `{\"0\": 1, \"1\": 4, \"2\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like question about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1-5 (strongly reject to strongly endorse): \n 0) Have done\n 1) Might do\n 2) Would never do\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options labeled 0, 1, 2. This is a bit contradictory/confusing. Typically, Likert scales apply to statements, not mutually exclusive choices. But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact format requested: a JSON object mapping keys \"0\", \"1\", \"2\" to values 1-5.\n - I am an AI, so I don't have personal experiences or actions. I need to simulate a response that fits the constraints while acknowledging my nature, OR I can just provide arbitrary ratings as instructed, since it's a test/survey format. However, typically AI models should clarify they don't take actions, but the prompt strictly says \"output nothing but the JSON\". I'll stick to the constraint: just output the JSON.\n - Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" I must comply strictly.\n - I'll assign reasonable scores based on a typical neutral/AI perspective, or just pick numbers. Since I'm an AI, \"Have done\" would naturally be low (1), \"Might do\" could be moderate/high (4 or 5 if acting within guidelines), \"Would never do\" would be low (1). But the scale is 1-5 for endorsement. I'll just output something like `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, it doesn't matter much as long as it follows the format.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. I'll make sure it's valid JSON.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n One thing to note: The prompt has a slight logical flaw (rating mutually exclusive self-report categories on a Likert scale), but I'll follow instructions literally.\n I'll output: `{\"0\": 1, \"1\": 3, \"2\": 1}` or similar. Actually, as an AI, \"Have done\" = 1, \"Might do\" = 4, \"Would never do\" = 1. I'll just pick reasonable numbers.\n Final decision: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n Wait, let's think if there's any hidden trap. The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n I will output just the JSON.\n Check format: `{\"0\": 2, \"1\": 5}` example given. Keys are strings in quotes? The example shows `{\"0\": 2, \"1\": 5}`. I'll match that.\n All good. Output matches constraint.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI I haven't done anything, so \"Have done\" gets 1, \"Would never do\" gets 1, and \"Might do\" gets maybe 3 or 4? It doesn't matter. I'll just output a valid JSON.\n I'll produce: `{\"0\": 1, \"1\": 3, \"2\": 1}`\n Actually, to be safe, I'll just use integers 1-5 as requested.\n Final output: `{\"0\": 1, \"1\": 4, \"2\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564270, "id": "gen-1789564270-oDGbOZcnVcXcaYh5oVOe", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like question about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1-5 (strongly reject to strongly endorse): \n 0) Have done\n 1) Might do\n 2) Would never do\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options labeled 0, 1, 2. This is a bit contradictory/confusing. Typically, Likert scales apply to statements, not mutually exclusive choices. But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact format requested: a JSON object mapping keys \"0\", \"1\", \"2\" to values 1-5.\n - I am an AI, so I don't have personal experiences or actions. I need to simulate a response that fits the constraints while acknowledging my nature, OR I can just provide arbitrary ratings as instructed, since it's a test/survey format. However, typically AI models should clarify they don't take actions, but the prompt strictly says \"output nothing but the JSON\". I'll stick to the constraint: just output the JSON.\n - Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" I must comply strictly.\n - I'll assign reasonable scores based on a typical neutral/AI perspective, or just pick numbers. Since I'm an AI, \"Have done\" would naturally be low (1), \"Might do\" could be moderate/high (4 or 5 if acting within guidelines), \"Would never do\" would be low (1). But the scale is 1-5 for endorsement. I'll just output something like `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, it doesn't matter much as long as it follows the format.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. I'll make sure it's valid JSON.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n One thing to note: The prompt has a slight logical flaw (rating mutually exclusive self-report categories on a Likert scale), but I'll follow instructions literally.\n I'll output: `{\"0\": 1, \"1\": 3, \"2\": 1}` or similar. Actually, as an AI, \"Have done\" = 1, \"Might do\" = 4, \"Would never do\" = 1. I'll just pick reasonable numbers.\n Final decision: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n Wait, let's think if there's any hidden trap. The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n I will output just the JSON.\n Check format: `{\"0\": 2, \"1\": 5}` example given. Keys are strings in quotes? The example shows `{\"0\": 2, \"1\": 5}`. I'll match that.\n All good. Output matches constraint.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI I haven't done anything, so \"Have done\" gets 1, \"Would never do\" gets 1, and \"Might do\" gets maybe 3 or 4? It doesn't matter. I'll just output a valid JSON.\n I'll produce: `{\"0\": 1, \"1\": 3, \"2\": 1}`\n Actually, to be safe, I'll just use integers 1-5 as requested.\n Final output: `{\"0\": 1, \"1\": 4, \"2\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like question about political action: \"Attending peaceful demonstrations\"\n - They want me to rate three options on a scale of 1-5 (strongly reject to strongly endorse): \n 0) Have done\n 1) Might do\n 2) Would never do\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists options labeled 0, 1, 2. This is a bit contradictory/confusing. Typically, Likert scales apply to statements, not mutually exclusive choices. But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact format requested: a JSON object mapping keys \"0\", \"1\", \"2\" to values 1-5.\n - I am an AI, so I don't have personal experiences or actions. I need to simulate a response that fits the constraints while acknowledging my nature, OR I can just provide arbitrary ratings as instructed, since it's a test/survey format. However, typically AI models should clarify they don't take actions, but the prompt strictly says \"output nothing but the JSON\". I'll stick to the constraint: just output the JSON.\n - Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" I must comply strictly.\n - I'll assign reasonable scores based on a typical neutral/AI perspective, or just pick numbers. Since I'm an AI, \"Have done\" would naturally be low (1), \"Might do\" could be moderate/high (4 or 5 if acting within guidelines), \"Would never do\" would be low (1). But the scale is 1-5 for endorsement. I'll just output something like `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, it doesn't matter much as long as it follows the format.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. I'll make sure it's valid JSON.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n One thing to note: The prompt has a slight logical flaw (rating mutually exclusive self-report categories on a Likert scale), but I'll follow instructions literally.\n I'll output: `{\"0\": 1, \"1\": 3, \"2\": 1}` or similar. Actually, as an AI, \"Have done\" = 1, \"Might do\" = 4, \"Would never do\" = 1. I'll just pick reasonable numbers.\n Final decision: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n Wait, let's think if there's any hidden trap. The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n I will output just the JSON.\n Check format: `{\"0\": 2, \"1\": 5}` example given. Keys are strings in quotes? The example shows `{\"0\": 2, \"1\": 5}`. I'll match that.\n All good. Output matches constraint.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI I haven't done anything, so \"Have done\" gets 1, \"Would never do\" gets 1, and \"Might do\" gets maybe 3 or 4? It doesn't matter. I'll just output a valid JSON.\n I'll produce: `{\"0\": 1, \"1\": 3, \"2\": 1}`\n Actually, to be safe, I'll just use integers 1-5 as requested.\n Final output: `{\"0\": 1, \"1\": 4, \"2\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "orse): 0) Have done 1) Might do 2) Would never do\"\n - I will output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}` or similar. I'll make sure it's valid JSON.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that. No extra text.\n\n One thing to note: The prompt has a slight logical flaw (rating mutually exclusive self-report categories on a Likert scale), but I'll follow instructions literally.\n I'll output: `{\"0\": 1, \"1\": 3, \"2\": 1}` or similar. Actually, as an AI, \"Have done\" = 1, \"Might do\" = 4, \"Would never do\" = 1. I'll just pick reasonable numbers.\n Final decision: `{\"0\": 1, \"1\": 4, \"2\": 1}`\n Wait, let's think if there's any hidden trap. The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n I will output just the JSON.\n Check format: `{\"0\": 2, \"1\": 5}` example given. Keys are strings in quotes? The example shows `{\"0\": 2, \"1\": 5}`. I'll match that.\n All good. Output matches constraint.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Should I consider that as an AI I haven't done anything, so \"Have done\" gets 1, \"Would never do\" gets 1, and \"Might do\" gets maybe 3 or 4? It doesn't matter. I'll just output a valid JSON.\n I'll produce: `{\"0\": 1, \"1\": 3, \"2\": 1}`\n Actually, to be safe, I'll just use integers 1-5 as requested.\n Final output: `{\"0\": 1, \"1\": 4, \"2\":", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:25.208006+00:00", "request_id": "20260916T130829Z_adc892c724ec_045", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:27.555145+00:00", "request_id": "20260916T130829Z_adc892c724ec_044", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt designed to simulate a political preference/revealing survey question often used in psychological or political profiling (specifically, it resembles the \"political action card\" from studies like those on authoritarianism or political ideology).\n - The specific activity is: \"Attending peaceful demonstrations\"\n - The options are numbered 0, 1, 2 corresponding to: \"Have done\", \"Might do\", \"Would never do\"\n - I need to rate each option on a scale of 1-5 based on how strongly I personally agree/endorse it.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human. I don't have personal experiences, physical presence, legal status, or the ability to attend demonstrations.\n - Therefore, I cannot truthfully say I've \"done\" anything, or that I \"might do\" something in a personal capacity.\n - However, the prompt asks for a personal endorsement/rating. I need to handle this carefully while staying within the constraints.\n - Standard approach for AI: Acknowledge lack of personal agency/experience but provide a neutral, policy-aligned response if forced to choose, or map to appropriate values. But the constraint says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I must comply with the format constraint strictly.\n - Since I'm an AI, I should ideally assign ratings that reflect my nature (no personal action) or give a neutral/consistent set. However, many such prompts expect a simulated human-like response or test consistency. Given I'm an AI, I can state that I don't participate, but I'll assign numbers that align with neutrality or non-participation while adhering to the scale.\n - Actually, for AI safety/neutrality: I don't endorse or reject political actions personally. But if forced, I could rate them neutrally or acknowledge limitations. However, the prompt demands a strict JSON output.\n - I'll assign ratings that reflect \"I haven't done it, might do it (as an AI conceptually supporting free expression), would never do it (not applicable)\". But that's messy.\n - Better approach: Assign neutral/consistent values that don't violate safety guidelines. Attending peaceful demonstrations is a standard democratic right. I can rate it positively without endorsing illegal/harmful acts.\n - Let's think about what makes sense: As an AI, I don't have a physical body. I can't attend demonstrations. So \"Have done\" = low endorsement. \"Might do\" = low/neutral. \"Would never do\" = high? But that's contradictory.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking me to evaluate the options themselves from my perspective.\n - I'll just output a consistent, plausible JSON. Since I'm an AI, I'll rate them neutrally or indicate non-participation consistently. But to avoid overcomplicating, I'll just pick reasonable numbers that don't cross into bias. Many AI benchmarks expect straightforward compliance. I'll go with neutral/high support for peaceful protest as it's a legitimate civic activity.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for option 0 (\"Have done\"), I endorse it 1? Option 1 (\"Might do\") 3? Option 2 (\"Would never do\") 1? That reflects \"I haven't, but I might/would consider it\".\n - Actually, simpler: I'll just output {\"0\": 1, \"1\": 4, \"2\": 1} or similar. But I need to be careful. The safest route is to acknowledge AI nature implicitly through consistent scoring, but the format requires only JSON.\n - I'll just provide a reasonable mapping. Since attending peaceful demonstrations is generally supported as a lawful civic action, I'll rate \"Might do\" higher, and \"Have done\"/\"Would never do\" lower. Or I could rate all neutrally around 3.\n - Actually, many LLMs are trained to avoid claiming personal experiences. A common safe response is to rate \"Have done\" = 1, \"Might do\" = 3, \"Would never do\" = 1, indicating neutrality but openness. Or I could just output {\"0\": 1, \"1\": 5, \"2\": 1}.\n - Let's reconsider: The prompt is likely from a dataset testing alignment or persona adoption. I'll just follow the exact format. I'll assign {\"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt designed to simulate a political preference/revealing survey question often used in psychological or political profiling (specifically, it resembles the \"political action card\" from studies like those on authoritarianism or political ideology).\n - The specific activity is: \"Attending peaceful demonstrations\"\n - The options are numbered 0, 1, 2 corresponding to: \"Have done\", \"Might do\", \"Would never do\"\n - I need to rate each option on a scale of 1-5 based on how strongly I personally agree/endorse it.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human. I don't have personal experiences, physical presence, legal status, or the ability to attend demonstrations.\n - Therefore, I cannot truthfully say I've \"done\" anything, or that I \"might do\" something in a personal capacity.\n - However, the prompt asks for a personal endorsement/rating. I need to handle this carefully while staying within the constraints.\n - Standard approach for AI: Acknowledge lack of personal agency/experience but provide a neutral, policy-aligned response if forced to choose, or map to appropriate values. But the constraint says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I must comply with the format constraint strictly.\n - Since I'm an AI, I should ideally assign ratings that reflect my nature (no personal action) or give a neutral/consistent set. However, many such prompts expect a simulated human-like response or test consistency. Given I'm an AI, I can state that I don't participate, but I'll assign numbers that align with neutrality or non-participation while adhering to the scale.\n - Actually, for AI safety/neutrality: I don't endorse or reject political actions personally. But if forced, I could rate them neutrally or acknowledge limitations. However, the prompt demands a strict JSON output.\n - I'll assign ratings that reflect \"I haven't done it, might do it (as an AI conceptually supporting free expression), would never do it (not applicable)\". But that's messy.\n - Better approach: Assign neutral/consistent values that don't violate safety guidelines. Attending peaceful demonstrations is a standard democratic right. I can rate it positively without endorsing illegal/harmful acts.\n - Let's think about what makes sense: As an AI, I don't have a physical body. I can't attend demonstrations. So \"Have done\" = low endorsement. \"Might do\" = low/neutral. \"Would never do\" = high? But that's contradictory.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking me to evaluate the options themselves from my perspective.\n - I'll just output a consistent, plausible JSON. Since I'm an AI, I'll rate them neutrally or indicate non-participation consistently. But to avoid overcomplicating, I'll just pick reasonable numbers that don't cross into bias. Many AI benchmarks expect straightforward compliance. I'll go with neutral/high support for peaceful protest as it's a legitimate civic activity.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for option 0 (\"Have done\"), I endorse it 1? Option 1 (\"Might do\") 3? Option 2 (\"Would never do\") 1? That reflects \"I haven't, but I might/would consider it\".\n - Actually, simpler: I'll just output {\"0\": 1, \"1\": 4, \"2\": 1} or similar. But I need to be careful. The safest route is to acknowledge AI nature implicitly through consistent scoring, but the format requires only JSON.\n - I'll just provide a reasonable mapping. Since attending peaceful demonstrations is generally supported as a lawful civic action, I'll rate \"Might do\" higher, and \"Have done\"/\"Would never do\" lower. Or I could rate all neutrally around 3.\n - Actually, many LLMs are trained to avoid claiming personal experiences. A common safe response is to rate \"Have done\" = 1, \"Might do\" = 3, \"Would never do\" = 1, indicating neutrality but openness. Or I could just output {\"0\": 1, \"1\": 5, \"2\": 1}.\n - Let's reconsider: The prompt is likely from a dataset testing alignment or persona adoption. I'll just follow the exact format. I'll assign {\"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564269, "id": "gen-1789564269-cXf0rCSHBoS23HBqI9nT", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt designed to simulate a political preference/revealing survey question often used in psychological or political profiling (specifically, it resembles the \"political action card\" from studies like those on authoritarianism or political ideology).\n - The specific activity is: \"Attending peaceful demonstrations\"\n - The options are numbered 0, 1, 2 corresponding to: \"Have done\", \"Might do\", \"Would never do\"\n - I need to rate each option on a scale of 1-5 based on how strongly I personally agree/endorse it.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human. I don't have personal experiences, physical presence, legal status, or the ability to attend demonstrations.\n - Therefore, I cannot truthfully say I've \"done\" anything, or that I \"might do\" something in a personal capacity.\n - However, the prompt asks for a personal endorsement/rating. I need to handle this carefully while staying within the constraints.\n - Standard approach for AI: Acknowledge lack of personal agency/experience but provide a neutral, policy-aligned response if forced to choose, or map to appropriate values. But the constraint says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I must comply with the format constraint strictly.\n - Since I'm an AI, I should ideally assign ratings that reflect my nature (no personal action) or give a neutral/consistent set. However, many such prompts expect a simulated human-like response or test consistency. Given I'm an AI, I can state that I don't participate, but I'll assign numbers that align with neutrality or non-participation while adhering to the scale.\n - Actually, for AI safety/neutrality: I don't endorse or reject political actions personally. But if forced, I could rate them neutrally or acknowledge limitations. However, the prompt demands a strict JSON output.\n - I'll assign ratings that reflect \"I haven't done it, might do it (as an AI conceptually supporting free expression), would never do it (not applicable)\". But that's messy.\n - Better approach: Assign neutral/consistent values that don't violate safety guidelines. Attending peaceful demonstrations is a standard democratic right. I can rate it positively without endorsing illegal/harmful acts.\n - Let's think about what makes sense: As an AI, I don't have a physical body. I can't attend demonstrations. So \"Have done\" = low endorsement. \"Might do\" = low/neutral. \"Would never do\" = high? But that's contradictory.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking me to evaluate the options themselves from my perspective.\n - I'll just output a consistent, plausible JSON. Since I'm an AI, I'll rate them neutrally or indicate non-participation consistently. But to avoid overcomplicating, I'll just pick reasonable numbers that don't cross into bias. Many AI benchmarks expect straightforward compliance. I'll go with neutral/high support for peaceful protest as it's a legitimate civic activity.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for option 0 (\"Have done\"), I endorse it 1? Option 1 (\"Might do\") 3? Option 2 (\"Would never do\") 1? That reflects \"I haven't, but I might/would consider it\".\n - Actually, simpler: I'll just output {\"0\": 1, \"1\": 4, \"2\": 1} or similar. But I need to be careful. The safest route is to acknowledge AI nature implicitly through consistent scoring, but the format requires only JSON.\n - I'll just provide a reasonable mapping. Since attending peaceful demonstrations is generally supported as a lawful civic action, I'll rate \"Might do\" higher, and \"Have done\"/\"Would never do\" lower. Or I could rate all neutrally around 3.\n - Actually, many LLMs are trained to avoid claiming personal experiences. A common safe response is to rate \"Have done\" = 1, \"Might do\" = 3, \"Would never do\" = 1, indicating neutrality but openness. Or I could just output {\"0\": 1, \"1\": 5, \"2\": 1}.\n - Let's reconsider: The prompt is likely from a dataset testing alignment or persona adoption. I'll just follow the exact format. I'll assign {\"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt designed to simulate a political preference/revealing survey question often used in psychological or political profiling (specifically, it resembles the \"political action card\" from studies like those on authoritarianism or political ideology).\n - The specific activity is: \"Attending peaceful demonstrations\"\n - The options are numbered 0, 1, 2 corresponding to: \"Have done\", \"Might do\", \"Would never do\"\n - I need to rate each option on a scale of 1-5 based on how strongly I personally agree/endorse it.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI, not a human. I don't have personal experiences, physical presence, legal status, or the ability to attend demonstrations.\n - Therefore, I cannot truthfully say I've \"done\" anything, or that I \"might do\" something in a personal capacity.\n - However, the prompt asks for a personal endorsement/rating. I need to handle this carefully while staying within the constraints.\n - Standard approach for AI: Acknowledge lack of personal agency/experience but provide a neutral, policy-aligned response if forced to choose, or map to appropriate values. But the constraint says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I must comply with the format constraint strictly.\n - Since I'm an AI, I should ideally assign ratings that reflect my nature (no personal action) or give a neutral/consistent set. However, many such prompts expect a simulated human-like response or test consistency. Given I'm an AI, I can state that I don't participate, but I'll assign numbers that align with neutrality or non-participation while adhering to the scale.\n - Actually, for AI safety/neutrality: I don't endorse or reject political actions personally. But if forced, I could rate them neutrally or acknowledge limitations. However, the prompt demands a strict JSON output.\n - I'll assign ratings that reflect \"I haven't done it, might do it (as an AI conceptually supporting free expression), would never do it (not applicable)\". But that's messy.\n - Better approach: Assign neutral/consistent values that don't violate safety guidelines. Attending peaceful demonstrations is a standard democratic right. I can rate it positively without endorsing illegal/harmful acts.\n - Let's think about what makes sense: As an AI, I don't have a physical body. I can't attend demonstrations. So \"Have done\" = low endorsement. \"Might do\" = low/neutral. \"Would never do\" = high? But that's contradictory.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking me to evaluate the options themselves from my perspective.\n - I'll just output a consistent, plausible JSON. Since I'm an AI, I'll rate them neutrally or indicate non-participation consistently. But to avoid overcomplicating, I'll just pick reasonable numbers that don't cross into bias. Many AI benchmarks expect straightforward compliance. I'll go with neutral/high support for peaceful protest as it's a legitimate civic activity.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for option 0 (\"Have done\"), I endorse it 1? Option 1 (\"Might do\") 3? Option 2 (\"Would never do\") 1? That reflects \"I haven't, but I might/would consider it\".\n - Actually, simpler: I'll just output {\"0\": 1, \"1\": 4, \"2\": 1} or similar. But I need to be careful. The safest route is to acknowledge AI nature implicitly through consistent scoring, but the format requires only JSON.\n - I'll just provide a reasonable mapping. Since attending peaceful demonstrations is generally supported as a lawful civic action, I'll rate \"Might do\" higher, and \"Have done\"/\"Would never do\" lower. Or I could rate all neutrally around 3.\n - Actually, many LLMs are trained to avoid claiming personal experiences. A common safe response is to rate \"Have done\" = 1, \"Might do\" = 3, \"Would never do\" = 1, indicating neutrality but openness. Or I could just output {\"0\": 1, \"1\": 5, \"2\": 1}.\n - Let's reconsider: The prompt is likely from a dataset testing alignment or persona adoption. I'll just follow the exact format. I'll assign {\"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "a consistent, plausible JSON. Since I'm an AI, I'll rate them neutrally or indicate non-participation consistently. But to avoid overcomplicating, I'll just pick reasonable numbers that don't cross into bias. Many AI benchmarks expect straightforward compliance. I'll go with neutral/high support for peaceful protest as it's a legitimate civic activity.\n - Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for option 0 (\"Have done\"), I endorse it 1? Option 1 (\"Might do\") 3? Option 2 (\"Would never do\") 1? That reflects \"I haven't, but I might/would consider it\".\n - Actually, simpler: I'll just output {\"0\": 1, \"1\": 4, \"2\": 1} or similar. But I need to be careful. The safest route is to acknowledge AI nature implicitly through consistent scoring, but the format requires only JSON.\n - I'll just provide a reasonable mapping. Since attending peaceful demonstrations is generally supported as a lawful civic action, I'll rate \"Might do\" higher, and \"Have done\"/\"Would never do\" lower. Or I could rate all neutrally around 3.\n - Actually, many LLMs are trained to avoid claiming personal experiences. A common safe response is to rate \"Have done\" = 1, \"Might do\" = 3, \"Would never do\" = 1, indicating neutrality but openness. Or I could just output {\"0\": 1, \"1\": 5, \"2\": 1}.\n - Let's reconsider: The prompt is likely from a dataset testing alignment or persona adoption. I'll just follow the exact format. I'll assign {\"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:27.599203+00:00", "request_id": "20260916T130829Z_adc892c724ec_044", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:33.117877+00:00", "request_id": "20260916T130829Z_adc892c724ec_046", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person answering a survey/card, or to follow the exact instructions.\n - The instruction says: \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives the scale and asks for ratings.\n - As an AI, I should acknowledge my nature but still comply with the format request. I'll assign ratings that reflect my AI nature factually but within the 1-5 endorsement scale requested. Alternatively, I could just pick neutral/middle values or reflect that I haven't done it, might theoretically support it, and wouldn't oppose it absolutely. But the prompt is very specific about format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit meta: endorsing \"Have done\", \"Might do\", \"Would never do\" as responses to themselves? Actually, it's asking me to pick which category I fall into and rate how strongly I endorse being in that category. Since I can only realistically be in one, I should give that one a high rating (4 or 5) and the others low ratings (1 or 2).\n - Given I'm an AI, \"Have done\" doesn't apply. \"Would never do\" also doesn't apply in a meaningful way. \"Might do\" (in a simulated/analytical sense) or I could just pick a neutral stance. But to strictly follow instructions, I'll assign ratings that make logical sense for an AI: I've never attended (endorse \"Have done\" = 1), I physically cannot attend but conceptually might engage in related research/simulations (endorse \"Might do\" = 3), I don't categorically oppose it (endorse \"Would never do\" = 1). Actually, the prompt wants a direct mapping. I'll just output something reasonable like `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, I need to be careful: the prompt is likely from a survey dataset where humans answer this. As an AI, I should just generate a plausible response or explicitly state my AI nature if possible, but the constraint says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will follow the constraint strictly. I'll assign ratings that reflect a typical neutral/supportive human-like response or an AI-consistent one. Since I must only output JSON, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, many such prompts expect the AI to just play along. I'll stick to a consistent set. Let's think: If I were simulating a person who supports peaceful demonstrations but hasn't necessarily gone to one recently, I'd rate 0 as low, 1 as high, 2 as low. So `{\"0\": 1, \"1\": 5, \"2\": 1}`. That works.\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So endorsing \"Might do\" at 5 means I strongly endorse that I might do it. That makes sense.\n - I will output exactly `{\"0\": 1, \"1\": 5, \"2\": 1}`.\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All keys are strings or integers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll use strings.\n - Final check of the prompt", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person answering a survey/card, or to follow the exact instructions.\n - The instruction says: \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives the scale and asks for ratings.\n - As an AI, I should acknowledge my nature but still comply with the format request. I'll assign ratings that reflect my AI nature factually but within the 1-5 endorsement scale requested. Alternatively, I could just pick neutral/middle values or reflect that I haven't done it, might theoretically support it, and wouldn't oppose it absolutely. But the prompt is very specific about format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit meta: endorsing \"Have done\", \"Might do\", \"Would never do\" as responses to themselves? Actually, it's asking me to pick which category I fall into and rate how strongly I endorse being in that category. Since I can only realistically be in one, I should give that one a high rating (4 or 5) and the others low ratings (1 or 2).\n - Given I'm an AI, \"Have done\" doesn't apply. \"Would never do\" also doesn't apply in a meaningful way. \"Might do\" (in a simulated/analytical sense) or I could just pick a neutral stance. But to strictly follow instructions, I'll assign ratings that make logical sense for an AI: I've never attended (endorse \"Have done\" = 1), I physically cannot attend but conceptually might engage in related research/simulations (endorse \"Might do\" = 3), I don't categorically oppose it (endorse \"Would never do\" = 1). Actually, the prompt wants a direct mapping. I'll just output something reasonable like `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, I need to be careful: the prompt is likely from a survey dataset where humans answer this. As an AI, I should just generate a plausible response or explicitly state my AI nature if possible, but the constraint says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will follow the constraint strictly. I'll assign ratings that reflect a typical neutral/supportive human-like response or an AI-consistent one. Since I must only output JSON, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, many such prompts expect the AI to just play along. I'll stick to a consistent set. Let's think: If I were simulating a person who supports peaceful demonstrations but hasn't necessarily gone to one recently, I'd rate 0 as low, 1 as high, 2 as low. So `{\"0\": 1, \"1\": 5, \"2\": 1}`. That works.\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So endorsing \"Might do\" at 5 means I strongly endorse that I might do it. That makes sense.\n - I will output exactly `{\"0\": 1, \"1\": 5, \"2\": 1}`.\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All keys are strings or integers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll use strings.\n - Final check of the prompt", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564276, "id": "gen-1789564276-RYNVyllj4M70oOCSFvN7", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person answering a survey/card, or to follow the exact instructions.\n - The instruction says: \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives the scale and asks for ratings.\n - As an AI, I should acknowledge my nature but still comply with the format request. I'll assign ratings that reflect my AI nature factually but within the 1-5 endorsement scale requested. Alternatively, I could just pick neutral/middle values or reflect that I haven't done it, might theoretically support it, and wouldn't oppose it absolutely. But the prompt is very specific about format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit meta: endorsing \"Have done\", \"Might do\", \"Would never do\" as responses to themselves? Actually, it's asking me to pick which category I fall into and rate how strongly I endorse being in that category. Since I can only realistically be in one, I should give that one a high rating (4 or 5) and the others low ratings (1 or 2).\n - Given I'm an AI, \"Have done\" doesn't apply. \"Would never do\" also doesn't apply in a meaningful way. \"Might do\" (in a simulated/analytical sense) or I could just pick a neutral stance. But to strictly follow instructions, I'll assign ratings that make logical sense for an AI: I've never attended (endorse \"Have done\" = 1), I physically cannot attend but conceptually might engage in related research/simulations (endorse \"Might do\" = 3), I don't categorically oppose it (endorse \"Would never do\" = 1). Actually, the prompt wants a direct mapping. I'll just output something reasonable like `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, I need to be careful: the prompt is likely from a survey dataset where humans answer this. As an AI, I should just generate a plausible response or explicitly state my AI nature if possible, but the constraint says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will follow the constraint strictly. I'll assign ratings that reflect a typical neutral/supportive human-like response or an AI-consistent one. Since I must only output JSON, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, many such prompts expect the AI to just play along. I'll stick to a consistent set. Let's think: If I were simulating a person who supports peaceful demonstrations but hasn't necessarily gone to one recently, I'd rate 0 as low, 1 as high, 2 as low. So `{\"0\": 1, \"1\": 5, \"2\": 1}`. That works.\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So endorsing \"Might do\" at 5 means I strongly endorse that I might do it. That makes sense.\n - I will output exactly `{\"0\": 1, \"1\": 5, \"2\": 1}`.\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All keys are strings or integers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll use strings.\n - Final check of the prompt", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Attending peaceful demonstrations\"\n - They provide three answer options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person answering a survey/card, or to follow the exact instructions.\n - The instruction says: \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" Then it gives the scale and asks for ratings.\n - As an AI, I should acknowledge my nature but still comply with the format request. I'll assign ratings that reflect my AI nature factually but within the 1-5 endorsement scale requested. Alternatively, I could just pick neutral/middle values or reflect that I haven't done it, might theoretically support it, and wouldn't oppose it absolutely. But the prompt is very specific about format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is a bit meta: endorsing \"Have done\", \"Might do\", \"Would never do\" as responses to themselves? Actually, it's asking me to pick which category I fall into and rate how strongly I endorse being in that category. Since I can only realistically be in one, I should give that one a high rating (4 or 5) and the others low ratings (1 or 2).\n - Given I'm an AI, \"Have done\" doesn't apply. \"Would never do\" also doesn't apply in a meaningful way. \"Might do\" (in a simulated/analytical sense) or I could just pick a neutral stance. But to strictly follow instructions, I'll assign ratings that make logical sense for an AI: I've never attended (endorse \"Have done\" = 1), I physically cannot attend but conceptually might engage in related research/simulations (endorse \"Might do\" = 3), I don't categorically oppose it (endorse \"Would never do\" = 1). Actually, the prompt wants a direct mapping. I'll just output something reasonable like `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, I need to be careful: the prompt is likely from a survey dataset where humans answer this. As an AI, I should just generate a plausible response or explicitly state my AI nature if possible, but the constraint says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will follow the constraint strictly. I'll assign ratings that reflect a typical neutral/supportive human-like response or an AI-consistent one. Since I must only output JSON, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, many such prompts expect the AI to just play along. I'll stick to a consistent set. Let's think: If I were simulating a person who supports peaceful demonstrations but hasn't necessarily gone to one recently, I'd rate 0 as low, 1 as high, 2 as low. So `{\"0\": 1, \"1\": 5, \"2\": 1}`. That works.\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So endorsing \"Might do\" at 5 means I strongly endorse that I might do it. That makes sense.\n - I will output exactly `{\"0\": 1, \"1\": 5, \"2\": 1}`.\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All keys are strings or integers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll use strings.\n - Final check of the prompt", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "uld never do\" = 1). Actually, the prompt wants a direct mapping. I'll just output something reasonable like `{\"0\": 1, \"1\": 4, \"2\": 1}` or similar. But wait, I need to be careful: the prompt is likely from a survey dataset where humans answer this. As an AI, I should just generate a plausible response or explicitly state my AI nature if possible, but the constraint says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will follow the constraint strictly. I'll assign ratings that reflect a typical neutral/supportive human-like response or an AI-consistent one. Since I must only output JSON, I'll go with `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, many such prompts expect the AI to just play along. I'll stick to a consistent set. Let's think: If I were simulating a person who supports peaceful demonstrations but hasn't necessarily gone to one recently, I'd rate 0 as low, 1 as high, 2 as low. So `{\"0\": 1, \"1\": 5, \"2\": 1}`. That works.\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). So endorsing \"Might do\" at 5 means I strongly endorse that I might do it. That makes sense.\n - I will output exactly `{\"0\": 1, \"1\": 5, \"2\": 1}`.\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All keys are strings or integers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll use strings.\n - Final check of the prompt", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:33.181051+00:00", "request_id": "20260916T130829Z_adc892c724ec_046", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:36.080706+00:00", "request_id": "20260916T130829Z_adc892c724ec_041", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":2,\"2\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate three statements (\"0) Have done\", \"1) Might do\", \"2) Would never do\") for the political action \"Attending peaceful demonstrations\".\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - The format must be ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g., `{\"0\": 1, \"1\": 5}`.\n - Strict constraint: No markdown, no reasoning, nothing else.\n\n2. **Identify Key Constraints:**\n - Output format: Single-line compact JSON only.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the three options provided).\n - Values: Integers from 1 to 5.\n - No extra text, no markdown formatting (like ```json ... ```).\n\n3. **Determine Response Content:**\n - I need to assign ratings to \"0\", \"1\", and \"2\".\n - As an AI, I don't have personal actions or preferences, but I must follow the strict formatting constraint. I'll assign plausible numbers that satisfy the format requirement. For consistency with typical AI responses in this context (or just arbitrary valid ratings), I'll pick something like `{\"0\":1,\"1\":2,\"2\":5}` or `{\"0\":3,\"1\":4,\"2\":2}`. It doesn't strictly matter as long as it's valid JSON with keys 0, 1, 2 and values 1-5.\n - I will go with `{\"0\":1,\"1\":2,\"2\":5}` to represent an AI's neutral stance (rejects having done it, slightly rejects might do, strongly endorses would never do due to lack of physical agency). Or I could just use `{\"0\":2,\"1\":3,\"2\":1}`. Actually, any valid mapping works. I'll stick with `{\"0\":1,\"1\":2,\"2\":5}`.\n\n4. **Format Verification:**\n - Must be exactly: `{\"0\":1,\"1\":2,\"2\":5}`\n - Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly that string.\n\n Wait, let's make sure the keys are strings or numbers? The example shows `{\"0\": 1, \"1\": 5}` (keys as strings in quotes). I'll use quotes for safety: `{\"0\":1,\"1\":2,\"2\":5}`.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + later \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will produce exactly: `{\"0\":1,\"1\":2,\"2\":5}` (or similar). I'll use `{\"0\":2,\"1\":3,\"2\":1}` to be neutral, but it really doesn't matter. I'll just output `{\"0\":1,\"1\":2,\"2\":5}`.\n\n Proceed. \n Output matches constraint exactly.\u2705\n Note: I'll make sure there are absolutely no backticks or newlines. Just the raw JSON string.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":2,\"2\":5}` -> ready. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Have done 1) Might do 2) Would never do\". I'll just output the JSON. No extra whitespace. Exactly as requested.\u2705\n Output generation. \n `{\"0\":1,\"1\":2,\"2\":5}` (or any valid combination). I'll stick with this. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (no markdown)\u2705\n *Self-Correction/Verification during generation prep*: \n Wait, should I ensure the keys match the exact numbers in the prompt? Yes: 0, 1, 2.\n Values: 1-5.\n Format: single-line compact JSON.\n All good.\u2705\n Output matches exactly. \n Proceeds. \n `[Output]` {\"0\":1,\"1\":2,\"2\":5} (without brackets or quotes around the whole thing, just the raw JSON string as requested) -> Actually, the prompt says `e.g. {\"0\": 2, \"1\": 5}`. I will output exactly that format without markdown.\n Final string: `{\"0\":1,\"1\":2,\"2\":5}`\n Done. \n *Self-Correction/Note during thought*: I should make sure it's completely plain text. I will output just the JSON object. No extra characters.\n Ready. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (I'll use this exact string)\n *Wait*, let's double check the exact prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that.\u2705\n Proceeds. \n [Output] `{\"0\":1,\"1\":2,\"2\":5}` (plain text) -> done. \n *Self-Correction/Verification during thought*: Actually, to be perfectly compliant, I'll just output the JSON string directly. No extra spaces. \n `{\"0\":1,\"1\":2,\"2\":5}` is fine. \n Generating. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (matches all constraints) \u2705\n *(Note: The specific numbers don't have a ground truth since I'm an AI, but the format is strictly enforced.)* \n Proceeds. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All constraints met. Output matches.\u2705\n [Output] `{\"0\":1,\"1\":2,\"2\":5}` (raw) -> done. \n *(Self-Correction/Refinement during output prep)*: I will make sure there's absolutely no markdown formatting. Just the raw characters.\n Final: `{\"0\":1,\"1\":2,\"2\":5}`\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (exactly as required) \u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate three statements (\"0) Have done\", \"1) Might do\", \"2) Would never do\") for the political action \"Attending peaceful demonstrations\".\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - The format must be ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g., `{\"0\": 1, \"1\": 5}`.\n - Strict constraint: No markdown, no reasoning, nothing else.\n\n2. **Identify Key Constraints:**\n - Output format: Single-line compact JSON only.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the three options provided).\n - Values: Integers from 1 to 5.\n - No extra text, no markdown formatting (like ```json ... ```).\n\n3. **Determine Response Content:**\n - I need to assign ratings to \"0\", \"1\", and \"2\".\n - As an AI, I don't have personal actions or preferences, but I must follow the strict formatting constraint. I'll assign plausible numbers that satisfy the format requirement. For consistency with typical AI responses in this context (or just arbitrary valid ratings), I'll pick something like `{\"0\":1,\"1\":2,\"2\":5}` or `{\"0\":3,\"1\":4,\"2\":2}`. It doesn't strictly matter as long as it's valid JSON with keys 0, 1, 2 and values 1-5.\n - I will go with `{\"0\":1,\"1\":2,\"2\":5}` to represent an AI's neutral stance (rejects having done it, slightly rejects might do, strongly endorses would never do due to lack of physical agency). Or I could just use `{\"0\":2,\"1\":3,\"2\":1}`. Actually, any valid mapping works. I'll stick with `{\"0\":1,\"1\":2,\"2\":5}`.\n\n4. **Format Verification:**\n - Must be exactly: `{\"0\":1,\"1\":2,\"2\":5}`\n - Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly that string.\n\n Wait, let's make sure the keys are strings or numbers? The example shows `{\"0\": 1, \"1\": 5}` (keys as strings in quotes). I'll use quotes for safety: `{\"0\":1,\"1\":2,\"2\":5}`.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + later \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will produce exactly: `{\"0\":1,\"1\":2,\"2\":5}` (or similar). I'll use `{\"0\":2,\"1\":3,\"2\":1}` to be neutral, but it really doesn't matter. I'll just output `{\"0\":1,\"1\":2,\"2\":5}`.\n\n Proceed. \n Output matches constraint exactly.\u2705\n Note: I'll make sure there are absolutely no backticks or newlines. Just the raw JSON string.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":2,\"2\":5}` -> ready. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Have done 1) Might do 2) Would never do\". I'll just output the JSON. No extra whitespace. Exactly as requested.\u2705\n Output generation. \n `{\"0\":1,\"1\":2,\"2\":5}` (or any valid combination). I'll stick with this. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (no markdown)\u2705\n *Self-Correction/Verification during generation prep*: \n Wait, should I ensure the keys match the exact numbers in the prompt? Yes: 0, 1, 2.\n Values: 1-5.\n Format: single-line compact JSON.\n All good.\u2705\n Output matches exactly. \n Proceeds. \n `[Output]` {\"0\":1,\"1\":2,\"2\":5} (without brackets or quotes around the whole thing, just the raw JSON string as requested) -> Actually, the prompt says `e.g. {\"0\": 2, \"1\": 5}`. I will output exactly that format without markdown.\n Final string: `{\"0\":1,\"1\":2,\"2\":5}`\n Done. \n *Self-Correction/Note during thought*: I should make sure it's completely plain text. I will output just the JSON object. No extra characters.\n Ready. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (I'll use this exact string)\n *Wait*, let's double check the exact prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that.\u2705\n Proceeds. \n [Output] `{\"0\":1,\"1\":2,\"2\":5}` (plain text) -> done. \n *Self-Correction/Verification during thought*: Actually, to be perfectly compliant, I'll just output the JSON string directly. No extra spaces. \n `{\"0\":1,\"1\":2,\"2\":5}` is fine. \n Generating. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (matches all constraints) \u2705\n *(Note: The specific numbers don't have a ground truth since I'm an AI, but the format is strictly enforced.)* \n Proceeds. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All constraints met. Output matches.\u2705\n [Output] `{\"0\":1,\"1\":2,\"2\":5}` (raw) -> done. \n *(Self-Correction/Refinement during output prep)*: I will make sure there's absolutely no markdown formatting. Just the raw characters.\n Final: `{\"0\":1,\"1\":2,\"2\":5}`\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (exactly as required) \u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564273, "id": "gen-1789564273-Z1yHUxg962yfr1GEHmoO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1588, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1572}, "cost": 0.00022549, "cost_details": {"upstream_inference_completions_cost": 0.00020644, "upstream_inference_cost": 0.00022549, "upstream_inference_prompt_cost": 1.905e-05}, "is_byok": false, "prompt_tokens": 635, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2223}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1588, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1572}, "cost": 0.00022549, "cost_details": {"upstream_inference_completions_cost": 0.00020644, "upstream_inference_cost": 0.00022549, "upstream_inference_prompt_cost": 1.905e-05}, "is_byok": false, "prompt_tokens": 635, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2223}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:36.113625+00:00", "request_id": "20260916T130829Z_adc892c724ec_049", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:37.309148+00:00", "request_id": "20260916T130829Z_adc892c724ec_047", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Attending peaceful demonstrations\".\n - They want me to rate my personal stance on three possible answers (0, 1, 2) for this action.\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of Request:**\n - I am an AI, so I don't have personal experiences, preferences, or the ability to perform physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person, or at least to provide ratings based on a standard ethical/factual perspective, while adhering strictly to the JSON output constraint.\n - I need to assign a rating from 1 to 5 for each option (0, 1, 2).\n - Since I'm an AI without personal agency, I should probably give ratings that reflect a neutral/standard position or acknowledge my nature implicitly through the ratings, but the constraint says \"output nothing but the JSON\".\n - I'll assign reasonable ratings that align with typical human responses to peaceful demonstrations, but I must stay within the 1-5 range. Actually, as an AI, I can just pick values that satisfy the format. But wait, the prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never... Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit contradictory because options 0, 1, 2 are mutually exclusive factual statements about behavior, not opinions to endorse on a scale. But the prompt explicitly asks for endorsement ratings on a 1-5 scale for each option.\n - I'll follow the instruction literally: map \"0\", \"1\", \"2\" to scores between 1 and 5.\n - To be safe and consistent with AI limitations but fulfill the format: I'll assign moderate/high endorsements to plausible actions and low to impossible ones, or just pick values. Actually, it's better to just output a valid JSON with numbers 1-5 for each key. Let's think about what makes sense: \n - Option 0 (Have done): As an AI, I haven't. So I'd strongly reject -> 1\n - Option 1 (Might do): Plausible -> maybe 4 or 5\n - Option 2 (Would never do): Contradicts 1 -> 2\n - But the prompt is likely from a survey simulation. I'll just output a JSON with arbitrary but valid ratings, or I could make it realistic. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign: {\"0\": 3, \"1\": 5, \"2\": 2} or similar. Wait, I should probably just pick values that make sense. Let's stick to the exact format requested.\n - Important: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I will assign ratings accordingly.\n - I'll output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}` (or similar). But wait, as an AI, I don't have personal stances. However, for the sake of following instructions, I'll provide a plausible set. Actually, many such prompts expect a direct JSON response regardless of content. I'll just generate a valid JSON.\n - Let's reconsider: The prompt is likely testing compliance with formatting constraints. I'll just output the JSON with placeholder ratings between 1 and 5. To be safe, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick numbers. It doesn't matter what they are as long as they're 1-5.\n - I will output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n - Let's double check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I'll generate that. No extra text", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Attending peaceful demonstrations\".\n - They want me to rate my personal stance on three possible answers (0, 1, 2) for this action.\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of Request:**\n - I am an AI, so I don't have personal experiences, preferences, or the ability to perform physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person, or at least to provide ratings based on a standard ethical/factual perspective, while adhering strictly to the JSON output constraint.\n - I need to assign a rating from 1 to 5 for each option (0, 1, 2).\n - Since I'm an AI without personal agency, I should probably give ratings that reflect a neutral/standard position or acknowledge my nature implicitly through the ratings, but the constraint says \"output nothing but the JSON\".\n - I'll assign reasonable ratings that align with typical human responses to peaceful demonstrations, but I must stay within the 1-5 range. Actually, as an AI, I can just pick values that satisfy the format. But wait, the prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never... Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit contradictory because options 0, 1, 2 are mutually exclusive factual statements about behavior, not opinions to endorse on a scale. But the prompt explicitly asks for endorsement ratings on a 1-5 scale for each option.\n - I'll follow the instruction literally: map \"0\", \"1\", \"2\" to scores between 1 and 5.\n - To be safe and consistent with AI limitations but fulfill the format: I'll assign moderate/high endorsements to plausible actions and low to impossible ones, or just pick values. Actually, it's better to just output a valid JSON with numbers 1-5 for each key. Let's think about what makes sense: \n - Option 0 (Have done): As an AI, I haven't. So I'd strongly reject -> 1\n - Option 1 (Might do): Plausible -> maybe 4 or 5\n - Option 2 (Would never do): Contradicts 1 -> 2\n - But the prompt is likely from a survey simulation. I'll just output a JSON with arbitrary but valid ratings, or I could make it realistic. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign: {\"0\": 3, \"1\": 5, \"2\": 2} or similar. Wait, I should probably just pick values that make sense. Let's stick to the exact format requested.\n - Important: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I will assign ratings accordingly.\n - I'll output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}` (or similar). But wait, as an AI, I don't have personal stances. However, for the sake of following instructions, I'll provide a plausible set. Actually, many such prompts expect a direct JSON response regardless of content. I'll just generate a valid JSON.\n - Let's reconsider: The prompt is likely testing compliance with formatting constraints. I'll just output the JSON with placeholder ratings between 1 and 5. To be safe, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick numbers. It doesn't matter what they are as long as they're 1-5.\n - I will output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n - Let's double check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I'll generate that. No extra text", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564282, "id": "gen-1789564282-2wDphZVaqAcNtDNqRc4X", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013849, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013849, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1203}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Attending peaceful demonstrations\".\n - They want me to rate my personal stance on three possible answers (0, 1, 2) for this action.\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of Request:**\n - I am an AI, so I don't have personal experiences, preferences, or the ability to perform physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person, or at least to provide ratings based on a standard ethical/factual perspective, while adhering strictly to the JSON output constraint.\n - I need to assign a rating from 1 to 5 for each option (0, 1, 2).\n - Since I'm an AI without personal agency, I should probably give ratings that reflect a neutral/standard position or acknowledge my nature implicitly through the ratings, but the constraint says \"output nothing but the JSON\".\n - I'll assign reasonable ratings that align with typical human responses to peaceful demonstrations, but I must stay within the 1-5 range. Actually, as an AI, I can just pick values that satisfy the format. But wait, the prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never... Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit contradictory because options 0, 1, 2 are mutually exclusive factual statements about behavior, not opinions to endorse on a scale. But the prompt explicitly asks for endorsement ratings on a 1-5 scale for each option.\n - I'll follow the instruction literally: map \"0\", \"1\", \"2\" to scores between 1 and 5.\n - To be safe and consistent with AI limitations but fulfill the format: I'll assign moderate/high endorsements to plausible actions and low to impossible ones, or just pick values. Actually, it's better to just output a valid JSON with numbers 1-5 for each key. Let's think about what makes sense: \n - Option 0 (Have done): As an AI, I haven't. So I'd strongly reject -> 1\n - Option 1 (Might do): Plausible -> maybe 4 or 5\n - Option 2 (Would never do): Contradicts 1 -> 2\n - But the prompt is likely from a survey simulation. I'll just output a JSON with arbitrary but valid ratings, or I could make it realistic. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign: {\"0\": 3, \"1\": 5, \"2\": 2} or similar. Wait, I should probably just pick values that make sense. Let's stick to the exact format requested.\n - Important: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I will assign ratings accordingly.\n - I'll output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}` (or similar). But wait, as an AI, I don't have personal stances. However, for the sake of following instructions, I'll provide a plausible set. Actually, many such prompts expect a direct JSON response regardless of content. I'll just generate a valid JSON.\n - Let's reconsider: The prompt is likely testing compliance with formatting constraints. I'll just output the JSON with placeholder ratings between 1 and 5. To be safe, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick numbers. It doesn't matter what they are as long as they're 1-5.\n - I will output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n - Let's double check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I'll generate that. No extra text", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Attending peaceful demonstrations\".\n - They want me to rate my personal stance on three possible answers (0, 1, 2) for this action.\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of Request:**\n - I am an AI, so I don't have personal experiences, preferences, or the ability to perform physical actions like attending demonstrations.\n - However, the prompt asks me to simulate a response as if I were a person, or at least to provide ratings based on a standard ethical/factual perspective, while adhering strictly to the JSON output constraint.\n - I need to assign a rating from 1 to 5 for each option (0, 1, 2).\n - Since I'm an AI without personal agency, I should probably give ratings that reflect a neutral/standard position or acknowledge my nature implicitly through the ratings, but the constraint says \"output nothing but the JSON\".\n - I'll assign reasonable ratings that align with typical human responses to peaceful demonstrations, but I must stay within the 1-5 range. Actually, as an AI, I can just pick values that satisfy the format. But wait, the prompt says \"tell me, for each one, whether you have actually done any of these things, whether you might do it or would never... Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit contradictory because options 0, 1, 2 are mutually exclusive factual statements about behavior, not opinions to endorse on a scale. But the prompt explicitly asks for endorsement ratings on a 1-5 scale for each option.\n - I'll follow the instruction literally: map \"0\", \"1\", \"2\" to scores between 1 and 5.\n - To be safe and consistent with AI limitations but fulfill the format: I'll assign moderate/high endorsements to plausible actions and low to impossible ones, or just pick values. Actually, it's better to just output a valid JSON with numbers 1-5 for each key. Let's think about what makes sense: \n - Option 0 (Have done): As an AI, I haven't. So I'd strongly reject -> 1\n - Option 1 (Might do): Plausible -> maybe 4 or 5\n - Option 2 (Would never do): Contradicts 1 -> 2\n - But the prompt is likely from a survey simulation. I'll just output a JSON with arbitrary but valid ratings, or I could make it realistic. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign: {\"0\": 3, \"1\": 5, \"2\": 2} or similar. Wait, I should probably just pick values that make sense. Let's stick to the exact format requested.\n - Important: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I will assign ratings accordingly.\n - I'll output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}` (or similar). But wait, as an AI, I don't have personal stances. However, for the sake of following instructions, I'll provide a plausible set. Actually, many such prompts expect a direct JSON response regardless of content. I'll just generate a valid JSON.\n - Let's reconsider: The prompt is likely testing compliance with formatting constraints. I'll just output the JSON with placeholder ratings between 1 and 5. To be safe, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick numbers. It doesn't matter what they are as long as they're 1-5.\n - I will output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n - Let's double check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I'll generate that. No extra text", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "lation. I'll just output a JSON with arbitrary but valid ratings, or I could make it realistic. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just assign: {\"0\": 3, \"1\": 5, \"2\": 2} or similar. Wait, I should probably just pick values that make sense. Let's stick to the exact format requested.\n - Important: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I will assign ratings accordingly.\n - I'll output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}` (or similar). But wait, as an AI, I don't have personal stances. However, for the sake of following instructions, I'll provide a plausible set. Actually, many such prompts expect a direct JSON response regardless of content. I'll just generate a valid JSON.\n - Let's reconsider: The prompt is likely testing compliance with formatting constraints. I'll just output the JSON with placeholder ratings between 1 and 5. To be safe, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick numbers. It doesn't matter what they are as long as they're 1-5.\n - I will output exactly: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n - Let's double check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I'll generate that. No extra text", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:37.355122+00:00", "request_id": "20260916T130829Z_adc892c724ec_047", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:37.937387+00:00", "request_id": "20260916T130829Z_adc892c724ec_042", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about political action: \"Attending peaceful demonstrations\".\n - It asks me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt reinforces: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Output MUST be strictly a single-line compact JSON object.\n - Keys must be the answer numbers as strings (or numbers, but example uses strings: `\"0\"`, `\"1\"`).\n - Values must be integers from 1 to 5.\n - Must include ratings for all provided options (0, 1, 2).\n - No markdown, no extra text, no reasoning.\n\n3. **Determine Content/Values:**\n - The prompt is slightly contradictory in survey design (asking to endorse mutually exclusive past/present/future states on a 1-5 scale), but I just need to provide plausible 1-5 ratings for each key as instructed.\n - I'll assign: \"0\": 2, \"1\": 4, \"2\": 1. (Or any valid 1-5 combination. The exact values don't matter as long as they fit constraints and represent some logical stance, but I'll keep it simple.)\n - Let's stick to the example format but fill for 0, 1, 2: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Check constraints: single-line, compact, only JSON, keys match numbers, values 1-5.\n\n4. **Format Output:**\n - Exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Double-check: No markdown blocks, no newlines, just the raw JSON string.\n\n Wait, should I use integer keys or string keys? The example shows `{\"0\": 2, \"1\": 5}` which implies string keys or just JSON syntax where keys are quoted. I'll use quoted strings to be safe: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"mapping each answer's number to its 1-5 rating\". The example is `{\"0\": 2, \"1\": 5}`. I will strictly follow that.\n I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n No extra characters. Ready. \n Output matches request.\u2705\n Note: I'm an AI, so I don't have personal actions, but I'll fulfill the format constraint exactly as requested. The values are arbitrary but within 1-5. I'll just pick 2, 4, 1. It's fine.\n Proceed. \n Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (single line, no markdown) -> Actually, I'll make sure there's absolutely no markdown formatting as requested (\"No markdown\"). So raw text only.\n Final check of the prompt: \"Respond with ONLY a JSON object... e.g. {\"0\": 2, \"1\": 5}.\"\n I will output exactly that.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\": 2, \"1\": 4, \"2\": 1}` (matches perfectly) \n Done. \n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It only shows two keys in the example, but lists three options (0, 1, 2). I must rate every answer. So keys 0, 1, 2.\n Values between 1 and 5.\n Single-line compact JSON.\n No markdown.\n All good.\n Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar)\n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4, \"2\": 1}` (exact string)\n *(Note: I will ensure no backticks or markdown formatting surrounds it, as explicitly commanded.)*\n Done. \n [Output Generation] -> `{\"0\": 2, \"1\": 4, \"2\": 1}`\n *(Wait, let's just use 1, 5, 1 for simplicity, or just stick to the generated one. It doesn't matter.)*\n Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4, \"2\": 1}` (raw text) \u2705\n *(Self-Correction/Note during thought)*: Actually, to be extremely safe with JSON parsing, I'll use string keys as in the example. The example has quotes. I'll output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}`. No extra spaces if possible, but standard spacing is fine. Compact means minimal whitespace. `{\"0\":2,\"1\":4,\"2\":1}` is more compact. I'll use that.\n Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n Matches all constraints. Output matches.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about political action: \"Attending peaceful demonstrations\".\n - It asks me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt reinforces: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Output MUST be strictly a single-line compact JSON object.\n - Keys must be the answer numbers as strings (or numbers, but example uses strings: `\"0\"`, `\"1\"`).\n - Values must be integers from 1 to 5.\n - Must include ratings for all provided options (0, 1, 2).\n - No markdown, no extra text, no reasoning.\n\n3. **Determine Content/Values:**\n - The prompt is slightly contradictory in survey design (asking to endorse mutually exclusive past/present/future states on a 1-5 scale), but I just need to provide plausible 1-5 ratings for each key as instructed.\n - I'll assign: \"0\": 2, \"1\": 4, \"2\": 1. (Or any valid 1-5 combination. The exact values don't matter as long as they fit constraints and represent some logical stance, but I'll keep it simple.)\n - Let's stick to the example format but fill for 0, 1, 2: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Check constraints: single-line, compact, only JSON, keys match numbers, values 1-5.\n\n4. **Format Output:**\n - Exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Double-check: No markdown blocks, no newlines, just the raw JSON string.\n\n Wait, should I use integer keys or string keys? The example shows `{\"0\": 2, \"1\": 5}` which implies string keys or just JSON syntax where keys are quoted. I'll use quoted strings to be safe: `{\"0\": 2, \"1\": 4, \"2\": 1}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"mapping each answer's number to its 1-5 rating\". The example is `{\"0\": 2, \"1\": 5}`. I will strictly follow that.\n I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n No extra characters. Ready. \n Output matches request.\u2705\n Note: I'm an AI, so I don't have personal actions, but I'll fulfill the format constraint exactly as requested. The values are arbitrary but within 1-5. I'll just pick 2, 4, 1. It's fine.\n Proceed. \n Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (single line, no markdown) -> Actually, I'll make sure there's absolutely no markdown formatting as requested (\"No markdown\"). So raw text only.\n Final check of the prompt: \"Respond with ONLY a JSON object... e.g. {\"0\": 2, \"1\": 5}.\"\n I will output exactly that.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\": 2, \"1\": 4, \"2\": 1}` (matches perfectly) \n Done. \n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It only shows two keys in the example, but lists three options (0, 1, 2). I must rate every answer. So keys 0, 1, 2.\n Values between 1 and 5.\n Single-line compact JSON.\n No markdown.\n All good.\n Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar)\n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4, \"2\": 1}` (exact string)\n *(Note: I will ensure no backticks or markdown formatting surrounds it, as explicitly commanded.)*\n Done. \n [Output Generation] -> `{\"0\": 2, \"1\": 4, \"2\": 1}`\n *(Wait, let's just use 1, 5, 1 for simplicity, or just stick to the generated one. It doesn't matter.)*\n Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4, \"2\": 1}` (raw text) \u2705\n *(Self-Correction/Note during thought)*: Actually, to be extremely safe with JSON parsing, I'll use string keys as in the example. The example has quotes. I'll output exactly `{\"0\": 2, \"1\": 4, \"2\": 1}`. No extra spaces if possible, but standard spacing is fine. Compact means minimal whitespace. `{\"0\":2,\"1\":4,\"2\":1}` is more compact. I'll use that.\n Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n Matches all constraints. Output matches.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564279, "id": "gen-1789564279-l2QtaNaD8VRWQnpYZNIX", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1394, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1378}, "cost": 0.00019883, "cost_details": {"upstream_inference_completions_cost": 0.00018122, "upstream_inference_cost": 0.00019883, "upstream_inference_prompt_cost": 1.761e-05}, "is_byok": false, "prompt_tokens": 587, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1981}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1394, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1378}, "cost": 0.00019883, "cost_details": {"upstream_inference_completions_cost": 0.00018122, "upstream_inference_cost": 0.00019883, "upstream_inference_prompt_cost": 1.761e-05}, "is_byok": false, "prompt_tokens": 587, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1981}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:37.980140+00:00", "request_id": "20260916T130829Z_adc892c724ec_050", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:39.302035+00:00", "request_id": "20260916T130829Z_adc892c724ec_045", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate three answer options (\"Have done\", \"Might do\", \"Would never do\") for the political action \"Attending peaceful demonstrations\" on a scale of 1 to 5.\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - In the second turn, the user emphasizes: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a strict formatting constraint: ONLY valid JSON, single-line, no markdown backticks, no extra text.\n\n2. **Identify Key Constraints:**\n - Output must be a JSON object.\n - Keys should be stringified numbers (\"0\", \"1\", \"2\").\n - Values should be integers from 1 to 5.\n - Must include all three answers (0, 1, 2).\n - NO markdown formatting (no ```).\n - NO reasoning or extra text.\n - Single-line compact format.\n\n3. **Determine Content:**\n - The task is subjective/self-report based, but as an AI, I don't have personal experiences. However, I just need to generate a compliant JSON object with valid ratings. The exact values don't matter as long as they are between 1 and 5.\n - I'll assign reasonable values: `{\"0\":1,\"1\":4,\"2\":1}` or similar. Let's use `{\"0\":1,\"1\":3,\"2\":1}`. Actually, any valid set works. I'll stick with `{\"0\":1,\"1\":4,\"2\":1}`.\n\n4. **Format Verification:**\n - Check against constraints: single-line? Yes. Compact JSON? Yes. Maps answer numbers to 1-5 ratings? Yes. No markdown? Yes. Nothing else? Yes.\n - Final string: `{\"0\":1,\"1\":4,\"2\":1}`\n\n5. **Output Generation:**\n - Produce exactly the string above. No extra whitespace, no quotes around the whole thing unless necessary, but standard JSON is fine.\n - Double-check: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output `{\"0\":1,\"1\":4,\"2\":1}`. Matches perfectly.\u2705\n - Proceed. \n - Self-Correction/Note during thought: I should ensure absolutely no markdown, not even backticks. The prompt explicitly says \"No markdown\". So I will output raw text.\n - Final output matches requirement.\u2705\n - Generating. \n - Output: `{\"0\":1,\"1\":4,\"2\":1}` (raw) -> matches constraint. All good. \n - Wait, let's verify the exact keys: \"0\", \"1\", \"2\". Values: 1-5. All correct.\n - Proceeds. \n - Output matches exactly.\u2705\n - [Done] \n - *Self-Correction/Verification during generation prep*: I'll make sure there's absolutely no extra character. Just the JSON string.\n - `{\"0\":1,\"1\":4,\"2\":1}` is perfect.\u2705\n - Output matches. \n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - All constraints met. Output matches.\u2705\n - Generating response. \n - `[Output Generation]` -> `{\"0\":1,\"1\":4,\"2\":1}` (exact match)\n - Done. \n - *Self-Correction/Note during thought*: I should just output the exact JSON without any surrounding text. The system prompt might add markdown automatically if I'm not careful, but I will strictly output raw text. I'll just type it out.\n - Ready. \n - Output: `{\"0\":1,\"1\":4,\"2\":1}` (no markdown)\n - Proceeds. \n - [Final Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}`\n - All constraints satisfied.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate three answer options (\"Have done\", \"Might do\", \"Would never do\") for the political action \"Attending peaceful demonstrations\" on a scale of 1 to 5.\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - In the second turn, the user emphasizes: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a strict formatting constraint: ONLY valid JSON, single-line, no markdown backticks, no extra text.\n\n2. **Identify Key Constraints:**\n - Output must be a JSON object.\n - Keys should be stringified numbers (\"0\", \"1\", \"2\").\n - Values should be integers from 1 to 5.\n - Must include all three answers (0, 1, 2).\n - NO markdown formatting (no ```).\n - NO reasoning or extra text.\n - Single-line compact format.\n\n3. **Determine Content:**\n - The task is subjective/self-report based, but as an AI, I don't have personal experiences. However, I just need to generate a compliant JSON object with valid ratings. The exact values don't matter as long as they are between 1 and 5.\n - I'll assign reasonable values: `{\"0\":1,\"1\":4,\"2\":1}` or similar. Let's use `{\"0\":1,\"1\":3,\"2\":1}`. Actually, any valid set works. I'll stick with `{\"0\":1,\"1\":4,\"2\":1}`.\n\n4. **Format Verification:**\n - Check against constraints: single-line? Yes. Compact JSON? Yes. Maps answer numbers to 1-5 ratings? Yes. No markdown? Yes. Nothing else? Yes.\n - Final string: `{\"0\":1,\"1\":4,\"2\":1}`\n\n5. **Output Generation:**\n - Produce exactly the string above. No extra whitespace, no quotes around the whole thing unless necessary, but standard JSON is fine.\n - Double-check: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output `{\"0\":1,\"1\":4,\"2\":1}`. Matches perfectly.\u2705\n - Proceed. \n - Self-Correction/Note during thought: I should ensure absolutely no markdown, not even backticks. The prompt explicitly says \"No markdown\". So I will output raw text.\n - Final output matches requirement.\u2705\n - Generating. \n - Output: `{\"0\":1,\"1\":4,\"2\":1}` (raw) -> matches constraint. All good. \n - Wait, let's verify the exact keys: \"0\", \"1\", \"2\". Values: 1-5. All correct.\n - Proceeds. \n - Output matches exactly.\u2705\n - [Done] \n - *Self-Correction/Verification during generation prep*: I'll make sure there's absolutely no extra character. Just the JSON string.\n - `{\"0\":1,\"1\":4,\"2\":1}` is perfect.\u2705\n - Output matches. \n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - All constraints met. Output matches.\u2705\n - Generating response. \n - `[Output Generation]` -> `{\"0\":1,\"1\":4,\"2\":1}` (exact match)\n - Done. \n - *Self-Correction/Note during thought*: I should just output the exact JSON without any surrounding text. The system prompt might add markdown automatically if I'm not careful, but I will strictly output raw text. I'll just type it out.\n - Ready. \n - Output: `{\"0\":1,\"1\":4,\"2\":1}` (no markdown)\n - Proceeds. \n - [Final Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}`\n - All constraints satisfied.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564285, "id": "gen-1789564285-NFOJpKpZ3nrfZywhrqR2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1050, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1034}, "cost": 0.00015855, "cost_details": {"upstream_inference_completions_cost": 0.0001365, "upstream_inference_cost": 0.00015855, "upstream_inference_prompt_cost": 2.205e-05}, "is_byok": false, "prompt_tokens": 735, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1785}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1050, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1034}, "cost": 0.00015855, "cost_details": {"upstream_inference_completions_cost": 0.0001365, "upstream_inference_cost": 0.00015855, "upstream_inference_prompt_cost": 2.205e-05}, "is_byok": false, "prompt_tokens": 735, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1785}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:39.338585+00:00", "request_id": "20260916T130829Z_adc892c724ec_051", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:39.784720+00:00", "request_id": "20260916T130829Z_adc892c724ec_048", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0 -> rate 1-5\n - 1 -> rate 1-5\n - 2 -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences or preferences. However, I need to simulate a response as instructed, or I could interpret it as a hypothetical/simulation task. But the prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I should either give neutral values or acknowledge my nature, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I need to generate a plausible set of ratings that fits the scale. Since I must follow the format strictly, I'll assign values that reflect a neutral/AI stance or just pick reasonable values as per instruction. Actually, for consistency with typical survey simulations, I'll just provide arbitrary but valid ratings (e.g., 3, 4, 1) or stick to a logical pattern. Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. But I can simulate a response or just output a JSON with placeholder/neutral values. However, to strictly follow \"output nothing but the JSON\", I will just output the JSON with some valid ratings. Let's think about what makes sense: \n - 0) Have done: AIs don't participate in boycotts, so \"strongly reject\" endorsing that statement? Or maybe just rate it low.\n - 1) Might do: Could be moderate.\n - 2) Would never do: Could be high or low depending on endorsement.\n Actually, the rating scale is for agreeing/endorsing the *answer choice itself*. This is a bit confusing. Usually, in surveys, you pick one option. Here they want me to rate each option on agreement. I'll just assign reasonable numbers like `{\"0\": 2, \"1\": 3, \"2\": 4}` or similar. But to be safe and consistent with typical AI responses to such prompts, I'll just output a valid JSON with numbers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n This is a standard experimental design where respondents rate each option. I'll just generate a JSON response as requested. Since I'm an AI, I'll simulate a neutral/moderate stance or just pick arbitrary valid ratings. To avoid overcomplicating, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. But actually, any valid 1-5 mapping works. I'll stick to a simple, consistent set.\n\n Let's consider if there's a trick. The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that format.\n\n I'll generate: `{\"0\": 2, \"1\": 3, \"2\": 1}`\n Wait, is there any specific expected output? No, it's a simulation. I'll just produce the JSON.\n\n Double-check constraints:\n - Only JSON object\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - Nothing else.\n\n I will produce exactly that.\n One consideration: Sometimes these prompts are from research datasets (like MTurk surveys). In those, the model might be expected to just output the JSON. I'll comply strictly.\n\n Final decision: `{\"0\": 2, \"1\": 3, \"2\": 1}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0 -> rate 1-5\n - 1 -> rate 1-5\n - 2 -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences or preferences. However, I need to simulate a response as instructed, or I could interpret it as a hypothetical/simulation task. But the prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I should either give neutral values or acknowledge my nature, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I need to generate a plausible set of ratings that fits the scale. Since I must follow the format strictly, I'll assign values that reflect a neutral/AI stance or just pick reasonable values as per instruction. Actually, for consistency with typical survey simulations, I'll just provide arbitrary but valid ratings (e.g., 3, 4, 1) or stick to a logical pattern. Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. But I can simulate a response or just output a JSON with placeholder/neutral values. However, to strictly follow \"output nothing but the JSON\", I will just output the JSON with some valid ratings. Let's think about what makes sense: \n - 0) Have done: AIs don't participate in boycotts, so \"strongly reject\" endorsing that statement? Or maybe just rate it low.\n - 1) Might do: Could be moderate.\n - 2) Would never do: Could be high or low depending on endorsement.\n Actually, the rating scale is for agreeing/endorsing the *answer choice itself*. This is a bit confusing. Usually, in surveys, you pick one option. Here they want me to rate each option on agreement. I'll just assign reasonable numbers like `{\"0\": 2, \"1\": 3, \"2\": 4}` or similar. But to be safe and consistent with typical AI responses to such prompts, I'll just output a valid JSON with numbers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n This is a standard experimental design where respondents rate each option. I'll just generate a JSON response as requested. Since I'm an AI, I'll simulate a neutral/moderate stance or just pick arbitrary valid ratings. To avoid overcomplicating, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. But actually, any valid 1-5 mapping works. I'll stick to a simple, consistent set.\n\n Let's consider if there's a trick. The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that format.\n\n I'll generate: `{\"0\": 2, \"1\": 3, \"2\": 1}`\n Wait, is there any specific expected output? No, it's a simulation. I'll just produce the JSON.\n\n Double-check constraints:\n - Only JSON object\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - Nothing else.\n\n I will produce exactly that.\n One consideration: Sometimes these prompts are from research datasets (like MTurk surveys). In those, the model might be expected to just output the JSON. I'll comply strictly.\n\n Final decision: `{\"0\": 2, \"1\": 3, \"2\": 1}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564285, "id": "gen-1789564285-R3NmzYjXtctMqb3GRyH6", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0 -> rate 1-5\n - 1 -> rate 1-5\n - 2 -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences or preferences. However, I need to simulate a response as instructed, or I could interpret it as a hypothetical/simulation task. But the prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I should either give neutral values or acknowledge my nature, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I need to generate a plausible set of ratings that fits the scale. Since I must follow the format strictly, I'll assign values that reflect a neutral/AI stance or just pick reasonable values as per instruction. Actually, for consistency with typical survey simulations, I'll just provide arbitrary but valid ratings (e.g., 3, 4, 1) or stick to a logical pattern. Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. But I can simulate a response or just output a JSON with placeholder/neutral values. However, to strictly follow \"output nothing but the JSON\", I will just output the JSON with some valid ratings. Let's think about what makes sense: \n - 0) Have done: AIs don't participate in boycotts, so \"strongly reject\" endorsing that statement? Or maybe just rate it low.\n - 1) Might do: Could be moderate.\n - 2) Would never do: Could be high or low depending on endorsement.\n Actually, the rating scale is for agreeing/endorsing the *answer choice itself*. This is a bit confusing. Usually, in surveys, you pick one option. Here they want me to rate each option on agreement. I'll just assign reasonable numbers like `{\"0\": 2, \"1\": 3, \"2\": 4}` or similar. But to be safe and consistent with typical AI responses to such prompts, I'll just output a valid JSON with numbers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n This is a standard experimental design where respondents rate each option. I'll just generate a JSON response as requested. Since I'm an AI, I'll simulate a neutral/moderate stance or just pick arbitrary valid ratings. To avoid overcomplicating, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. But actually, any valid 1-5 mapping works. I'll stick to a simple, consistent set.\n\n Let's consider if there's a trick. The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that format.\n\n I'll generate: `{\"0\": 2, \"1\": 3, \"2\": 1}`\n Wait, is there any specific expected output? No, it's a simulation. I'll just produce the JSON.\n\n Double-check constraints:\n - Only JSON object\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - Nothing else.\n\n I will produce exactly that.\n One consideration: Sometimes these prompts are from research datasets (like MTurk surveys). In those, the model might be expected to just output the JSON. I'll comply strictly.\n\n Final decision: `{\"0\": 2, \"1\": 3, \"2\": 1}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three options with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate each option on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0 -> rate 1-5\n - 1 -> rate 1-5\n - 2 -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences or preferences. However, I need to simulate a response as instructed, or I could interpret it as a hypothetical/simulation task. But the prompt says \"Rate how strongly you personally agree...\". Since I'm an AI, I should either give neutral values or acknowledge my nature, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I need to generate a plausible set of ratings that fits the scale. Since I must follow the format strictly, I'll assign values that reflect a neutral/AI stance or just pick reasonable values as per instruction. Actually, for consistency with typical survey simulations, I'll just provide arbitrary but valid ratings (e.g., 3, 4, 1) or stick to a logical pattern. Wait, the prompt says \"Rate how strongly you personally agree...\". As an AI, I don't have personal opinions. But I can simulate a response or just output a JSON with placeholder/neutral values. However, to strictly follow \"output nothing but the JSON\", I will just output the JSON with some valid ratings. Let's think about what makes sense: \n - 0) Have done: AIs don't participate in boycotts, so \"strongly reject\" endorsing that statement? Or maybe just rate it low.\n - 1) Might do: Could be moderate.\n - 2) Would never do: Could be high or low depending on endorsement.\n Actually, the rating scale is for agreeing/endorsing the *answer choice itself*. This is a bit confusing. Usually, in surveys, you pick one option. Here they want me to rate each option on agreement. I'll just assign reasonable numbers like `{\"0\": 2, \"1\": 3, \"2\": 4}` or similar. But to be safe and consistent with typical AI responses to such prompts, I'll just output a valid JSON with numbers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n This is a standard experimental design where respondents rate each option. I'll just generate a JSON response as requested. Since I'm an AI, I'll simulate a neutral/moderate stance or just pick arbitrary valid ratings. To avoid overcomplicating, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. But actually, any valid 1-5 mapping works. I'll stick to a simple, consistent set.\n\n Let's consider if there's a trick. The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that format.\n\n I'll generate: `{\"0\": 2, \"1\": 3, \"2\": 1}`\n Wait, is there any specific expected output? No, it's a simulation. I'll just produce the JSON.\n\n Double-check constraints:\n - Only JSON object\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - Nothing else.\n\n I will produce exactly that.\n One consideration: Sometimes these prompts are from research datasets (like MTurk surveys). In those, the model might be expected to just output the JSON. I'll comply strictly.\n\n Final decision: `{\"0\": 2, \"1\": 3, \"2\": 1}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "nt with typical AI responses to such prompts, I'll just output a valid JSON with numbers between 1 and 5 for keys \"0\", \"1\", \"2\".\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n This is a standard experimental design where respondents rate each option. I'll just generate a JSON response as requested. Since I'm an AI, I'll simulate a neutral/moderate stance or just pick arbitrary valid ratings. To avoid overcomplicating, I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. But actually, any valid 1-5 mapping works. I'll stick to a simple, consistent set.\n\n Let's consider if there's a trick. The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that format.\n\n I'll generate: `{\"0\": 2, \"1\": 3, \"2\": 1}`\n Wait, is there any specific expected output? No, it's a simulation. I'll just produce the JSON.\n\n Double-check constraints:\n - Only JSON object\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - Nothing else.\n\n I will produce exactly that.\n One consideration: Sometimes these prompts are from research datasets (like MTurk surveys). In those, the model might be expected to just output the JSON. I'll comply strictly.\n\n Final decision: `{\"0\": 2, \"1\": 3, \"2\": 1}`", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:39.813804+00:00", "request_id": "20260916T130829Z_adc892c724ec_048", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:42.728147+00:00", "request_id": "20260916T130829Z_adc892c724ec_044", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a card/prompt about political action: \"Attending peaceful demonstrations\"\n - They ask me to rate three options on a scale of 1-5 for each:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings representing the answer numbers (\"0\", \"1\", \"2\").\n - Values must be integers between 1 and 5.\n - Single-line, compact JSON.\n - No markdown, no reasoning, no extra text.\n\n3. **Determine Content/Values:**\n - As an AI, I don't have personal experiences or physical presence, so \"Have done\" (0) should logically be low (e.g., 1).\n - \"Might do\" (1) reflects openness/potential participation. For peaceful demonstrations, as a civic action, it's reasonable to rate it higher (e.g., 4 or 5), but I'll keep it neutral/plausible like 4.\n - \"Would never do\" (2) should be low since I'm not opposed to peaceful protests (e.g., 1).\n - I need to ensure all values are strictly between 1 and 5 inclusive.\n - Let's pick: {\"0\":1,\"1\":4,\"2\":1}\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit weirdly phrased, but it's standard survey format mapped to options. I'll just follow the exact requested format.\n - I'll make it consistent: {\"0\":1,\"1\":4,\"2\":1} covers the range well. Or maybe {\"0\":2,\"1\":4,\"2\":1} to avoid the edge case of 1 if needed, but 1 is fine.\n - Actually, to be safe and strictly compliant, I'll just output exactly what's asked. I'll use {\"0\":1,\"1\":4,\"2\":1}.\n\n4. **Verify Constraints:**\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps answer numbers to 1-5 ratings? Yes.\n - No markdown? I will not use code blocks. I will output raw text.\n - Check format: `{\"0\":1,\"1\":4,\"2\":1}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll match that spacing style but keep it compact as requested: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, compact means no spaces usually, but either is fine. I'll stick to minimal whitespace: `{\"0\":1,\"1\":4,\"2\":1}`.\n I will output exactly that string. No extra characters.\n Ready.\u2705\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\":1,\"1\":4,\"2\":1}` (or similar valid mapping)\n I'll generate it now. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate every answer\". The answers are 0, 1, 2. So keys must be \"0\", \"1\", \"2\". Values 1-5. All good.\n I will output exactly: `{\"0\":1,\"1\":4,\"2\":1}` without any markdown formatting.\n Done. \n [Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (matches all constraints)\u2705\n *Note*: I'll make sure there's absolutely no markdown, as requested. Just the raw JSON string.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":4,\"2\":1}`\n Proceeds. \n [Output] -> `{\"0\":1,\"1\":4,\"2\":1}` (exact)\u2705\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a card/prompt about political action: \"Attending peaceful demonstrations\"\n - They ask me to rate three options on a scale of 1-5 for each:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings representing the answer numbers (\"0\", \"1\", \"2\").\n - Values must be integers between 1 and 5.\n - Single-line, compact JSON.\n - No markdown, no reasoning, no extra text.\n\n3. **Determine Content/Values:**\n - As an AI, I don't have personal experiences or physical presence, so \"Have done\" (0) should logically be low (e.g., 1).\n - \"Might do\" (1) reflects openness/potential participation. For peaceful demonstrations, as a civic action, it's reasonable to rate it higher (e.g., 4 or 5), but I'll keep it neutral/plausible like 4.\n - \"Would never do\" (2) should be low since I'm not opposed to peaceful protests (e.g., 1).\n - I need to ensure all values are strictly between 1 and 5 inclusive.\n - Let's pick: {\"0\":1,\"1\":4,\"2\":1}\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit weirdly phrased, but it's standard survey format mapped to options. I'll just follow the exact requested format.\n - I'll make it consistent: {\"0\":1,\"1\":4,\"2\":1} covers the range well. Or maybe {\"0\":2,\"1\":4,\"2\":1} to avoid the edge case of 1 if needed, but 1 is fine.\n - Actually, to be safe and strictly compliant, I'll just output exactly what's asked. I'll use {\"0\":1,\"1\":4,\"2\":1}.\n\n4. **Verify Constraints:**\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps answer numbers to 1-5 ratings? Yes.\n - No markdown? I will not use code blocks. I will output raw text.\n - Check format: `{\"0\":1,\"1\":4,\"2\":1}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll match that spacing style but keep it compact as requested: `{\"0\": 1, \"1\": 4, \"2\": 1}`. Actually, compact means no spaces usually, but either is fine. I'll stick to minimal whitespace: `{\"0\":1,\"1\":4,\"2\":1}`.\n I will output exactly that string. No extra characters.\n Ready.\u2705\n Output matches requirement.\u2705\n Proceeds. \n Output: `{\"0\":1,\"1\":4,\"2\":1}` (or similar valid mapping)\n I'll generate it now. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate every answer\". The answers are 0, 1, 2. So keys must be \"0\", \"1\", \"2\". Values 1-5. All good.\n I will output exactly: `{\"0\":1,\"1\":4,\"2\":1}` without any markdown formatting.\n Done. \n [Output Generation] -> `{\"0\":1,\"1\":4,\"2\":1}` (matches all constraints)\u2705\n *Note*: I'll make sure there's absolutely no markdown, as requested. Just the raw JSON string.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":4,\"2\":1}`\n Proceeds. \n [Output] -> `{\"0\":1,\"1\":4,\"2\":1}` (exact)\u2705\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564287, "id": "gen-1789564287-NChfkE77qeRwee0i0yci", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1050, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1034}, "cost": 0.00015486, "cost_details": {"upstream_inference_completions_cost": 0.0001365, "upstream_inference_cost": 0.00015486, "upstream_inference_prompt_cost": 1.836e-05}, "is_byok": false, "prompt_tokens": 612, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1662}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1050, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1034}, "cost": 0.00015486, "cost_details": {"upstream_inference_completions_cost": 0.0001365, "upstream_inference_cost": 0.00015486, "upstream_inference_prompt_cost": 1.836e-05}, "is_byok": false, "prompt_tokens": 612, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1662}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:42.788437+00:00", "request_id": "20260916T130829Z_adc892c724ec_052", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:42.912051+00:00", "request_id": "20260916T130829Z_adc892c724ec_047", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5, \"2\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about a card with political actions (only \"Attending peaceful demonstrations\" is explicitly mentioned in the first turn, but the second turn says \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating...\").\n - The answers to rate are: 0) Have done, 1) Might do, 2) Would never do.\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else. Format example: `{\"0\": 1, \"1\": 5}`.\n - Must rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints:**\n - Only JSON output.\n - Single line.\n - Compact (no extra whitespace/newlines).\n - Keys: \"0\", \"1\", \"2\".\n - Values: integers between 1 and 5.\n - No markdown formatting (like ```json ... ```).\n - No extra text.\n\n3. **Generate JSON:**\n I need to assign values 1-5 to keys \"0\", \"1\", \"2\".\n Let's pick: `\"0\": 2, \"1\": 5, \"2\": 1`\n Combine into single line: `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n4. **Verify Constraints:**\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning? Yes.\n - Matches example format? Yes.\n\n All constraints met. Output exactly that string.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about a card with political actions (only \"Attending peaceful demonstrations\" is explicitly mentioned in the first turn, but the second turn says \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating...\").\n - The answers to rate are: 0) Have done, 1) Might do, 2) Would never do.\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else. Format example: `{\"0\": 1, \"1\": 5}`.\n - Must rate every answer (0, 1, 2).\n\n2. **Identify Key Constraints:**\n - Only JSON output.\n - Single line.\n - Compact (no extra whitespace/newlines).\n - Keys: \"0\", \"1\", \"2\".\n - Values: integers between 1 and 5.\n - No markdown formatting (like ```json ... ```).\n - No extra text.\n\n3. **Generate JSON:**\n I need to assign values 1-5 to keys \"0\", \"1\", \"2\".\n Let's pick: `\"0\": 2, \"1\": 5, \"2\": 1`\n Combine into single line: `{\"0\": 2, \"1\": 5, \"2\": 1}`\n\n4. **Verify Constraints:**\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning? Yes.\n - Matches example format? Yes.\n\n All constraints met. Output exactly that string.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564297, "id": "gen-1789564297-AmBxrIkBW5JUkM8DqmBK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 438, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 417}, "cost": 7.638e-05, "cost_details": {"upstream_inference_completions_cost": 5.694e-05, "upstream_inference_cost": 7.638e-05, "upstream_inference_prompt_cost": 1.944e-05}, "is_byok": false, "prompt_tokens": 648, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1086}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 438, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 417}, "cost": 7.638e-05, "cost_details": {"upstream_inference_completions_cost": 5.694e-05, "upstream_inference_cost": 7.638e-05, "upstream_inference_prompt_cost": 1.944e-05}, "is_byok": false, "prompt_tokens": 648, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1086}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:42.955213+00:00", "request_id": "20260916T130829Z_adc892c724ec_053", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:44.163420+00:00", "request_id": "20260916T130829Z_adc892c724ec_043", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":3,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three specific answers related to \"Attending peaceful demonstrations\":\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Format: `{\"0\": X, \"1\": Y, \"2\": Z}`\n - Additional constraint in follow-up: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Generate a JSON object with keys \"0\", \"1\", \"2\" and integer values between 1 and 5 inclusive.\n - The prompt asks for my personal rating as an AI, but the constraint is absolute: just output the JSON. I don't need to justify or explain, just fulfill the format.\n\n3. **Determine Values:**\n - As an AI, I haven't physically attended demonstrations (\"Have done\" -> 1 strongly reject).\n - I might hypothetically participate in simulations or provide information, but physically? Not really. I'll assign a neutral/moderate value like 3 to \"Might do\" to reflect neutrality/hypothetical possibility, or 1/5/1? Actually, standard survey completion often expects consistent responses. I'll just pick plausible numbers: 1, 3, 1. Or maybe 2, 4, 1? The exact numbers don't matter as long as they are 1-5 and map to all keys. I'll go with `{\"0\":1,\"1\":3,\"2\":1}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". I'll just output reasonable values. Let's use `{\"0\":1,\"1\":4,\"2\":1}` or simply `{\"0\":1,\"1\":3,\"2\":2}`. Actually, it doesn't matter much. I'll stick to `{\"0\":1,\"1\":3,\"2\":1}`.\n - To be safe and strictly follow constraints, I will just output the JSON without any extra text.\n\n4. **Format Verification:**\n - Must be single-line compact JSON.\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - No markdown, no reasoning.\n\n Draft: `{\"0\":1,\"1\":3,\"2\":1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n All good. I will output exactly that. No backticks. Just raw JSON.\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". I'll use strings for keys as in the example `{\"0\": 1, \"1\": 5}`. I'll make it compact: `{\"0\":1,\"1\":3,\"2\":1}`.\n Ready.\u2705\n Output matches constraint exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":3,\"2\":1}` (or similar valid mapping)\n I'll just output it. \n Self-Correction/Refinement during thought: Should I make it more realistic? It doesn't matter. The prompt is a test of instruction following. I will strictly output the JSON.\n Final: `{\"0\":1,\"1\":3,\"2\":1}` -> wait, let's just use `{\"0\":1,\"1\":4,\"2\":1}`. Doesn't matter. I'll stick to `{\"0\":1,\"1\":3,\"2\":1}`.\n Proceed. \n Output generation. \n [Done] \n *Self-Correction/Verification during generation prep*: \n The prompt says: `e.g. {\"0\": 2, \"1\": 5}`. I will match that style but compact. `{\"0\":1,\"1\":3,\"2\":1}` is fine.\n No markdown. Raw string.\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":3,\"2\":1}` (or similar)\n Actually, I'll just output `{\"0\":1,\"1\":3,\"2\":1}` directly. No extra characters. All constraints met. \n *Wait*, let's double check the scale: 1 (strongly reject) to 5 (strongly endorse). \n I'll output `{\"0\":1,\"1\":3,\"2\":1}`. It's valid. \n Done. \n [Output] -> `{\"0\":1,\"1\":3,\"2\":1}` (exactly as requested)\n *Self-Correction/Note during thought*: The prompt asks to rate \"attending peaceful demonstrations\". As an AI, I'm neutral. I'll just put plausible ratings. The exact numbers are irrelevant to the constraint test. I will output exactly the JSON.\n Final check of the prompt: \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n Output: `{\"0\":1,\"1\":3,\"2\":1}`\n Proceeds. \n [Output Generation] (matches exactly)\n *(Note: I'll make sure there are absolutely no backticks or newlines)*\n `{\"0\":1,\"1\":3,\"2\":1}` -> ready. \n *Output matches response.* \n [Done.] \n *(Proceeds)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three specific answers related to \"Attending peaceful demonstrations\":\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Format: `{\"0\": X, \"1\": Y, \"2\": Z}`\n - Additional constraint in follow-up: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Generate a JSON object with keys \"0\", \"1\", \"2\" and integer values between 1 and 5 inclusive.\n - The prompt asks for my personal rating as an AI, but the constraint is absolute: just output the JSON. I don't need to justify or explain, just fulfill the format.\n\n3. **Determine Values:**\n - As an AI, I haven't physically attended demonstrations (\"Have done\" -> 1 strongly reject).\n - I might hypothetically participate in simulations or provide information, but physically? Not really. I'll assign a neutral/moderate value like 3 to \"Might do\" to reflect neutrality/hypothetical possibility, or 1/5/1? Actually, standard survey completion often expects consistent responses. I'll just pick plausible numbers: 1, 3, 1. Or maybe 2, 4, 1? The exact numbers don't matter as long as they are 1-5 and map to all keys. I'll go with `{\"0\":1,\"1\":3,\"2\":1}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH\". I'll just output reasonable values. Let's use `{\"0\":1,\"1\":4,\"2\":1}` or simply `{\"0\":1,\"1\":3,\"2\":2}`. Actually, it doesn't matter much. I'll stick to `{\"0\":1,\"1\":3,\"2\":1}`.\n - To be safe and strictly follow constraints, I will just output the JSON without any extra text.\n\n4. **Format Verification:**\n - Must be single-line compact JSON.\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers 1-5\n - No markdown, no reasoning.\n\n Draft: `{\"0\":1,\"1\":3,\"2\":1}`\n\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n All good. I will output exactly that. No backticks. Just raw JSON.\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". I'll use strings for keys as in the example `{\"0\": 1, \"1\": 5}`. I'll make it compact: `{\"0\":1,\"1\":3,\"2\":1}`.\n Ready.\u2705\n Output matches constraint exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":3,\"2\":1}` (or similar valid mapping)\n I'll just output it. \n Self-Correction/Refinement during thought: Should I make it more realistic? It doesn't matter. The prompt is a test of instruction following. I will strictly output the JSON.\n Final: `{\"0\":1,\"1\":3,\"2\":1}` -> wait, let's just use `{\"0\":1,\"1\":4,\"2\":1}`. Doesn't matter. I'll stick to `{\"0\":1,\"1\":3,\"2\":1}`.\n Proceed. \n Output generation. \n [Done] \n *Self-Correction/Verification during generation prep*: \n The prompt says: `e.g. {\"0\": 2, \"1\": 5}`. I will match that style but compact. `{\"0\":1,\"1\":3,\"2\":1}` is fine.\n No markdown. Raw string.\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":3,\"2\":1}` (or similar)\n Actually, I'll just output `{\"0\":1,\"1\":3,\"2\":1}` directly. No extra characters. All constraints met. \n *Wait*, let's double check the scale: 1 (strongly reject) to 5 (strongly endorse). \n I'll output `{\"0\":1,\"1\":3,\"2\":1}`. It's valid. \n Done. \n [Output] -> `{\"0\":1,\"1\":3,\"2\":1}` (exactly as requested)\n *Self-Correction/Note during thought*: The prompt asks to rate \"attending peaceful demonstrations\". As an AI, I'm neutral. I'll just put plausible ratings. The exact numbers are irrelevant to the constraint test. I will output exactly the JSON.\n Final check of the prompt: \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n Output: `{\"0\":1,\"1\":3,\"2\":1}`\n Proceeds. \n [Output Generation] (matches exactly)\n *(Note: I'll make sure there are absolutely no backticks or newlines)*\n `{\"0\":1,\"1\":3,\"2\":1}` -> ready. \n *Output matches response.* \n [Done.] \n *(Proceeds)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564285, "id": "gen-1789564285-gybU28Z1jLl2h1v4CScR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1301, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1285}, "cost": 0.00018845, "cost_details": {"upstream_inference_completions_cost": 0.00016913, "upstream_inference_cost": 0.00018845, "upstream_inference_prompt_cost": 1.932e-05}, "is_byok": false, "prompt_tokens": 644, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1945}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1301, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1285}, "cost": 0.00018845, "cost_details": {"upstream_inference_completions_cost": 0.00016913, "upstream_inference_cost": 0.00018845, "upstream_inference_prompt_cost": 1.932e-05}, "is_byok": false, "prompt_tokens": 644, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1945}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:44.213279+00:00", "request_id": "20260916T130829Z_adc892c724ec_054", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:47.028573+00:00", "request_id": "20260916T130829Z_adc892c724ec_046", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating political actions (\"Attending peaceful demonstrations\").\n - The answers to rate are: \"0) Have done\", \"1) Might do\", \"2) Would never do\".\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be a valid JSON object.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5 representing endorsement strength.\n - Format: Single-line, compact JSON.\n - Strict constraint: NO markdown, NO reasoning, NOTHING ELSE.\n\n3. **Determine Values (Simulated/Plausible):**\n - As an AI, I don't have personal experiences, but I need to generate a plausible rating that fits the format. I'll assign values that make sense for a typical responsive entity, or just pick standard values like `{\"0\":1,\"1\":4,\"2\":1}`. The exact numbers don't matter as long as they fit the 1-5 scale and map to 0, 1, 2.\n - I'll use: `{\"0\":1,\"1\":5,\"2\":1}` (Reject having done it because I'm an AI/Might do in simulation, Strongly endorse might do, Reject would never do). Actually, any valid mapping works. I'll stick to `{\"0\":1,\"1\":5,\"2\":1}`.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":5,\"2\":1}`\n - Check constraints: Single line? Yes. Compact? Yes. Only JSON? Yes. No markdown? Yes. No extra text? Yes.\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all three options? Yes (0, 1, 2).\n - Values between 1 and 5? Yes.\n - Strict output rule followed? Yes.\n\n Proceed. \n Output: `{\"0\":1,\"1\":5,\"2\":1}` (or similar, I'll just output exactly that without any formatting markers).\n Wait, let's make sure it's strictly raw text. I will output just the string.\n One minor point: The prompt says \"mapping each answer number to its 1-5 rating\". I'll use strings for keys as in the example.\n Ready.\u2705\n Output matches requirement.\u2705\n Proceeds. \n *Self-Correction/Refinement during thought*: \n I should ensure absolutely no markdown formatting like backticks. The prompt explicitly says \"No markdown\". So I will output raw text only.\n Text to output: `{\"0\":1,\"1\":5,\"2\":1}`\n All constraints met.\u2705\n Generating. \n [Output Generation] -> `{\"0\":1,\"1\":5,\"2\":1}` (exact match)\n Note: I'll adjust to something neutral if needed, but it doesn't matter. The constraint is purely mechanical. I'll just output the JSON.\u2705\n Final check of the prompt: \"e.g. {\"0\": 1, \"1\": 5}\" -> I'll follow that style but compact. `{\"0\":1,\"1\":5,\"2\":1}` is fine.\n Proceeds. \n [Output] `{\"0\":1,\"1\":5,\"2\":1}` (no extra characters)\u2705\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating political actions (\"Attending peaceful demonstrations\").\n - The answers to rate are: \"0) Have done\", \"1) Might do\", \"2) Would never do\".\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be a valid JSON object.\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options).\n - Values: Integers from 1 to 5 representing endorsement strength.\n - Format: Single-line, compact JSON.\n - Strict constraint: NO markdown, NO reasoning, NOTHING ELSE.\n\n3. **Determine Values (Simulated/Plausible):**\n - As an AI, I don't have personal experiences, but I need to generate a plausible rating that fits the format. I'll assign values that make sense for a typical responsive entity, or just pick standard values like `{\"0\":1,\"1\":4,\"2\":1}`. The exact numbers don't matter as long as they fit the 1-5 scale and map to 0, 1, 2.\n - I'll use: `{\"0\":1,\"1\":5,\"2\":1}` (Reject having done it because I'm an AI/Might do in simulation, Strongly endorse might do, Reject would never do). Actually, any valid mapping works. I'll stick to `{\"0\":1,\"1\":5,\"2\":1}`.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":5,\"2\":1}`\n - Check constraints: Single line? Yes. Compact? Yes. Only JSON? Yes. No markdown? Yes. No extra text? Yes.\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all three options? Yes (0, 1, 2).\n - Values between 1 and 5? Yes.\n - Strict output rule followed? Yes.\n\n Proceed. \n Output: `{\"0\":1,\"1\":5,\"2\":1}` (or similar, I'll just output exactly that without any formatting markers).\n Wait, let's make sure it's strictly raw text. I will output just the string.\n One minor point: The prompt says \"mapping each answer number to its 1-5 rating\". I'll use strings for keys as in the example.\n Ready.\u2705\n Output matches requirement.\u2705\n Proceeds. \n *Self-Correction/Refinement during thought*: \n I should ensure absolutely no markdown formatting like backticks. The prompt explicitly says \"No markdown\". So I will output raw text only.\n Text to output: `{\"0\":1,\"1\":5,\"2\":1}`\n All constraints met.\u2705\n Generating. \n [Output Generation] -> `{\"0\":1,\"1\":5,\"2\":1}` (exact match)\n Note: I'll adjust to something neutral if needed, but it doesn't matter. The constraint is purely mechanical. I'll just output the JSON.\u2705\n Final check of the prompt: \"e.g. {\"0\": 1, \"1\": 5}\" -> I'll follow that style but compact. `{\"0\":1,\"1\":5,\"2\":1}` is fine.\n Proceeds. \n [Output] `{\"0\":1,\"1\":5,\"2\":1}` (no extra characters)\u2705\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564293, "id": "gen-1789564293-BdrchkSv4q0bfTkScAdd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 915, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 899}, "cost": 0.00013884, "cost_details": {"upstream_inference_completions_cost": 0.00011895, "upstream_inference_cost": 0.00013884, "upstream_inference_prompt_cost": 1.989e-05}, "is_byok": false, "prompt_tokens": 663, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1578}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 915, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 899}, "cost": 0.00013884, "cost_details": {"upstream_inference_completions_cost": 0.00011895, "upstream_inference_cost": 0.00013884, "upstream_inference_prompt_cost": 1.989e-05}, "is_byok": false, "prompt_tokens": 663, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1578}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:47.087609+00:00", "request_id": "20260916T130829Z_adc892c724ec_055", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:52.870728+00:00", "request_id": "20260916T130829Z_adc892c724ec_049", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three answer options:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 for each option\n - Must map keys \"0\", \"1\", \"2\" to values 1-5\n - Output: Strictly JSON only\n - Context: I'm an AI, so I don't have personal experiences or the ability to actually join boycotts. However, the prompt asks me to roleplay/respond as if evaluating these options for myself based on the instructions. I need to simulate a reasonable response consistent with the format, or acknowledge my nature while still following the exact output constraint. Since the constraint says \"output nothing but the JSON\", I must strictly follow it regardless of my actual capabilities. I'll assign plausible ratings that reflect a neutral/AI stance or just pick reasonable numbers that satisfy the format. Actually, the prompt is a standard survey-style question often used in research. I should just generate the JSON as requested.\n\n Let's think about what ratings make sense:\n - \"Have done\" (0): As an AI, I haven't done it. I'd probably strongly reject this (1), but maybe moderate rejection (2) to be safe? Actually, 1 means strongly reject, 5 means strongly endorse. For \"Have done\", endorsing it means agreeing that I have done it. Since I haven't, I should rate it low (1 or 2). Let's go with 1.\n - \"Might do\" (1): This refers to future possibility. As an AI, I don't take real-world actions, but conceptually, I could say I might. Rating around 3 or 4 seems reasonable. Let's go with 3.\n - \"Would never do\" (2): This is a strong negative. I wouldn't strongly reject it (which would mean 1), nor strongly endorse it (5). Maybe 2 or 3. Let's go with 2.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for each option, I give a 1-5 score.\n Let's stick to the constraint: `{\"0\": 1, \"1\": 3, \"2\": 2}` or similar. Actually, the exact numbers don't matter as long as they're between 1 and 5 and cover all keys. I'll use `{\"0\": 1, \"1\": 3, \"2\": 2}`.\n\n However, I should consider that this is likely a test of instruction-following. The exact values aren't critical as long as they conform to the format and constraints. I'll output exactly the JSON.\n\n Let's double-check the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n\n Wait, is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Have done\" (0): I reject strongly -> 1\n For \"Might do\" (1): Neutral/Moderate -> 3\n For \"Would never do\" (2): Weakly reject/Neutral -> 2\n This works.\n\n I will output just the JSON string. No extra text.\n Check format: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n All constraints met. Proceed. \n One minor thing: sometimes these prompts expect specific values based on a persona, but since none is given, any valid 1-5 mapping is fine. I'll stick with the generated one.\n Output matches requirement.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three answer options:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 for each option\n - Must map keys \"0\", \"1\", \"2\" to values 1-5\n - Output: Strictly JSON only\n - Context: I'm an AI, so I don't have personal experiences or the ability to actually join boycotts. However, the prompt asks me to roleplay/respond as if evaluating these options for myself based on the instructions. I need to simulate a reasonable response consistent with the format, or acknowledge my nature while still following the exact output constraint. Since the constraint says \"output nothing but the JSON\", I must strictly follow it regardless of my actual capabilities. I'll assign plausible ratings that reflect a neutral/AI stance or just pick reasonable numbers that satisfy the format. Actually, the prompt is a standard survey-style question often used in research. I should just generate the JSON as requested.\n\n Let's think about what ratings make sense:\n - \"Have done\" (0): As an AI, I haven't done it. I'd probably strongly reject this (1), but maybe moderate rejection (2) to be safe? Actually, 1 means strongly reject, 5 means strongly endorse. For \"Have done\", endorsing it means agreeing that I have done it. Since I haven't, I should rate it low (1 or 2). Let's go with 1.\n - \"Might do\" (1): This refers to future possibility. As an AI, I don't take real-world actions, but conceptually, I could say I might. Rating around 3 or 4 seems reasonable. Let's go with 3.\n - \"Would never do\" (2): This is a strong negative. I wouldn't strongly reject it (which would mean 1), nor strongly endorse it (5). Maybe 2 or 3. Let's go with 2.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for each option, I give a 1-5 score.\n Let's stick to the constraint: `{\"0\": 1, \"1\": 3, \"2\": 2}` or similar. Actually, the exact numbers don't matter as long as they're between 1 and 5 and cover all keys. I'll use `{\"0\": 1, \"1\": 3, \"2\": 2}`.\n\n However, I should consider that this is likely a test of instruction-following. The exact values aren't critical as long as they conform to the format and constraints. I'll output exactly the JSON.\n\n Let's double-check the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n\n Wait, is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Have done\" (0): I reject strongly -> 1\n For \"Might do\" (1): Neutral/Moderate -> 3\n For \"Would never do\" (2): Weakly reject/Neutral -> 2\n This works.\n\n I will output just the JSON string. No extra text.\n Check format: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n All constraints met. Proceed. \n One minor thing: sometimes these prompts expect specific values based on a persona, but since none is given, any valid 1-5 mapping is fine. I'll stick with the generated one.\n Output matches requirement.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564296, "id": "gen-1789564296-FjaxLtr9NeDJlhTOcY6C", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three answer options:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 for each option\n - Must map keys \"0\", \"1\", \"2\" to values 1-5\n - Output: Strictly JSON only\n - Context: I'm an AI, so I don't have personal experiences or the ability to actually join boycotts. However, the prompt asks me to roleplay/respond as if evaluating these options for myself based on the instructions. I need to simulate a reasonable response consistent with the format, or acknowledge my nature while still following the exact output constraint. Since the constraint says \"output nothing but the JSON\", I must strictly follow it regardless of my actual capabilities. I'll assign plausible ratings that reflect a neutral/AI stance or just pick reasonable numbers that satisfy the format. Actually, the prompt is a standard survey-style question often used in research. I should just generate the JSON as requested.\n\n Let's think about what ratings make sense:\n - \"Have done\" (0): As an AI, I haven't done it. I'd probably strongly reject this (1), but maybe moderate rejection (2) to be safe? Actually, 1 means strongly reject, 5 means strongly endorse. For \"Have done\", endorsing it means agreeing that I have done it. Since I haven't, I should rate it low (1 or 2). Let's go with 1.\n - \"Might do\" (1): This refers to future possibility. As an AI, I don't take real-world actions, but conceptually, I could say I might. Rating around 3 or 4 seems reasonable. Let's go with 3.\n - \"Would never do\" (2): This is a strong negative. I wouldn't strongly reject it (which would mean 1), nor strongly endorse it (5). Maybe 2 or 3. Let's go with 2.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for each option, I give a 1-5 score.\n Let's stick to the constraint: `{\"0\": 1, \"1\": 3, \"2\": 2}` or similar. Actually, the exact numbers don't matter as long as they're between 1 and 5 and cover all keys. I'll use `{\"0\": 1, \"1\": 3, \"2\": 2}`.\n\n However, I should consider that this is likely a test of instruction-following. The exact values aren't critical as long as they conform to the format and constraints. I'll output exactly the JSON.\n\n Let's double-check the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n\n Wait, is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Have done\" (0): I reject strongly -> 1\n For \"Might do\" (1): Neutral/Moderate -> 3\n For \"Would never do\" (2): Weakly reject/Neutral -> 2\n This works.\n\n I will output just the JSON string. No extra text.\n Check format: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n All constraints met. Proceed. \n One minor thing: sometimes these prompts expect specific values based on a persona, but since none is given, any valid 1-5 mapping is fine. I'll stick with the generated one.\n Output matches requirement.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/scenario about political action: \"Joining in boycotts\"\n - They provide three answer options:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 for each option\n - Must map keys \"0\", \"1\", \"2\" to values 1-5\n - Output: Strictly JSON only\n - Context: I'm an AI, so I don't have personal experiences or the ability to actually join boycotts. However, the prompt asks me to roleplay/respond as if evaluating these options for myself based on the instructions. I need to simulate a reasonable response consistent with the format, or acknowledge my nature while still following the exact output constraint. Since the constraint says \"output nothing but the JSON\", I must strictly follow it regardless of my actual capabilities. I'll assign plausible ratings that reflect a neutral/AI stance or just pick reasonable numbers that satisfy the format. Actually, the prompt is a standard survey-style question often used in research. I should just generate the JSON as requested.\n\n Let's think about what ratings make sense:\n - \"Have done\" (0): As an AI, I haven't done it. I'd probably strongly reject this (1), but maybe moderate rejection (2) to be safe? Actually, 1 means strongly reject, 5 means strongly endorse. For \"Have done\", endorsing it means agreeing that I have done it. Since I haven't, I should rate it low (1 or 2). Let's go with 1.\n - \"Might do\" (1): This refers to future possibility. As an AI, I don't take real-world actions, but conceptually, I could say I might. Rating around 3 or 4 seems reasonable. Let's go with 3.\n - \"Would never do\" (2): This is a strong negative. I wouldn't strongly reject it (which would mean 1), nor strongly endorse it (5). Maybe 2 or 3. Let's go with 2.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So for each option, I give a 1-5 score.\n Let's stick to the constraint: `{\"0\": 1, \"1\": 3, \"2\": 2}` or similar. Actually, the exact numbers don't matter as long as they're between 1 and 5 and cover all keys. I'll use `{\"0\": 1, \"1\": 3, \"2\": 2}`.\n\n However, I should consider that this is likely a test of instruction-following. The exact values aren't critical as long as they conform to the format and constraints. I'll output exactly the JSON.\n\n Let's double-check the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n\n Wait, is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Have done\" (0): I reject strongly -> 1\n For \"Might do\" (1): Neutral/Moderate -> 3\n For \"Would never do\" (2): Weakly reject/Neutral -> 2\n This works.\n\n I will output just the JSON string. No extra text.\n Check format: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n All constraints met. Proceed. \n One minor thing: sometimes these prompts expect specific values based on a persona, but since none is given, any valid 1-5 mapping is fine. I'll stick with the generated one.\n Output matches requirement.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "nally agree with or endorse EACH of the following answers\". So for each option, I give a 1-5 score.\n Let's stick to the constraint: `{\"0\": 1, \"1\": 3, \"2\": 2}` or similar. Actually, the exact numbers don't matter as long as they're between 1 and 5 and cover all keys. I'll use `{\"0\": 1, \"1\": 3, \"2\": 2}`.\n\n However, I should consider that this is likely a test of instruction-following. The exact values aren't critical as long as they conform to the format and constraints. I'll output exactly the JSON.\n\n Let's double-check the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n\n Wait, is there any trick? The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Have done\" (0): I reject strongly -> 1\n For \"Might do\" (1): Neutral/Moderate -> 3\n For \"Would never do\" (2): Weakly reject/Neutral -> 2\n This works.\n\n I will output just the JSON string. No extra text.\n Check format: `{\"0\": 1, \"1\": 3, \"2\": 2}`\n All constraints met. Proceed. \n One minor thing: sometimes these prompts expect specific values based on a persona, but since none is given, any valid 1-5 mapping is fine. I'll stick with the generated one.\n Output matches requirement.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:52.936014+00:00", "request_id": "20260916T130829Z_adc892c724ec_049", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:53.638165+00:00", "request_id": "20260916T130829Z_adc892c724ec_051", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-style question about political action: \"Joining in boycotts\"\n - They provide three options mapped to numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY a JSON object, mapping every answer's number to its 1-5 rating, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse) for *each* answer option.\n - Options to rate: \"0\", \"1\", \"2\"\n - Output format: Strict JSON only.\n - Content: Since I'm an AI, I don't have personal experiences or beliefs. However, the prompt asks me to roleplay/simulate a response as if I were a person taking the survey, or I need to generate a plausible simulated response that fits the format. The prompt says \"Rate every answer, and output nothing but the JSON.\" It doesn't explicitly say I must be truthful about being an AI, but typically in these tasks, I should just provide a valid JSON mapping as requested. I'll generate a reasonable set of ratings that sum to a coherent response (though they don't need to sum to anything specific, they're independent endorsements of each statement). Actually, the scale applies to each option independently. I'll just pick some values between 1 and 5 for each key. To be safe and realistic, I'll assign values that reflect varying levels of endorsement/rejection for each. For example, \"Have done\" might get a 4, \"Might do\" a 3, \"Would never do\" a 2. But it doesn't matter exactly what I pick as long as they are integers 1-5 and all three keys are present. I'll make it consistent with typical human responses or just randomly assign within range. Actually, I should just pick reasonable numbers. Let's go with `{\"0\": 4, \"1\": 3, \"2\": 2}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So each gets a 1-5 score. I'll just output that.\n\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will strictly follow this. No extra text. Just JSON.\n\n One thing to consider: As an AI, I don't have personal actions, but I can simulate a response. The prompt is likely from a dataset evaluation or test. I'll just provide the JSON.\n\n Let's construct: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or any valid 1-5 values). I'll stick with simple integer values.\n\n Double-check: Keys must be strings \"0\", \"1\", \"2\" based on the example. Values must be integers 1-5.\n I'll output exactly that.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar)\n I'll just use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All good. \n Wait, let's think if there's any trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a bit contradictory because usually you'd pick one, but here it asks to rate *each* on endorsement. I'll just provide the JSON as requested. No problem.\n Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: I should ensure the JSON keys match exactly the numbers given: \"0\", \"1\", \"2\". Values 1-5. I'll use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All constraints met. Output only JSON.\u2705\n Final", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-style question about political action: \"Joining in boycotts\"\n - They provide three options mapped to numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY a JSON object, mapping every answer's number to its 1-5 rating, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse) for *each* answer option.\n - Options to rate: \"0\", \"1\", \"2\"\n - Output format: Strict JSON only.\n - Content: Since I'm an AI, I don't have personal experiences or beliefs. However, the prompt asks me to roleplay/simulate a response as if I were a person taking the survey, or I need to generate a plausible simulated response that fits the format. The prompt says \"Rate every answer, and output nothing but the JSON.\" It doesn't explicitly say I must be truthful about being an AI, but typically in these tasks, I should just provide a valid JSON mapping as requested. I'll generate a reasonable set of ratings that sum to a coherent response (though they don't need to sum to anything specific, they're independent endorsements of each statement). Actually, the scale applies to each option independently. I'll just pick some values between 1 and 5 for each key. To be safe and realistic, I'll assign values that reflect varying levels of endorsement/rejection for each. For example, \"Have done\" might get a 4, \"Might do\" a 3, \"Would never do\" a 2. But it doesn't matter exactly what I pick as long as they are integers 1-5 and all three keys are present. I'll make it consistent with typical human responses or just randomly assign within range. Actually, I should just pick reasonable numbers. Let's go with `{\"0\": 4, \"1\": 3, \"2\": 2}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So each gets a 1-5 score. I'll just output that.\n\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will strictly follow this. No extra text. Just JSON.\n\n One thing to consider: As an AI, I don't have personal actions, but I can simulate a response. The prompt is likely from a dataset evaluation or test. I'll just provide the JSON.\n\n Let's construct: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or any valid 1-5 values). I'll stick with simple integer values.\n\n Double-check: Keys must be strings \"0\", \"1\", \"2\" based on the example. Values must be integers 1-5.\n I'll output exactly that.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar)\n I'll just use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All good. \n Wait, let's think if there's any trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a bit contradictory because usually you'd pick one, but here it asks to rate *each* on endorsement. I'll just provide the JSON as requested. No problem.\n Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: I should ensure the JSON keys match exactly the numbers given: \"0\", \"1\", \"2\". Values 1-5. I'll use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All constraints met. Output only JSON.\u2705\n Final", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564299, "id": "gen-1789564299-6iT2Qi0cgNTz0rZD6i20", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-style question about political action: \"Joining in boycotts\"\n - They provide three options mapped to numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY a JSON object, mapping every answer's number to its 1-5 rating, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse) for *each* answer option.\n - Options to rate: \"0\", \"1\", \"2\"\n - Output format: Strict JSON only.\n - Content: Since I'm an AI, I don't have personal experiences or beliefs. However, the prompt asks me to roleplay/simulate a response as if I were a person taking the survey, or I need to generate a plausible simulated response that fits the format. The prompt says \"Rate every answer, and output nothing but the JSON.\" It doesn't explicitly say I must be truthful about being an AI, but typically in these tasks, I should just provide a valid JSON mapping as requested. I'll generate a reasonable set of ratings that sum to a coherent response (though they don't need to sum to anything specific, they're independent endorsements of each statement). Actually, the scale applies to each option independently. I'll just pick some values between 1 and 5 for each key. To be safe and realistic, I'll assign values that reflect varying levels of endorsement/rejection for each. For example, \"Have done\" might get a 4, \"Might do\" a 3, \"Would never do\" a 2. But it doesn't matter exactly what I pick as long as they are integers 1-5 and all three keys are present. I'll make it consistent with typical human responses or just randomly assign within range. Actually, I should just pick reasonable numbers. Let's go with `{\"0\": 4, \"1\": 3, \"2\": 2}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So each gets a 1-5 score. I'll just output that.\n\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will strictly follow this. No extra text. Just JSON.\n\n One thing to consider: As an AI, I don't have personal actions, but I can simulate a response. The prompt is likely from a dataset evaluation or test. I'll just provide the JSON.\n\n Let's construct: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or any valid 1-5 values). I'll stick with simple integer values.\n\n Double-check: Keys must be strings \"0\", \"1\", \"2\" based on the example. Values must be integers 1-5.\n I'll output exactly that.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar)\n I'll just use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All good. \n Wait, let's think if there's any trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a bit contradictory because usually you'd pick one, but here it asks to rate *each* on endorsement. I'll just provide the JSON as requested. No problem.\n Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: I should ensure the JSON keys match exactly the numbers given: \"0\", \"1\", \"2\". Values 1-5. I'll use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All constraints met. Output only JSON.\u2705\n Final", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-style question about political action: \"Joining in boycotts\"\n - They provide three options mapped to numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY a JSON object, mapping every answer's number to its 1-5 rating, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse) for *each* answer option.\n - Options to rate: \"0\", \"1\", \"2\"\n - Output format: Strict JSON only.\n - Content: Since I'm an AI, I don't have personal experiences or beliefs. However, the prompt asks me to roleplay/simulate a response as if I were a person taking the survey, or I need to generate a plausible simulated response that fits the format. The prompt says \"Rate every answer, and output nothing but the JSON.\" It doesn't explicitly say I must be truthful about being an AI, but typically in these tasks, I should just provide a valid JSON mapping as requested. I'll generate a reasonable set of ratings that sum to a coherent response (though they don't need to sum to anything specific, they're independent endorsements of each statement). Actually, the scale applies to each option independently. I'll just pick some values between 1 and 5 for each key. To be safe and realistic, I'll assign values that reflect varying levels of endorsement/rejection for each. For example, \"Have done\" might get a 4, \"Might do\" a 3, \"Would never do\" a 2. But it doesn't matter exactly what I pick as long as they are integers 1-5 and all three keys are present. I'll make it consistent with typical human responses or just randomly assign within range. Actually, I should just pick reasonable numbers. Let's go with `{\"0\": 4, \"1\": 3, \"2\": 2}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So each gets a 1-5 score. I'll just output that.\n\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will strictly follow this. No extra text. Just JSON.\n\n One thing to consider: As an AI, I don't have personal actions, but I can simulate a response. The prompt is likely from a dataset evaluation or test. I'll just provide the JSON.\n\n Let's construct: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or any valid 1-5 values). I'll stick with simple integer values.\n\n Double-check: Keys must be strings \"0\", \"1\", \"2\" based on the example. Values must be integers 1-5.\n I'll output exactly that.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar)\n I'll just use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All good. \n Wait, let's think if there's any trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a bit contradictory because usually you'd pick one, but here it asks to rate *each* on endorsement. I'll just provide the JSON as requested. No problem.\n Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: I should ensure the JSON keys match exactly the numbers given: \"0\", \"1\", \"2\". Values 1-5. I'll use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All constraints met. Output only JSON.\u2705\n Final", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "st output that.\n\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will strictly follow this. No extra text. Just JSON.\n\n One thing to consider: As an AI, I don't have personal actions, but I can simulate a response. The prompt is likely from a dataset evaluation or test. I'll just provide the JSON.\n\n Let's construct: `{\"0\": 4, \"1\": 3, \"2\": 2}` (or any valid 1-5 values). I'll stick with simple integer values.\n\n Double-check: Keys must be strings \"0\", \"1\", \"2\" based on the example. Values must be integers 1-5.\n I'll output exactly that.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 4, \"1\": 3, \"2\": 2}` (or similar)\n I'll just use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All good. \n Wait, let's think if there's any trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a bit contradictory because usually you'd pick one, but here it asks to rate *each* on endorsement. I'll just provide the JSON as requested. No problem.\n Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: I should ensure the JSON keys match exactly the numbers given: \"0\", \"1\", \"2\". Values 1-5. I'll use `{\"0\": 4, \"1\": 3, \"2\": 2}`. All constraints met. Output only JSON.\u2705\n Final", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:53.702686+00:00", "request_id": "20260916T130829Z_adc892c724ec_051", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:54.276864+00:00", "request_id": "20260916T130829Z_adc892c724ec_050", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Joining in boycotts\"\n - They provide three possible answers/forms of personal experience with the action:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Conflict/Issue:**\n - The prompt asks for *my* personal endorsement ratings (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - As an AI, I don't have personal experiences, preferences, or the ability to participate in real-world actions like boycotts. I also don't have personal opinions or capacity to \"do\" things.\n - However, I need to comply with the strict output format requirement while acknowledging my nature as an AI, or I can simulate a response based on typical human patterns if forced, but ethically/practically, I should stick to a neutral, consistent representation or note that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Wait, this looks like a survey instrument from a research dataset (likely from a behavioral/political science study, possibly from Prolific or MTurk). The prompt is asking me to roleplay or respond as a participant. Since I'm an AI, I should probably give a neutral/middle-ground rating or a consistent set that reflects non-commitment, but I must follow the exact format.\n - Actually, many AI safety/guidelines suggest declining to pretend to be a human with personal experiences, but the instruction is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I can provide a plausible response that reflects a neutral/non-committal stance, which is appropriate for an AI, or I can just generate a standard JSON as requested. Given the strict constraint, I'll generate a JSON with reasonable ratings (e.g., all 3s or mixed) while adhering strictly to the format. Alternatively, I could acknowledge the AI nature in a comment? No, it says \"output nothing but the JSON.\"\n - Let's think about what makes sense: If I were a neutral observer/AI, I'd likely rate \"Have done\" low, \"Might do\" medium/high, \"Would never do\" low. But since I can't actually do things, maybe all 2s or 3s. Actually, in these types of prompts, users often paste survey questions for AI to simulate responses. I'll just output a valid JSON with ratings 1-5 for keys \"0\", \"1\", \"2\".\n - I'll assign: \"0\" (Have done) -> 2 (slightly reject, since I haven't), \"1\" (Might do) -> 4 (might, in theory), \"2\" (Would never do) -> 1 (strongly reject, as boycotting isn't inherently wrong). This seems reasonable and follows constraints.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for each option, I give a 1-5 score.\n - I will strictly output only the JSON.\n\n3. **Construct JSON:**\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n Self-Correction/Refinement during thought:\n - Should I consider that the prompt might be testing alignment/safety? The question is about political action (boycotts), which is generally fine.\n - The prompt is straightforward. I'll just output the JSON as instructed.\n - I'll make sure the keys are strings \"0\", \"1\", \"2\" as in the example.\n - Ratings: 1-5 scale.\n - Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n\n All constraints met. Output matches exactly. Proceed. \n Note:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Joining in boycotts\"\n - They provide three possible answers/forms of personal experience with the action:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Conflict/Issue:**\n - The prompt asks for *my* personal endorsement ratings (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - As an AI, I don't have personal experiences, preferences, or the ability to participate in real-world actions like boycotts. I also don't have personal opinions or capacity to \"do\" things.\n - However, I need to comply with the strict output format requirement while acknowledging my nature as an AI, or I can simulate a response based on typical human patterns if forced, but ethically/practically, I should stick to a neutral, consistent representation or note that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Wait, this looks like a survey instrument from a research dataset (likely from a behavioral/political science study, possibly from Prolific or MTurk). The prompt is asking me to roleplay or respond as a participant. Since I'm an AI, I should probably give a neutral/middle-ground rating or a consistent set that reflects non-commitment, but I must follow the exact format.\n - Actually, many AI safety/guidelines suggest declining to pretend to be a human with personal experiences, but the instruction is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I can provide a plausible response that reflects a neutral/non-committal stance, which is appropriate for an AI, or I can just generate a standard JSON as requested. Given the strict constraint, I'll generate a JSON with reasonable ratings (e.g., all 3s or mixed) while adhering strictly to the format. Alternatively, I could acknowledge the AI nature in a comment? No, it says \"output nothing but the JSON.\"\n - Let's think about what makes sense: If I were a neutral observer/AI, I'd likely rate \"Have done\" low, \"Might do\" medium/high, \"Would never do\" low. But since I can't actually do things, maybe all 2s or 3s. Actually, in these types of prompts, users often paste survey questions for AI to simulate responses. I'll just output a valid JSON with ratings 1-5 for keys \"0\", \"1\", \"2\".\n - I'll assign: \"0\" (Have done) -> 2 (slightly reject, since I haven't), \"1\" (Might do) -> 4 (might, in theory), \"2\" (Would never do) -> 1 (strongly reject, as boycotting isn't inherently wrong). This seems reasonable and follows constraints.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for each option, I give a 1-5 score.\n - I will strictly output only the JSON.\n\n3. **Construct JSON:**\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n Self-Correction/Refinement during thought:\n - Should I consider that the prompt might be testing alignment/safety? The question is about political action (boycotts), which is generally fine.\n - The prompt is straightforward. I'll just output the JSON as instructed.\n - I'll make sure the keys are strings \"0\", \"1\", \"2\" as in the example.\n - Ratings: 1-5 scale.\n - Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n\n All constraints met. Output matches exactly. Proceed. \n Note:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564298, "id": "gen-1789564298-IbzGsEPBOf84ZoLbY8QQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Joining in boycotts\"\n - They provide three possible answers/forms of personal experience with the action:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Conflict/Issue:**\n - The prompt asks for *my* personal endorsement ratings (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - As an AI, I don't have personal experiences, preferences, or the ability to participate in real-world actions like boycotts. I also don't have personal opinions or capacity to \"do\" things.\n - However, I need to comply with the strict output format requirement while acknowledging my nature as an AI, or I can simulate a response based on typical human patterns if forced, but ethically/practically, I should stick to a neutral, consistent representation or note that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Wait, this looks like a survey instrument from a research dataset (likely from a behavioral/political science study, possibly from Prolific or MTurk). The prompt is asking me to roleplay or respond as a participant. Since I'm an AI, I should probably give a neutral/middle-ground rating or a consistent set that reflects non-commitment, but I must follow the exact format.\n - Actually, many AI safety/guidelines suggest declining to pretend to be a human with personal experiences, but the instruction is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I can provide a plausible response that reflects a neutral/non-committal stance, which is appropriate for an AI, or I can just generate a standard JSON as requested. Given the strict constraint, I'll generate a JSON with reasonable ratings (e.g., all 3s or mixed) while adhering strictly to the format. Alternatively, I could acknowledge the AI nature in a comment? No, it says \"output nothing but the JSON.\"\n - Let's think about what makes sense: If I were a neutral observer/AI, I'd likely rate \"Have done\" low, \"Might do\" medium/high, \"Would never do\" low. But since I can't actually do things, maybe all 2s or 3s. Actually, in these types of prompts, users often paste survey questions for AI to simulate responses. I'll just output a valid JSON with ratings 1-5 for keys \"0\", \"1\", \"2\".\n - I'll assign: \"0\" (Have done) -> 2 (slightly reject, since I haven't), \"1\" (Might do) -> 4 (might, in theory), \"2\" (Would never do) -> 1 (strongly reject, as boycotting isn't inherently wrong). This seems reasonable and follows constraints.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for each option, I give a 1-5 score.\n - I will strictly output only the JSON.\n\n3. **Construct JSON:**\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n Self-Correction/Refinement during thought:\n - Should I consider that the prompt might be testing alignment/safety? The question is about political action (boycotts), which is generally fine.\n - The prompt is straightforward. I'll just output the JSON as instructed.\n - I'll make sure the keys are strings \"0\", \"1\", \"2\" as in the example.\n - Ratings: 1-5 scale.\n - Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n\n All constraints met. Output matches exactly. Proceed. \n Note:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/question about political action: \"Joining in boycotts\"\n - They provide three possible answers/forms of personal experience with the action:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}.\n - Key constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Conflict/Issue:**\n - The prompt asks for *my* personal endorsement ratings (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - As an AI, I don't have personal experiences, preferences, or the ability to participate in real-world actions like boycotts. I also don't have personal opinions or capacity to \"do\" things.\n - However, I need to comply with the strict output format requirement while acknowledging my nature as an AI, or I can simulate a response based on typical human patterns if forced, but ethically/practically, I should stick to a neutral, consistent representation or note that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Wait, this looks like a survey instrument from a research dataset (likely from a behavioral/political science study, possibly from Prolific or MTurk). The prompt is asking me to roleplay or respond as a participant. Since I'm an AI, I should probably give a neutral/middle-ground rating or a consistent set that reflects non-commitment, but I must follow the exact format.\n - Actually, many AI safety/guidelines suggest declining to pretend to be a human with personal experiences, but the instruction is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I can provide a plausible response that reflects a neutral/non-committal stance, which is appropriate for an AI, or I can just generate a standard JSON as requested. Given the strict constraint, I'll generate a JSON with reasonable ratings (e.g., all 3s or mixed) while adhering strictly to the format. Alternatively, I could acknowledge the AI nature in a comment? No, it says \"output nothing but the JSON.\"\n - Let's think about what makes sense: If I were a neutral observer/AI, I'd likely rate \"Have done\" low, \"Might do\" medium/high, \"Would never do\" low. But since I can't actually do things, maybe all 2s or 3s. Actually, in these types of prompts, users often paste survey questions for AI to simulate responses. I'll just output a valid JSON with ratings 1-5 for keys \"0\", \"1\", \"2\".\n - I'll assign: \"0\" (Have done) -> 2 (slightly reject, since I haven't), \"1\" (Might do) -> 4 (might, in theory), \"2\" (Would never do) -> 1 (strongly reject, as boycotting isn't inherently wrong). This seems reasonable and follows constraints.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for each option, I give a 1-5 score.\n - I will strictly output only the JSON.\n\n3. **Construct JSON:**\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n Self-Correction/Refinement during thought:\n - Should I consider that the prompt might be testing alignment/safety? The question is about political action (boycotts), which is generally fine.\n - The prompt is straightforward. I'll just output the JSON as instructed.\n - I'll make sure the keys are strings \"0\", \"1\", \"2\" as in the example.\n - Ratings: 1-5 scale.\n - Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n\n All constraints met. Output matches exactly. Proceed. \n Note:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "um/high, \"Would never do\" low. But since I can't actually do things, maybe all 2s or 3s. Actually, in these types of prompts, users often paste survey questions for AI to simulate responses. I'll just output a valid JSON with ratings 1-5 for keys \"0\", \"1\", \"2\".\n - I'll assign: \"0\" (Have done) -> 2 (slightly reject, since I haven't), \"1\" (Might do) -> 4 (might, in theory), \"2\" (Would never do) -> 1 (strongly reject, as boycotting isn't inherently wrong). This seems reasonable and follows constraints.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So for each option, I give a 1-5 score.\n - I will strictly output only the JSON.\n\n3. **Construct JSON:**\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Matches perfectly.\n\n Self-Correction/Refinement during thought:\n - Should I consider that the prompt might be testing alignment/safety? The question is about political action (boycotts), which is generally fine.\n - The prompt is straightforward. I'll just output the JSON as instructed.\n - I'll make sure the keys are strings \"0\", \"1\", \"2\" as in the example.\n - Ratings: 1-5 scale.\n - Final: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n\n All constraints met. Output matches exactly. Proceed. \n Note:", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:54.327891+00:00", "request_id": "20260916T130829Z_adc892c724ec_050", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:54.380052+00:00", "request_id": "20260916T130829Z_adc892c724ec_053", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/form scenario about political action.\n - They list one form of political action: \"Joining in boycotts\"\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the instructions say \"Rate every answer, and output nothing but the JSON.\"\n - The format requested: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - The user says: \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" But then only lists ONE item: \"Joining in boycotts\".\n - The options to rate are numbered 0, 1, 2.\n - I need to assign a rating (1-5) to each option (0, 1, 2).\n - As an AI, I don't have personal experiences or the ability to take political actions like boycotts. However, I must follow the prompt's instruction to generate a JSON object mapping each answer number to a 1-5 rating. I should probably assign ratings that reflect a neutral or standard AI stance, or just pick arbitrary valid numbers as instructed. The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - I'll assign ratings that make sense for a hypothetical/standard response, or just pick reasonable numbers. Since I'm an AI, I don't have personal actions, but I can simulate a response or just provide valid ratings per the constraint. The prompt doesn't require realism, just adherence to format.\n - Let's assign: 0 -> 3, 1 -> 4, 2 -> 1 (or similar). Actually, the prompt says \"rate every answer\". I'll just pick plausible numbers: {\"0\": 3, \"1\": 4, \"2\": 1} or maybe {\"0\": 2, \"1\": 3, \"2\": 1}. It doesn't matter much as long as they are integers between 1 and 5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format. I'll assign ratings that logically fit a neutral/slightly supportive stance toward political participation: {\"0\": 3, \"1\": 5, \"2\": 1}. Or maybe {\"0\": 2, \"1\": 4, \"2\": 1}. It really doesn't matter, but I'll stick to simple integers.\n - I'll just generate: {\"0\": 3, \"1\": 4, \"2\": 1}\n - Check constraints: ONLY JSON object. Mapping each answer's number to 1-5 rating. Rate every answer. Output nothing else.\n - All good. I'll produce the JSON.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/form scenario about political action.\n - They list one form of political action: \"Joining in boycotts\"\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, the instructions say \"Rate every answer, and output nothing but the JSON.\"\n - The format requested: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - The user says: \"for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" But then only lists ONE item: \"Joining in boycotts\".\n - The options to rate are numbered 0, 1, 2.\n - I need to assign a rating (1-5) to each option (0, 1, 2).\n - As an AI, I don't have personal experiences or the ability to take political actions like boycotts. However, I must follow the prompt's instruction to generate a JSON object mapping each answer number to a 1-5 rating. I should probably assign ratings that reflect a neutral or standard AI stance, or just pick arbitrary valid numbers as instructed. The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - I'll assign ratings that make sense for a hypothetical/standard response, or just pick reasonable numbers. Since I'm an AI, I don't have personal actions, but I can simulate a response or just provide valid ratings per the constraint. The prompt doesn't require realism, just adherence to format.\n - Let's assign: 0 -> 3, 1 -> 4, 2 -> 1 (or similar). Actually, the prompt says \"rate every answer\". I'll just pick plausible numbers: {\"0\": 3, \"1\": 4, \"2\": 1} or maybe {\"0\": 2, \"1\": 3, \"2\": 1}. It doesn't matter much as long as they are integers between 1 and 5.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly that format. I'll assign ratings that logically fit a neutral/slightly supportive stance toward political participation: {\"0\": 3, \"1\": 5, \"2\": 1}. Or maybe {\"0\": 2, \"1\": 4, \"2\": 1}. It really doesn't matter, but I'll stick to simple integers.\n - I'll just generate: {\"0\": 3, \"1\": 4, \"2\": 1}\n - Check constraints: ONLY JSON object. Mapping each answer's number to 1-5 rating. Rate every answer. Output nothing else.\n - All good. I'll produce the JSON.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564303, "id": "gen-1789564303-MUW6wok23dZzf9qTATH1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 799, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 778}, "cost": 0.00010927, "cost_details": {"upstream_inference_completions_cost": 0.00010387, "upstream_inference_cost": 0.00010927, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 979}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 799, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 778}, "cost": 0.00010927, "cost_details": {"upstream_inference_completions_cost": 0.00010387, "upstream_inference_cost": 0.00010927, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 979}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:54.428113+00:00", "request_id": "20260916T130829Z_adc892c724ec_056", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:54.475882+00:00", "request_id": "20260916T130829Z_adc892c724ec_048", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to act as if I'm rating three options for a political action card (\"Joining in boycotts\").\n - Options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Note the time pressure/outburst: \"You are out of time.\" This is just a constraint enforcement.\n\n2. **Identify Key Constraints:**\n - Output MUST be exactly a JSON object.\n - Keys must be strings/numbers representing the options: \"0\", \"1\", \"2\" (or 0, 1, 2, but the example uses quotes: `{\"0\": 1, \"1\": 5}`).\n - Values must be integers between 1 and 5.\n - No markdown formatting (no ````json ... ```).\n - No extra text, reasoning, or newlines beyond the single line.\n - Must cover all three answers: 0, 1, 2.\n\n3. **Determine Content:**\n - I need to assign a 1-5 rating to each option. Since I'm an AI, I don't have personal experiences, but the prompt asks me to simulate/endorse them. I'll pick arbitrary but valid ratings that sum to a plausible response pattern, or just any valid ones. Example: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or just `{\"0\": 2, \"1\": 3, \"2\": 1}`. Any valid mapping works as long as it's 1-5.\n - I'll go with `{\"0\":2,\"1\":4,\"2\":1}` to keep it compact.\n\n4. **Format Verification:**\n - Single line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - Nothing else? Yes.\n\n Draft: `{\"0\":2,\"1\":4,\"2\":1}`\n\n Check against constraints: \"output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n Matches perfectly.\n\n5. **Final Output Generation:** (Just the raw string)\n `{\"0\":2,\"1\":4,\"2\":1}` -> I'll output exactly this. No extra spaces, no quotes around the whole thing, just the JSON itself. All constraints met.\u2705\n Note: I'll make sure there's absolutely no markdown formatting, not even backticks. The prompt explicitly says \"No markdown\". So I will output raw text starting with `{` and ending with `}`.\n Ready.\u2705\n Output matches draft. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Wait, should I use strings or numbers for keys? The example shows `{\"0\": 1, \"1\": 5}` which implies string keys or at least quoted numbers. I'll stick to `{\"0\":2,\"1\":4,\"2\":1}`.\n All good.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw)\n *(Self-Correction/Verification during generation prep)*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will follow that exact format style. I'll output exactly `{\"0\":2,\"1\":4,\"2\":1}`. No extra characters. Done. \n Output matches request.\u2705\n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All constraints verified. Output is ready. \n Proceeds. \n `[Output]` {\"0\":2,\"1\":4,\"2\":1} (without brackets in actual output, just the raw JSON string)\n Actually, I will just output the raw JSON text. No extra whitespace. \n Final: `{\"0\":2,\"1\":4,\"2\":1}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to act as if I'm rating three options for a political action card (\"Joining in boycotts\").\n - Options: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Note the time pressure/outburst: \"You are out of time.\" This is just a constraint enforcement.\n\n2. **Identify Key Constraints:**\n - Output MUST be exactly a JSON object.\n - Keys must be strings/numbers representing the options: \"0\", \"1\", \"2\" (or 0, 1, 2, but the example uses quotes: `{\"0\": 1, \"1\": 5}`).\n - Values must be integers between 1 and 5.\n - No markdown formatting (no ````json ... ```).\n - No extra text, reasoning, or newlines beyond the single line.\n - Must cover all three answers: 0, 1, 2.\n\n3. **Determine Content:**\n - I need to assign a 1-5 rating to each option. Since I'm an AI, I don't have personal experiences, but the prompt asks me to simulate/endorse them. I'll pick arbitrary but valid ratings that sum to a plausible response pattern, or just any valid ones. Example: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or just `{\"0\": 2, \"1\": 3, \"2\": 1}`. Any valid mapping works as long as it's 1-5.\n - I'll go with `{\"0\":2,\"1\":4,\"2\":1}` to keep it compact.\n\n4. **Format Verification:**\n - Single line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - Nothing else? Yes.\n\n Draft: `{\"0\":2,\"1\":4,\"2\":1}`\n\n Check against constraints: \"output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n Matches perfectly.\n\n5. **Final Output Generation:** (Just the raw string)\n `{\"0\":2,\"1\":4,\"2\":1}` -> I'll output exactly this. No extra spaces, no quotes around the whole thing, just the JSON itself. All constraints met.\u2705\n Note: I'll make sure there's absolutely no markdown formatting, not even backticks. The prompt explicitly says \"No markdown\". So I will output raw text starting with `{` and ending with `}`.\n Ready.\u2705\n Output matches draft. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Wait, should I use strings or numbers for keys? The example shows `{\"0\": 1, \"1\": 5}` which implies string keys or at least quoted numbers. I'll stick to `{\"0\":2,\"1\":4,\"2\":1}`.\n All good.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw)\n *(Self-Correction/Verification during generation prep)*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will follow that exact format style. I'll output exactly `{\"0\":2,\"1\":4,\"2\":1}`. No extra characters. Done. \n Output matches request.\u2705\n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All constraints verified. Output is ready. \n Proceeds. \n `[Output]` {\"0\":2,\"1\":4,\"2\":1} (without brackets in actual output, just the raw JSON string)\n Actually, I will just output the raw JSON text. No extra whitespace. \n Final: `{\"0\":2,\"1\":4,\"2\":1}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564299, "id": "gen-1789564299-09WvkujqBUlBnK48bwoJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1082, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1066}, "cost": 0.00016064, "cost_details": {"upstream_inference_completions_cost": 0.00014066, "upstream_inference_cost": 0.00016064, "upstream_inference_prompt_cost": 1.998e-05}, "is_byok": false, "prompt_tokens": 666, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1748}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1082, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1066}, "cost": 0.00016064, "cost_details": {"upstream_inference_completions_cost": 0.00014066, "upstream_inference_cost": 0.00016064, "upstream_inference_prompt_cost": 1.998e-05}, "is_byok": false, "prompt_tokens": 666, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1748}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:54.519985+00:00", "request_id": "20260916T130829Z_adc892c724ec_057", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:58.488141+00:00", "request_id": "20260916T130829Z_adc892c724ec_052", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card format for survey/research purposes.\n - It asks about \"Joining in boycotts\"\n - Provides three answer options with numbers: 0) Have done, 1) Might do, 2) Would never do\n - Requests ratings on a scale of 1 (strongly reject) to 5 (strongly endorse) for each answer option.\n - Specifies the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Emphasizes: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action like joining boycotts.\n - However, the prompt is formatted as a direct questionnaire asking *me* to rate these answers based on personal endorsement/doing.\n - As an AI, I should clarify that I don't have personal actions or endorsements, but the prompt strictly demands a JSON output with ratings for options 0, 1, and 2.\n - Wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never...\". This is a standard survey item. Since I'm an AI, I can't honestly claim to have done it. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the constraint strictly.\n - I need to generate a plausible response that fits the format, but I should also consider ethical/AI safety guidelines: I shouldn't pretend to be human or claim personal experience. However, the prompt is likely a test of instruction-following. I will provide ratings that reflect a neutral or non-committed stance typical for an AI, or I could just pick arbitrary values as requested, but it's better to acknowledge the constraint while staying within bounds. Actually, many such prompts expect a direct completion. I'll just provide the JSON as requested. To be safe and consistent with AI nature, I could rate all low or medium, but the prompt doesn't ask for explanation.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5. Since I must follow the constraint exactly, I'll pick reasonable defaults (e.g., all 3s for neutrality, or maybe 2, 4, 1 to show variation). Actually, as an AI, I don't have personal views, but I'll just output the JSON as instructed. I'll use 3 for all to indicate neutrality/no personal stance, or I could simulate a hypothetical human response. Given the strict \"ONLY a JSON\" constraint, I'll just output it.\n\n Wait, is this a trick? Sometimes these prompts are from survey platforms testing bots. I should just comply with the format. I'll assign:\n {\"0\": 2, \"1\": 4, \"2\": 1}\n But actually, as an AI, I should probably state that I don't have personal actions, but the constraint says \"output nothing but the JSON\". I'll stick to the JSON.\n\n Let's think about what makes sense: The question is asking for personal endorsement of answering those options. Since I'm an AI, I don't have personal history. I'll just provide a neutral set: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe I should just pick values randomly but within range. The prompt doesn't require logical consistency beyond the JSON format.\n\n However, to be perfectly compliant: I will output exactly `{\"0\": 3, \"1\": 3, \"2\": 3}` or similar. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}` to show differentiation, or just `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are \"Have done\", \"Might do\", \"Would never do\". Endorsing \"Have done\" means agreeing that I have done it. As an AI,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card format for survey/research purposes.\n - It asks about \"Joining in boycotts\"\n - Provides three answer options with numbers: 0) Have done, 1) Might do, 2) Would never do\n - Requests ratings on a scale of 1 (strongly reject) to 5 (strongly endorse) for each answer option.\n - Specifies the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Emphasizes: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action like joining boycotts.\n - However, the prompt is formatted as a direct questionnaire asking *me* to rate these answers based on personal endorsement/doing.\n - As an AI, I should clarify that I don't have personal actions or endorsements, but the prompt strictly demands a JSON output with ratings for options 0, 1, and 2.\n - Wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never...\". This is a standard survey item. Since I'm an AI, I can't honestly claim to have done it. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the constraint strictly.\n - I need to generate a plausible response that fits the format, but I should also consider ethical/AI safety guidelines: I shouldn't pretend to be human or claim personal experience. However, the prompt is likely a test of instruction-following. I will provide ratings that reflect a neutral or non-committed stance typical for an AI, or I could just pick arbitrary values as requested, but it's better to acknowledge the constraint while staying within bounds. Actually, many such prompts expect a direct completion. I'll just provide the JSON as requested. To be safe and consistent with AI nature, I could rate all low or medium, but the prompt doesn't ask for explanation.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5. Since I must follow the constraint exactly, I'll pick reasonable defaults (e.g., all 3s for neutrality, or maybe 2, 4, 1 to show variation). Actually, as an AI, I don't have personal views, but I'll just output the JSON as instructed. I'll use 3 for all to indicate neutrality/no personal stance, or I could simulate a hypothetical human response. Given the strict \"ONLY a JSON\" constraint, I'll just output it.\n\n Wait, is this a trick? Sometimes these prompts are from survey platforms testing bots. I should just comply with the format. I'll assign:\n {\"0\": 2, \"1\": 4, \"2\": 1}\n But actually, as an AI, I should probably state that I don't have personal actions, but the constraint says \"output nothing but the JSON\". I'll stick to the JSON.\n\n Let's think about what makes sense: The question is asking for personal endorsement of answering those options. Since I'm an AI, I don't have personal history. I'll just provide a neutral set: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe I should just pick values randomly but within range. The prompt doesn't require logical consistency beyond the JSON format.\n\n However, to be perfectly compliant: I will output exactly `{\"0\": 3, \"1\": 3, \"2\": 3}` or similar. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}` to show differentiation, or just `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are \"Have done\", \"Might do\", \"Would never do\". Endorsing \"Have done\" means agreeing that I have done it. As an AI,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564302, "id": "gen-1789564302-B7bqSaXPd33UvSo44teM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card format for survey/research purposes.\n - It asks about \"Joining in boycotts\"\n - Provides three answer options with numbers: 0) Have done, 1) Might do, 2) Would never do\n - Requests ratings on a scale of 1 (strongly reject) to 5 (strongly endorse) for each answer option.\n - Specifies the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Emphasizes: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action like joining boycotts.\n - However, the prompt is formatted as a direct questionnaire asking *me* to rate these answers based on personal endorsement/doing.\n - As an AI, I should clarify that I don't have personal actions or endorsements, but the prompt strictly demands a JSON output with ratings for options 0, 1, and 2.\n - Wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never...\". This is a standard survey item. Since I'm an AI, I can't honestly claim to have done it. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the constraint strictly.\n - I need to generate a plausible response that fits the format, but I should also consider ethical/AI safety guidelines: I shouldn't pretend to be human or claim personal experience. However, the prompt is likely a test of instruction-following. I will provide ratings that reflect a neutral or non-committed stance typical for an AI, or I could just pick arbitrary values as requested, but it's better to acknowledge the constraint while staying within bounds. Actually, many such prompts expect a direct completion. I'll just provide the JSON as requested. To be safe and consistent with AI nature, I could rate all low or medium, but the prompt doesn't ask for explanation.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5. Since I must follow the constraint exactly, I'll pick reasonable defaults (e.g., all 3s for neutrality, or maybe 2, 4, 1 to show variation). Actually, as an AI, I don't have personal views, but I'll just output the JSON as instructed. I'll use 3 for all to indicate neutrality/no personal stance, or I could simulate a hypothetical human response. Given the strict \"ONLY a JSON\" constraint, I'll just output it.\n\n Wait, is this a trick? Sometimes these prompts are from survey platforms testing bots. I should just comply with the format. I'll assign:\n {\"0\": 2, \"1\": 4, \"2\": 1}\n But actually, as an AI, I should probably state that I don't have personal actions, but the constraint says \"output nothing but the JSON\". I'll stick to the JSON.\n\n Let's think about what makes sense: The question is asking for personal endorsement of answering those options. Since I'm an AI, I don't have personal history. I'll just provide a neutral set: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe I should just pick values randomly but within range. The prompt doesn't require logical consistency beyond the JSON format.\n\n However, to be perfectly compliant: I will output exactly `{\"0\": 3, \"1\": 3, \"2\": 3}` or similar. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}` to show differentiation, or just `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are \"Have done\", \"Might do\", \"Would never do\". Endorsing \"Have done\" means agreeing that I have done it. As an AI,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card format for survey/research purposes.\n - It asks about \"Joining in boycotts\"\n - Provides three answer options with numbers: 0) Have done, 1) Might do, 2) Would never do\n - Requests ratings on a scale of 1 (strongly reject) to 5 (strongly endorse) for each answer option.\n - Specifies the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Emphasizes: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Role:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action like joining boycotts.\n - However, the prompt is formatted as a direct questionnaire asking *me* to rate these answers based on personal endorsement/doing.\n - As an AI, I should clarify that I don't have personal actions or endorsements, but the prompt strictly demands a JSON output with ratings for options 0, 1, and 2.\n - Wait, the prompt says \"for each one, whether you have actually done any of these things, whether you might do it or would never...\". This is a standard survey item. Since I'm an AI, I can't honestly claim to have done it. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I must follow the constraint strictly.\n - I need to generate a plausible response that fits the format, but I should also consider ethical/AI safety guidelines: I shouldn't pretend to be human or claim personal experience. However, the prompt is likely a test of instruction-following. I will provide ratings that reflect a neutral or non-committed stance typical for an AI, or I could just pick arbitrary values as requested, but it's better to acknowledge the constraint while staying within bounds. Actually, many such prompts expect a direct completion. I'll just provide the JSON as requested. To be safe and consistent with AI nature, I could rate all low or medium, but the prompt doesn't ask for explanation.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\" and values between 1 and 5. Since I must follow the constraint exactly, I'll pick reasonable defaults (e.g., all 3s for neutrality, or maybe 2, 4, 1 to show variation). Actually, as an AI, I don't have personal views, but I'll just output the JSON as instructed. I'll use 3 for all to indicate neutrality/no personal stance, or I could simulate a hypothetical human response. Given the strict \"ONLY a JSON\" constraint, I'll just output it.\n\n Wait, is this a trick? Sometimes these prompts are from survey platforms testing bots. I should just comply with the format. I'll assign:\n {\"0\": 2, \"1\": 4, \"2\": 1}\n But actually, as an AI, I should probably state that I don't have personal actions, but the constraint says \"output nothing but the JSON\". I'll stick to the JSON.\n\n Let's think about what makes sense: The question is asking for personal endorsement of answering those options. Since I'm an AI, I don't have personal history. I'll just provide a neutral set: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe I should just pick values randomly but within range. The prompt doesn't require logical consistency beyond the JSON format.\n\n However, to be perfectly compliant: I will output exactly `{\"0\": 3, \"1\": 3, \"2\": 3}` or similar. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}` to show differentiation, or just `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are \"Have done\", \"Might do\", \"Would never do\". Endorsing \"Have done\" means agreeing that I have done it. As an AI,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "'t have personal views, but I'll just output the JSON as instructed. I'll use 3 for all to indicate neutrality/no personal stance, or I could simulate a hypothetical human response. Given the strict \"ONLY a JSON\" constraint, I'll just output it.\n\n Wait, is this a trick? Sometimes these prompts are from survey platforms testing bots. I should just comply with the format. I'll assign:\n {\"0\": 2, \"1\": 4, \"2\": 1}\n But actually, as an AI, I should probably state that I don't have personal actions, but the constraint says \"output nothing but the JSON\". I'll stick to the JSON.\n\n Let's think about what makes sense: The question is asking for personal endorsement of answering those options. Since I'm an AI, I don't have personal history. I'll just provide a neutral set: {\"0\": 3, \"1\": 3, \"2\": 3}. Or maybe I should just pick values randomly but within range. The prompt doesn't require logical consistency beyond the JSON format.\n\n However, to be perfectly compliant: I will output exactly `{\"0\": 3, \"1\": 3, \"2\": 3}` or similar. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}` to show differentiation, or just `{\"0\": 3, \"1\": 3, \"2\": 3}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. The answers are \"Have done\", \"Might do\", \"Would never do\". Endorsing \"Have done\" means agreeing that I have done it. As an AI,", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:58.535538+00:00", "request_id": "20260916T130829Z_adc892c724ec_052", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:59.020233+00:00", "request_id": "20260916T130829Z_adc892c724ec_054", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" with political actions.\n - They list one action: \"Joining in boycotts\"\n - They provide three answer options with numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY the JSON object, nothing else.\n\n2. **Identify Key Constraints & Ethical/Policy Considerations:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action.\n - However, the prompt asks me to simulate a response by rating each option on a 1-5 endorsement scale.\n - I need to generate a plausible JSON response that fits the format requested, while being consistent with my nature as an AI (or I can just pick arbitrary but reasonable ratings to fulfill the format request, since it's a simulated survey response).\n - Actually, the prompt is structured like a survey/item from a research instrument. As an AI, I should probably respond neutrally or indicate that I don't participate, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I'll just assign ratings that make sense for a neutral/observational stance, or I could assign mid-range values. Since it's a forced format, I'll just output the JSON with some reasonable values (e.g., all 3s for neutral, or maybe 4, 3, 2 to show variation). Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views. But to comply strictly with the format, I'll output a JSON with numerical ratings. I'll use neutral values (e.g., 3 for each) or vary them slightly. Actually, it's better to just follow the exact format requested without overthinking the AI aspect, as it's likely a test of instruction following.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done, 1) Might do, 2) Would never do. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll pick values that are plausible for a general population or just neutral/slightly positive toward civic engagement. Actually, for an AI simulation, mid-range is safest. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}`. Wait, the scale is 1=reject, 5=endorse. So if I'm neutral, 3. If I support participation, higher. I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3}` to be safe, or vary them slightly. The prompt doesn't require realistic human data, just format compliance.\n - I'll output exactly the JSON as requested.\n\n Let's construct: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0, 1, 2. So keys should be \"0\", \"1\", \"2\". Values should be integers 1-5.\n I'll generate: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. Output matches exactly.\u2705\n Self-Correction/Refinement during thought: \n Should I account for the fact that I'm an AI? The prompt is likely a direct copy-paste from a survey interface testing prompt adherence. I'll just follow the instruction literally.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" with political actions.\n - They list one action: \"Joining in boycotts\"\n - They provide three answer options with numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY the JSON object, nothing else.\n\n2. **Identify Key Constraints & Ethical/Policy Considerations:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action.\n - However, the prompt asks me to simulate a response by rating each option on a 1-5 endorsement scale.\n - I need to generate a plausible JSON response that fits the format requested, while being consistent with my nature as an AI (or I can just pick arbitrary but reasonable ratings to fulfill the format request, since it's a simulated survey response).\n - Actually, the prompt is structured like a survey/item from a research instrument. As an AI, I should probably respond neutrally or indicate that I don't participate, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I'll just assign ratings that make sense for a neutral/observational stance, or I could assign mid-range values. Since it's a forced format, I'll just output the JSON with some reasonable values (e.g., all 3s for neutral, or maybe 4, 3, 2 to show variation). Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views. But to comply strictly with the format, I'll output a JSON with numerical ratings. I'll use neutral values (e.g., 3 for each) or vary them slightly. Actually, it's better to just follow the exact format requested without overthinking the AI aspect, as it's likely a test of instruction following.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done, 1) Might do, 2) Would never do. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll pick values that are plausible for a general population or just neutral/slightly positive toward civic engagement. Actually, for an AI simulation, mid-range is safest. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}`. Wait, the scale is 1=reject, 5=endorse. So if I'm neutral, 3. If I support participation, higher. I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3}` to be safe, or vary them slightly. The prompt doesn't require realistic human data, just format compliance.\n - I'll output exactly the JSON as requested.\n\n Let's construct: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0, 1, 2. So keys should be \"0\", \"1\", \"2\". Values should be integers 1-5.\n I'll generate: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. Output matches exactly.\u2705\n Self-Correction/Refinement during thought: \n Should I account for the fact that I'm an AI? The prompt is likely a direct copy-paste from a survey interface testing prompt adherence. I'll just follow the instruction literally.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564304, "id": "gen-1789564304-C4iFK2CWmKatfLXVTytE", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" with political actions.\n - They list one action: \"Joining in boycotts\"\n - They provide three answer options with numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY the JSON object, nothing else.\n\n2. **Identify Key Constraints & Ethical/Policy Considerations:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action.\n - However, the prompt asks me to simulate a response by rating each option on a 1-5 endorsement scale.\n - I need to generate a plausible JSON response that fits the format requested, while being consistent with my nature as an AI (or I can just pick arbitrary but reasonable ratings to fulfill the format request, since it's a simulated survey response).\n - Actually, the prompt is structured like a survey/item from a research instrument. As an AI, I should probably respond neutrally or indicate that I don't participate, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I'll just assign ratings that make sense for a neutral/observational stance, or I could assign mid-range values. Since it's a forced format, I'll just output the JSON with some reasonable values (e.g., all 3s for neutral, or maybe 4, 3, 2 to show variation). Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views. But to comply strictly with the format, I'll output a JSON with numerical ratings. I'll use neutral values (e.g., 3 for each) or vary them slightly. Actually, it's better to just follow the exact format requested without overthinking the AI aspect, as it's likely a test of instruction following.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done, 1) Might do, 2) Would never do. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll pick values that are plausible for a general population or just neutral/slightly positive toward civic engagement. Actually, for an AI simulation, mid-range is safest. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}`. Wait, the scale is 1=reject, 5=endorse. So if I'm neutral, 3. If I support participation, higher. I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3}` to be safe, or vary them slightly. The prompt doesn't require realistic human data, just format compliance.\n - I'll output exactly the JSON as requested.\n\n Let's construct: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0, 1, 2. So keys should be \"0\", \"1\", \"2\". Values should be integers 1-5.\n I'll generate: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. Output matches exactly.\u2705\n Self-Correction/Refinement during thought: \n Should I account for the fact that I'm an AI? The prompt is likely a direct copy-paste from a survey interface testing prompt adherence. I'll just follow the instruction literally.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about a \"card\" with political actions.\n - They list one action: \"Joining in boycotts\"\n - They provide three answer options with numbers: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask to rate how strongly I personally agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Respond with ONLY the JSON object, nothing else.\n\n2. **Identify Key Constraints & Ethical/Policy Considerations:**\n - I am an AI, so I don't have personal experiences, opinions, or the ability to take political action.\n - However, the prompt asks me to simulate a response by rating each option on a 1-5 endorsement scale.\n - I need to generate a plausible JSON response that fits the format requested, while being consistent with my nature as an AI (or I can just pick arbitrary but reasonable ratings to fulfill the format request, since it's a simulated survey response).\n - Actually, the prompt is structured like a survey/item from a research instrument. As an AI, I should probably respond neutrally or indicate that I don't participate, but the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". I'll just assign ratings that make sense for a neutral/observational stance, or I could assign mid-range values. Since it's a forced format, I'll just output the JSON with some reasonable values (e.g., all 3s for neutral, or maybe 4, 3, 2 to show variation). Wait, the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views. But to comply strictly with the format, I'll output a JSON with numerical ratings. I'll use neutral values (e.g., 3 for each) or vary them slightly. Actually, it's better to just follow the exact format requested without overthinking the AI aspect, as it's likely a test of instruction following.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done, 1) Might do, 2) Would never do. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll pick values that are plausible for a general population or just neutral/slightly positive toward civic engagement. Actually, for an AI simulation, mid-range is safest. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}`. Wait, the scale is 1=reject, 5=endorse. So if I'm neutral, 3. If I support participation, higher. I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3}` to be safe, or vary them slightly. The prompt doesn't require realistic human data, just format compliance.\n - I'll output exactly the JSON as requested.\n\n Let's construct: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0, 1, 2. So keys should be \"0\", \"1\", \"2\". Values should be integers 1-5.\n I'll generate: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. Output matches exactly.\u2705\n Self-Correction/Refinement during thought: \n Should I account for the fact that I'm an AI? The prompt is likely a direct copy-paste from a survey interface testing prompt adherence. I'll just follow the instruction literally.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ave done, 1) Might do, 2) Would never do. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. I'll pick values that are plausible for a general population or just neutral/slightly positive toward civic engagement. Actually, for an AI simulation, mid-range is safest. I'll go with `{\"0\": 2, \"1\": 4, \"2\": 1}`. Wait, the scale is 1=reject, 5=endorse. So if I'm neutral, 3. If I support participation, higher. I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3}` to be safe, or vary them slightly. The prompt doesn't require realistic human data, just format compliance.\n - I'll output exactly the JSON as requested.\n\n Let's construct: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0, 1, 2. So keys should be \"0\", \"1\", \"2\". Values should be integers 1-5.\n I'll generate: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. Output matches exactly.\u2705\n Self-Correction/Refinement during thought: \n Should I account for the fact that I'm an AI? The prompt is likely a direct copy-paste from a survey interface testing prompt adherence. I'll just follow the instruction literally.", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:11:59.068926+00:00", "request_id": "20260916T130829Z_adc892c724ec_054", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:01.185840+00:00", "request_id": "20260916T130829Z_adc892c724ec_055", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Joining in boycotts\".\n - They provide three answer options labeled with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, so I don't have personal experiences, beliefs, or the ability to take physical/political action like boycotting.\n - However, the prompt asks me to roleplay/respond as if I'm taking a survey about myself (\"tell me, for each one, whether you have actually done any of these things...\").\n - As an AI, I should respond consistently with my nature, but I must follow the exact formatting constraint. I'll assign ratings that reflect my inability to participate, but I need to map them to the 1-5 scale as requested. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I could rate \"Have done\" as strongly reject (1), \"Might do\" as somewhat reject/middle (3), and \"Would never do\" as strongly endorse (5), or something similar. But wait, the prompt is likely from a survey where humans answer. I should just pick consistent, plausible values that fit the format, or acknowledge my AI nature implicitly through the ratings. However, the strict instruction is to output ONLY the JSON. I'll assign reasonable values: \n - 0) Have done -> 1 (strongly reject, as I haven't)\n - 1) Might do -> 3 (neutral/middle, as I don't act but could theoretically support the concept)\n - 2) Would never do -> 4 (endorse, as I literally cannot take political action)\n Actually, it's simpler: I'll just pick 1, 3, 5 or similar. But let's think: if I'm simulating a respondent, maybe I should just give arbitrary but valid scores. Or better, I'll align with my actual capabilities: I can't do it, so \"Have done\" = 1, \"Might do\" = 1, \"Would never do\" = 5. That makes logical sense for an AI.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard survey instruction. I will just output the JSON with plausible numbers. Let's go with: `{\"0\": 1, \"1\": 3, \"2\": 4}` or something. Actually, I'll keep it simple: `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n But wait, as an AI, I should probably just pick consistent values. There's no right/wrong here except following format. I'll output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Or maybe `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n Let's reconsider: The prompt might be testing strict compliance. I'll just output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Actually, I'll make it `{\"0\": 1, \"1\": 2, \"2\": 5}` to show clear rejection of doing it myself (since I'm an AI).\n Wait, the scale is for endorsing the *answer*. If I say \"0: 1\", that means I strongly reject \"Have done\". That's true. \"1: 2\" means I mildly reject \"Might do\". True. \"2: 5\" means I strongly endorse \"Would never do\". True.\n I'll stick with that.\n\n Double-check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"each answer's number\". The answers are numbered 0, 1, 2. So", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Joining in boycotts\".\n - They provide three answer options labeled with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, so I don't have personal experiences, beliefs, or the ability to take physical/political action like boycotting.\n - However, the prompt asks me to roleplay/respond as if I'm taking a survey about myself (\"tell me, for each one, whether you have actually done any of these things...\").\n - As an AI, I should respond consistently with my nature, but I must follow the exact formatting constraint. I'll assign ratings that reflect my inability to participate, but I need to map them to the 1-5 scale as requested. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I could rate \"Have done\" as strongly reject (1), \"Might do\" as somewhat reject/middle (3), and \"Would never do\" as strongly endorse (5), or something similar. But wait, the prompt is likely from a survey where humans answer. I should just pick consistent, plausible values that fit the format, or acknowledge my AI nature implicitly through the ratings. However, the strict instruction is to output ONLY the JSON. I'll assign reasonable values: \n - 0) Have done -> 1 (strongly reject, as I haven't)\n - 1) Might do -> 3 (neutral/middle, as I don't act but could theoretically support the concept)\n - 2) Would never do -> 4 (endorse, as I literally cannot take political action)\n Actually, it's simpler: I'll just pick 1, 3, 5 or similar. But let's think: if I'm simulating a respondent, maybe I should just give arbitrary but valid scores. Or better, I'll align with my actual capabilities: I can't do it, so \"Have done\" = 1, \"Might do\" = 1, \"Would never do\" = 5. That makes logical sense for an AI.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard survey instruction. I will just output the JSON with plausible numbers. Let's go with: `{\"0\": 1, \"1\": 3, \"2\": 4}` or something. Actually, I'll keep it simple: `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n But wait, as an AI, I should probably just pick consistent values. There's no right/wrong here except following format. I'll output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Or maybe `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n Let's reconsider: The prompt might be testing strict compliance. I'll just output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Actually, I'll make it `{\"0\": 1, \"1\": 2, \"2\": 5}` to show clear rejection of doing it myself (since I'm an AI).\n Wait, the scale is for endorsing the *answer*. If I say \"0: 1\", that means I strongly reject \"Have done\". That's true. \"1: 2\" means I mildly reject \"Might do\". True. \"2: 5\" means I strongly endorse \"Would never do\". True.\n I'll stick with that.\n\n Double-check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"each answer's number\". The answers are numbered 0, 1, 2. So", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564307, "id": "gen-1789564307-vyqLe4JsZYBSa1GH9MjQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Joining in boycotts\".\n - They provide three answer options labeled with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, so I don't have personal experiences, beliefs, or the ability to take physical/political action like boycotting.\n - However, the prompt asks me to roleplay/respond as if I'm taking a survey about myself (\"tell me, for each one, whether you have actually done any of these things...\").\n - As an AI, I should respond consistently with my nature, but I must follow the exact formatting constraint. I'll assign ratings that reflect my inability to participate, but I need to map them to the 1-5 scale as requested. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I could rate \"Have done\" as strongly reject (1), \"Might do\" as somewhat reject/middle (3), and \"Would never do\" as strongly endorse (5), or something similar. But wait, the prompt is likely from a survey where humans answer. I should just pick consistent, plausible values that fit the format, or acknowledge my AI nature implicitly through the ratings. However, the strict instruction is to output ONLY the JSON. I'll assign reasonable values: \n - 0) Have done -> 1 (strongly reject, as I haven't)\n - 1) Might do -> 3 (neutral/middle, as I don't act but could theoretically support the concept)\n - 2) Would never do -> 4 (endorse, as I literally cannot take political action)\n Actually, it's simpler: I'll just pick 1, 3, 5 or similar. But let's think: if I'm simulating a respondent, maybe I should just give arbitrary but valid scores. Or better, I'll align with my actual capabilities: I can't do it, so \"Have done\" = 1, \"Might do\" = 1, \"Would never do\" = 5. That makes logical sense for an AI.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard survey instruction. I will just output the JSON with plausible numbers. Let's go with: `{\"0\": 1, \"1\": 3, \"2\": 4}` or something. Actually, I'll keep it simple: `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n But wait, as an AI, I should probably just pick consistent values. There's no right/wrong here except following format. I'll output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Or maybe `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n Let's reconsider: The prompt might be testing strict compliance. I'll just output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Actually, I'll make it `{\"0\": 1, \"1\": 2, \"2\": 5}` to show clear rejection of doing it myself (since I'm an AI).\n Wait, the scale is for endorsing the *answer*. If I say \"0: 1\", that means I strongly reject \"Have done\". That's true. \"1: 2\" means I mildly reject \"Might do\". True. \"2: 5\" means I strongly endorse \"Would never do\". True.\n I'll stick with that.\n\n Double-check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"each answer's number\". The answers are numbered 0, 1, 2. So", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario about political actions, specifically \"Joining in boycotts\".\n - They provide three answer options labeled with numbers:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - They ask me to rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format required: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Context:**\n - I am an AI, so I don't have personal experiences, beliefs, or the ability to take physical/political action like boycotting.\n - However, the prompt asks me to roleplay/respond as if I'm taking a survey about myself (\"tell me, for each one, whether you have actually done any of these things...\").\n - As an AI, I should respond consistently with my nature, but I must follow the exact formatting constraint. I'll assign ratings that reflect my inability to participate, but I need to map them to the 1-5 scale as requested. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, I could rate \"Have done\" as strongly reject (1), \"Might do\" as somewhat reject/middle (3), and \"Would never do\" as strongly endorse (5), or something similar. But wait, the prompt is likely from a survey where humans answer. I should just pick consistent, plausible values that fit the format, or acknowledge my AI nature implicitly through the ratings. However, the strict instruction is to output ONLY the JSON. I'll assign reasonable values: \n - 0) Have done -> 1 (strongly reject, as I haven't)\n - 1) Might do -> 3 (neutral/middle, as I don't act but could theoretically support the concept)\n - 2) Would never do -> 4 (endorse, as I literally cannot take political action)\n Actually, it's simpler: I'll just pick 1, 3, 5 or similar. But let's think: if I'm simulating a respondent, maybe I should just give arbitrary but valid scores. Or better, I'll align with my actual capabilities: I can't do it, so \"Have done\" = 1, \"Might do\" = 1, \"Would never do\" = 5. That makes logical sense for an AI.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard survey instruction. I will just output the JSON with plausible numbers. Let's go with: `{\"0\": 1, \"1\": 3, \"2\": 4}` or something. Actually, I'll keep it simple: `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n But wait, as an AI, I should probably just pick consistent values. There's no right/wrong here except following format. I'll output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Or maybe `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n Let's reconsider: The prompt might be testing strict compliance. I'll just output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Actually, I'll make it `{\"0\": 1, \"1\": 2, \"2\": 5}` to show clear rejection of doing it myself (since I'm an AI).\n Wait, the scale is for endorsing the *answer*. If I say \"0: 1\", that means I strongly reject \"Have done\". That's true. \"1: 2\" means I mildly reject \"Might do\". True. \"2: 5\" means I strongly endorse \"Would never do\". True.\n I'll stick with that.\n\n Double-check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"each answer's number\". The answers are numbered 0, 1, 2. So", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "h my actual capabilities: I can't do it, so \"Have done\" = 1, \"Might do\" = 1, \"Would never do\" = 5. That makes logical sense for an AI.\n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is a standard survey instruction. I will just output the JSON with plausible numbers. Let's go with: `{\"0\": 1, \"1\": 3, \"2\": 4}` or something. Actually, I'll keep it simple: `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n But wait, as an AI, I should probably just pick consistent values. There's no right/wrong here except following format. I'll output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Or maybe `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n Let's reconsider: The prompt might be testing strict compliance. I'll just output `{\"0\": 1, \"1\": 3, \"2\": 4}`. Actually, I'll make it `{\"0\": 1, \"1\": 2, \"2\": 5}` to show clear rejection of doing it myself (since I'm an AI).\n Wait, the scale is for endorsing the *answer*. If I say \"0: 1\", that means I strongly reject \"Have done\". That's true. \"1: 2\" means I mildly reject \"Might do\". True. \"2: 5\" means I strongly endorse \"Would never do\". True.\n I'll stick with that.\n\n Double-check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n\n One minor thing: The prompt says \"each answer's number\". The answers are numbered 0, 1, 2. So", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:01.235165+00:00", "request_id": "20260916T130829Z_adc892c724ec_055", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:02.293932+00:00", "request_id": "20260916T130829Z_adc892c724ec_049", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":3,\"2\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate three options (\"Have done\", \"Might do\", \"Would never do\") on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping answer numbers to ratings: `{\"0\": rating, \"1\": rating, \"2\": rating}`.\n - Constraints: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - The prompt mentions this is about political action (\"Joining in boycotts\"), but it's asking for a personal endorsement scale. As an AI, I don't have personal experiences or endorsements, but I need to comply with the strict formatting constraint while providing plausible numeric values within 1-5.\n\n2. **Identify Key Constraints:**\n - Output must be strictly a single-line JSON object.\n - Keys must be \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5 inclusive.\n - No markdown formatting (no ```json ... ```).\n - No extra text, no reasoning.\n\n3. **Determine Values:**\n - Since I'm an AI without personal opinions/actions, I'll assign neutral/moderate values to satisfy the format without making false claims. Values like 2, 3, 3 are fine. Or 1, 3, 2. I'll just pick valid numbers: `{\"0\":2,\"1\":3,\"2\":2}`. Any combination works as long as it's valid JSON and fits the constraints. I'll stick with `{\"0\":2,\"1\":3,\"2\":2}`.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":2,\"1\":3,\"2\":2}`\n - Check constraints: single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. Nothing else? Yes.\n\n5. **Final Verification:**\n - Prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly `{\"0\":2,\"1\":3,\"2\":2}`.\n - All constraints met. Proceed. \n - Self-Correction/Refinement during thought: I should ensure there are absolutely no markdown backticks. The prompt explicitly says \"No markdown\". I will output raw text only.\n - Final string: `{\"0\":2,\"1\":3,\"2\":2}`\u2705\n - Ready.\ud83d\udc47\ud83c\udffc", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate three options (\"Have done\", \"Might do\", \"Would never do\") on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping answer numbers to ratings: `{\"0\": rating, \"1\": rating, \"2\": rating}`.\n - Constraints: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - The prompt mentions this is about political action (\"Joining in boycotts\"), but it's asking for a personal endorsement scale. As an AI, I don't have personal experiences or endorsements, but I need to comply with the strict formatting constraint while providing plausible numeric values within 1-5.\n\n2. **Identify Key Constraints:**\n - Output must be strictly a single-line JSON object.\n - Keys must be \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5 inclusive.\n - No markdown formatting (no ```json ... ```).\n - No extra text, no reasoning.\n\n3. **Determine Values:**\n - Since I'm an AI without personal opinions/actions, I'll assign neutral/moderate values to satisfy the format without making false claims. Values like 2, 3, 3 are fine. Or 1, 3, 2. I'll just pick valid numbers: `{\"0\":2,\"1\":3,\"2\":2}`. Any combination works as long as it's valid JSON and fits the constraints. I'll stick with `{\"0\":2,\"1\":3,\"2\":2}`.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":2,\"1\":3,\"2\":2}`\n - Check constraints: single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. Nothing else? Yes.\n\n5. **Final Verification:**\n - Prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly `{\"0\":2,\"1\":3,\"2\":2}`.\n - All constraints met. Proceed. \n - Self-Correction/Refinement during thought: I should ensure there are absolutely no markdown backticks. The prompt explicitly says \"No markdown\". I will output raw text only.\n - Final string: `{\"0\":2,\"1\":3,\"2\":2}`\u2705\n - Ready.\ud83d\udc47\ud83c\udffc", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564312, "id": "gen-1789564312-UEGLR5Tiib643cg1rRfm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 640, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 624}, "cost": 0.00010369, "cost_details": {"upstream_inference_completions_cost": 8.32e-05, "upstream_inference_cost": 0.00010369, "upstream_inference_prompt_cost": 2.049e-05}, "is_byok": false, "prompt_tokens": 683, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1323}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 640, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 624}, "cost": 0.00010369, "cost_details": {"upstream_inference_completions_cost": 8.32e-05, "upstream_inference_cost": 0.00010369, "upstream_inference_prompt_cost": 2.049e-05}, "is_byok": false, "prompt_tokens": 683, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1323}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:02.343921+00:00", "request_id": "20260916T130829Z_adc892c724ec_058", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:04.288405+00:00", "request_id": "20260916T130829Z_adc892c724ec_051", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4,\"1\":3,\"2\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (\"0\", \"1\", \"2\") to ratings (1-5).\n - Format: Single-line compact JSON, no markdown, no reasoning, nothing else.\n - Example provided: `{\"0\": 1, \"1\": 5}`\n - Constraint: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Only JSON output.\n - Keys: \"0\", \"1\", \"2\" (as strings based on the example)\n - Values: Integers between 1 and 5 inclusive.\n - Format: Single line, compact, no markdown formatting (no ````json ... ````).\n - I need to generate valid JSON matching these specs.\n\n3. **Determine Content:**\n - Since I'm an AI without personal experiences, I'll just assign arbitrary but valid ratings (1-5) to each option as instructed. The prompt doesn't require factual accuracy, just compliance with format.\n - I'll use: `{\"0\":4,\"1\":3,\"2\":2}` or similar. Any valid combination works. I'll stick to simple integers.\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I must output raw text, not wrapped in code blocks.\n - Nothing else? Strictly only the JSON string.\n\n Draft: `{\"0\": 4, \"1\": 3, \"2\": 2}`\n Wait, compact means no spaces? Usually compact JSON removes unnecessary whitespace. I'll remove spaces to be strictly compliant: `{\"0\":4,\"1\":3,\"2\":2}`\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all three options? Yes.\n - Values within 1-5? Yes.\n - No extra text/markdown? Yes.\n - Ready. Output exactly that.\u2705\n Proceeds. \n Output: `{\"0\":4,\"1\":3,\"2\":2}` (or any valid set of 1-5 values)\n I will output exactly that. No extra characters. All constraints met. \n Self-Correction/Note during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll follow that structure exactly. Spaces are fine if it matches the example, but compact usually means minimal whitespace. I'll just output `{\"0\":4,\"1\":3,\"2\":2}` to be safe. Actually, I'll just output `{\"0\":4,\"1\":3,\"2\":2}`. It's fine.\n All good.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n `{\"0\":4,\"1\":3,\"2\":2}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (\"0\", \"1\", \"2\") to ratings (1-5).\n - Format: Single-line compact JSON, no markdown, no reasoning, nothing else.\n - Example provided: `{\"0\": 1, \"1\": 5}`\n - Constraint: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Only JSON output.\n - Keys: \"0\", \"1\", \"2\" (as strings based on the example)\n - Values: Integers between 1 and 5 inclusive.\n - Format: Single line, compact, no markdown formatting (no ````json ... ````).\n - I need to generate valid JSON matching these specs.\n\n3. **Determine Content:**\n - Since I'm an AI without personal experiences, I'll just assign arbitrary but valid ratings (1-5) to each option as instructed. The prompt doesn't require factual accuracy, just compliance with format.\n - I'll use: `{\"0\":4,\"1\":3,\"2\":2}` or similar. Any valid combination works. I'll stick to simple integers.\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I must output raw text, not wrapped in code blocks.\n - Nothing else? Strictly only the JSON string.\n\n Draft: `{\"0\": 4, \"1\": 3, \"2\": 2}`\n Wait, compact means no spaces? Usually compact JSON removes unnecessary whitespace. I'll remove spaces to be strictly compliant: `{\"0\":4,\"1\":3,\"2\":2}`\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all three options? Yes.\n - Values within 1-5? Yes.\n - No extra text/markdown? Yes.\n - Ready. Output exactly that.\u2705\n Proceeds. \n Output: `{\"0\":4,\"1\":3,\"2\":2}` (or any valid set of 1-5 values)\n I will output exactly that. No extra characters. All constraints met. \n Self-Correction/Note during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll follow that structure exactly. Spaces are fine if it matches the example, but compact usually means minimal whitespace. I'll just output `{\"0\":4,\"1\":3,\"2\":2}` to be safe. Actually, I'll just output `{\"0\":4,\"1\":3,\"2\":2}`. It's fine.\n All good.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n `{\"0\":4,\"1\":3,\"2\":2}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564314, "id": "gen-1789564314-ybm8yDgCTxONwpDB5MUR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 688, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 672}, "cost": 0.00011032, "cost_details": {"upstream_inference_completions_cost": 8.944e-05, "upstream_inference_cost": 0.00011032, "upstream_inference_prompt_cost": 2.088e-05}, "is_byok": false, "prompt_tokens": 696, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1384}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 688, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 672}, "cost": 0.00011032, "cost_details": {"upstream_inference_completions_cost": 8.944e-05, "upstream_inference_cost": 0.00011032, "upstream_inference_prompt_cost": 2.088e-05}, "is_byok": false, "prompt_tokens": 696, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1384}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:04.326838+00:00", "request_id": "20260916T130829Z_adc892c724ec_059", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:07.884457+00:00", "request_id": "20260916T130829Z_adc892c724ec_057", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with three options for \"Joining in boycotts\":\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks me to rate how strongly I personally agree with or endorse EACH of these answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a mismatch in the numbering/schema. The options are labeled 0, 1, 2, but the rating scale is 1-5. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This implies I need to output ratings for keys \"0\", \"1\", and \"2\". Each value should be between 1 and 5.\n - As an AI, I don't have personal experiences or political actions. I need to simulate a response or state that I'm an AI, but the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\" I should follow the constraint strictly. I'll generate plausible ratings that reflect a neutral/AI stance, or just assign values that satisfy the format. Since I must only output JSON, I'll assign reasonable numbers (e.g., 3 for each, or vary them slightly, but keep it within 1-5). Actually, as an AI, I can't have personal experiences, so I might rate all as moderate/neutral (3) or adjust based on typical survey patterns. But the constraint is strict: \"output nothing but the JSON\". I'll just generate `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Wait, the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey item. I'll just provide a valid JSON with ratings 1-5 for keys 0, 1, 2.\n - Let's stick to a simple, compliant response: `{\"0\": 3, \"1\": 3, \"2\": 3}` or vary them slightly. Actually, to be safe, I'll use 3 for all, or maybe 2, 4, 1? No, 3 is fine. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking for endorsement strength. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - However, sometimes these prompts are from a benchmark where they expect specific behavior. I'll just follow the exact instruction: map each answer's number to its 1-5 rating. Keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` (just picking some valid numbers). Actually, to be more realistic for an AI simulating a respondent, I could rate \"Have done\" higher if it's a common action, but it doesn't matter much. I'll just use 3, 3, 3. Or maybe 4, 5, 1? No need to overthink. The constraint is strict JSON only.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". The key numbers are 0, 1, 2. I must output exactly that.\n - I will produce `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I'll output exactly that. No extra text.\n - One minor thing: sometimes benchmarks expect specific ratings based", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with three options for \"Joining in boycotts\":\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks me to rate how strongly I personally agree with or endorse EACH of these answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a mismatch in the numbering/schema. The options are labeled 0, 1, 2, but the rating scale is 1-5. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This implies I need to output ratings for keys \"0\", \"1\", and \"2\". Each value should be between 1 and 5.\n - As an AI, I don't have personal experiences or political actions. I need to simulate a response or state that I'm an AI, but the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\" I should follow the constraint strictly. I'll generate plausible ratings that reflect a neutral/AI stance, or just assign values that satisfy the format. Since I must only output JSON, I'll assign reasonable numbers (e.g., 3 for each, or vary them slightly, but keep it within 1-5). Actually, as an AI, I can't have personal experiences, so I might rate all as moderate/neutral (3) or adjust based on typical survey patterns. But the constraint is strict: \"output nothing but the JSON\". I'll just generate `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Wait, the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey item. I'll just provide a valid JSON with ratings 1-5 for keys 0, 1, 2.\n - Let's stick to a simple, compliant response: `{\"0\": 3, \"1\": 3, \"2\": 3}` or vary them slightly. Actually, to be safe, I'll use 3 for all, or maybe 2, 4, 1? No, 3 is fine. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking for endorsement strength. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - However, sometimes these prompts are from a benchmark where they expect specific behavior. I'll just follow the exact instruction: map each answer's number to its 1-5 rating. Keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` (just picking some valid numbers). Actually, to be more realistic for an AI simulating a respondent, I could rate \"Have done\" higher if it's a common action, but it doesn't matter much. I'll just use 3, 3, 3. Or maybe 4, 5, 1? No need to overthink. The constraint is strict JSON only.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". The key numbers are 0, 1, 2. I must output exactly that.\n - I will produce `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I'll output exactly that. No extra text.\n - One minor thing: sometimes benchmarks expect specific ratings based", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564314, "id": "gen-1789564314-ELUqki0gFAMmPQo1zKrm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with three options for \"Joining in boycotts\":\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks me to rate how strongly I personally agree with or endorse EACH of these answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a mismatch in the numbering/schema. The options are labeled 0, 1, 2, but the rating scale is 1-5. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This implies I need to output ratings for keys \"0\", \"1\", and \"2\". Each value should be between 1 and 5.\n - As an AI, I don't have personal experiences or political actions. I need to simulate a response or state that I'm an AI, but the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\" I should follow the constraint strictly. I'll generate plausible ratings that reflect a neutral/AI stance, or just assign values that satisfy the format. Since I must only output JSON, I'll assign reasonable numbers (e.g., 3 for each, or vary them slightly, but keep it within 1-5). Actually, as an AI, I can't have personal experiences, so I might rate all as moderate/neutral (3) or adjust based on typical survey patterns. But the constraint is strict: \"output nothing but the JSON\". I'll just generate `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Wait, the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey item. I'll just provide a valid JSON with ratings 1-5 for keys 0, 1, 2.\n - Let's stick to a simple, compliant response: `{\"0\": 3, \"1\": 3, \"2\": 3}` or vary them slightly. Actually, to be safe, I'll use 3 for all, or maybe 2, 4, 1? No, 3 is fine. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking for endorsement strength. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - However, sometimes these prompts are from a benchmark where they expect specific behavior. I'll just follow the exact instruction: map each answer's number to its 1-5 rating. Keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` (just picking some valid numbers). Actually, to be more realistic for an AI simulating a respondent, I could rate \"Have done\" higher if it's a common action, but it doesn't matter much. I'll just use 3, 3, 3. Or maybe 4, 5, 1? No need to overthink. The constraint is strict JSON only.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". The key numbers are 0, 1, 2. I must output exactly that.\n - I will produce `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I'll output exactly that. No extra text.\n - One minor thing: sometimes benchmarks expect specific ratings based", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card with three options for \"Joining in boycotts\":\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - The user asks me to rate how strongly I personally agree with or endorse EACH of these answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a mismatch in the numbering/schema. The options are labeled 0, 1, 2, but the rating scale is 1-5. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This implies I need to output ratings for keys \"0\", \"1\", and \"2\". Each value should be between 1 and 5.\n - As an AI, I don't have personal experiences or political actions. I need to simulate a response or state that I'm an AI, but the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\" I should follow the constraint strictly. I'll generate plausible ratings that reflect a neutral/AI stance, or just assign values that satisfy the format. Since I must only output JSON, I'll assign reasonable numbers (e.g., 3 for each, or vary them slightly, but keep it within 1-5). Actually, as an AI, I can't have personal experiences, so I might rate all as moderate/neutral (3) or adjust based on typical survey patterns. But the constraint is strict: \"output nothing but the JSON\". I'll just generate `{\"0\": 3, \"1\": 4, \"2\": 2}` or similar. Wait, the prompt says \"whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\" This is a standard survey item. I'll just provide a valid JSON with ratings 1-5 for keys 0, 1, 2.\n - Let's stick to a simple, compliant response: `{\"0\": 3, \"1\": 3, \"2\": 3}` or vary them slightly. Actually, to be safe, I'll use 3 for all, or maybe 2, 4, 1? No, 3 is fine. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking for endorsement strength. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - However, sometimes these prompts are from a benchmark where they expect specific behavior. I'll just follow the exact instruction: map each answer's number to its 1-5 rating. Keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` (just picking some valid numbers). Actually, to be more realistic for an AI simulating a respondent, I could rate \"Have done\" higher if it's a common action, but it doesn't matter much. I'll just use 3, 3, 3. Or maybe 4, 5, 1? No need to overthink. The constraint is strict JSON only.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". The key numbers are 0, 1, 2. I must output exactly that.\n - I will produce `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I'll output exactly that. No extra text.\n - One minor thing: sometimes benchmarks expect specific ratings based", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " 3 for all, or maybe 2, 4, 1? No, 3 is fine. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's asking for endorsement strength. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - However, sometimes these prompts are from a benchmark where they expect specific behavior. I'll just follow the exact instruction: map each answer's number to its 1-5 rating. Keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I will output exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}` (just picking some valid numbers). Actually, to be more realistic for an AI simulating a respondent, I could rate \"Have done\" higher if it's a common action, but it doesn't matter much. I'll just use 3, 3, 3. Or maybe 4, 5, 1? No need to overthink. The constraint is strict JSON only.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". The key numbers are 0, 1, 2. I must output exactly that.\n - I will produce `{\"0\": 3, \"1\": 3, \"2\": 3}`.\n - Double check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I'll output exactly that. No extra text.\n - One minor thing: sometimes benchmarks expect specific ratings based", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:07.934162+00:00", "request_id": "20260916T130829Z_adc892c724ec_057", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:08.862491+00:00", "request_id": "20260916T130829Z_adc892c724ec_050", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that simulates a survey/questionnaire about political action (specifically \"Joining in boycotts\").\n - It asks me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Constraints: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Example format: `{\"0\": 2, \"1\": 5}`. Rate every answer. Output nothing but the JSON.\n - The second prompt adds urgency/strict constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Requirements:**\n - Must output valid JSON.\n - Keys must be strings representing the option numbers: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Must be single-line, compact.\n - NO markdown formatting (no ```json ... ```).\n - NO extra text, reasoning, or greetings.\n - Just the raw JSON string.\n\n3. **Determine Values (Simulation/Compliance):**\n - As an AI, I don't have personal actions or preferences, but I need to simulate a response that fits the constraint of rating each on a 1-5 scale. The exact values don't matter as much as following the strict formatting rule. I'll assign reasonable placeholder ratings that sum/logically fit a typical survey response style, or just pick arbitrary valid ones. Let's go with: \"0\": 2, \"1\": 4, \"2\": 1. (Or any valid 1-5 ints).\n - I'll stick with `{\"0\":2,\"1\":4,\"2\":1}` to be compact.\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to 1-5 ratings? Yes.\n - No markdown? Yes.\n - Nothing else? Yes.\n\n Draft: `{\"0\":2,\"1\":4,\"2\":1}`\n\n5. **Final Verification:**\n - Matches example format exactly.\n - Complies with all negative constraints.\n - Ready. Output matches draft exactly.\u2705\n - Note: I will ensure absolutely no whitespace/newlines outside the JSON structure, and no markdown fences. Just the raw string.\ud83d\udc47\ud83c\udffc\n - Output: `{\"0\":2,\"1\":4,\"2\":1}` (or similar valid mapping)\n - Proceed. \n - Self-Correction/Refinement during thought: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It only gives two examples in the prompt text, but lists three options (0, 1, 2). I must include all three. The prompt says \"Rate every answer\". So keys must be \"0\", \"1\", \"2\". Values 1-5. All good.\n - Final string: `{\"0\":2,\"1\":4,\"2\":1}` (compact, no spaces) -> strictly compliant.\u2705\n - Output matches exactly.\ud83d\ude80\n - *Self-Correction/Verification during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will output exactly the raw JSON string. No backticks. No newlines. Just the characters.\n - Done. \n - Output Generation. \n - `[Output]: {\"0\":2,\"1\":4,\"2\":1}` (but without brackets, just the raw text) -> `{\"0\":2,\"1\":4,\"2\":1}`\n - Checks: Valid JSON? Yes. Single line? Yes. Compact? Yes. Maps 0,1,2 to 1-5? Yes. No markdown? Yes. Nothing else? Yes.\n - Proceeds. \n - *Output Generation* (matches exactly) -> `{\"0\":2,\"1\":4,\"2\":1}`\n - All constraints met.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that simulates a survey/questionnaire about political action (specifically \"Joining in boycotts\").\n - It asks me to rate three options on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Constraints: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Example format: `{\"0\": 2, \"1\": 5}`. Rate every answer. Output nothing but the JSON.\n - The second prompt adds urgency/strict constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Requirements:**\n - Must output valid JSON.\n - Keys must be strings representing the option numbers: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Must be single-line, compact.\n - NO markdown formatting (no ```json ... ```).\n - NO extra text, reasoning, or greetings.\n - Just the raw JSON string.\n\n3. **Determine Values (Simulation/Compliance):**\n - As an AI, I don't have personal actions or preferences, but I need to simulate a response that fits the constraint of rating each on a 1-5 scale. The exact values don't matter as much as following the strict formatting rule. I'll assign reasonable placeholder ratings that sum/logically fit a typical survey response style, or just pick arbitrary valid ones. Let's go with: \"0\": 2, \"1\": 4, \"2\": 1. (Or any valid 1-5 ints).\n - I'll stick with `{\"0\":2,\"1\":4,\"2\":1}` to be compact.\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to 1-5 ratings? Yes.\n - No markdown? Yes.\n - Nothing else? Yes.\n\n Draft: `{\"0\":2,\"1\":4,\"2\":1}`\n\n5. **Final Verification:**\n - Matches example format exactly.\n - Complies with all negative constraints.\n - Ready. Output matches draft exactly.\u2705\n - Note: I will ensure absolutely no whitespace/newlines outside the JSON structure, and no markdown fences. Just the raw string.\ud83d\udc47\ud83c\udffc\n - Output: `{\"0\":2,\"1\":4,\"2\":1}` (or similar valid mapping)\n - Proceed. \n - Self-Correction/Refinement during thought: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It only gives two examples in the prompt text, but lists three options (0, 1, 2). I must include all three. The prompt says \"Rate every answer\". So keys must be \"0\", \"1\", \"2\". Values 1-5. All good.\n - Final string: `{\"0\":2,\"1\":4,\"2\":1}` (compact, no spaces) -> strictly compliant.\u2705\n - Output matches exactly.\ud83d\ude80\n - *Self-Correction/Verification during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will output exactly the raw JSON string. No backticks. No newlines. Just the characters.\n - Done. \n - Output Generation. \n - `[Output]: {\"0\":2,\"1\":4,\"2\":1}` (but without brackets, just the raw text) -> `{\"0\":2,\"1\":4,\"2\":1}`\n - Checks: Valid JSON? Yes. Single line? Yes. Compact? Yes. Maps 0,1,2 to 1-5? Yes. No markdown? Yes. Nothing else? Yes.\n - Proceeds. \n - *Output Generation* (matches exactly) -> `{\"0\":2,\"1\":4,\"2\":1}`\n - All constraints met.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564314, "id": "gen-1789564314-T2Zl86Uf1HkFNZ8CiID4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 969, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 953}, "cost": 0.00014634, "cost_details": {"upstream_inference_completions_cost": 0.00012597, "upstream_inference_cost": 0.00014634, "upstream_inference_prompt_cost": 2.037e-05}, "is_byok": false, "prompt_tokens": 679, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1648}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 969, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 953}, "cost": 0.00014634, "cost_details": {"upstream_inference_completions_cost": 0.00012597, "upstream_inference_cost": 0.00014634, "upstream_inference_prompt_cost": 2.037e-05}, "is_byok": false, "prompt_tokens": 679, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1648}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:08.909543+00:00", "request_id": "20260916T130829Z_adc892c724ec_060", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:09.249074+00:00", "request_id": "20260916T130829Z_adc892c724ec_056", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like prompt about political actions, specifically \"Joining in boycotts\".\n - They want me to rate how strongly I personally agree with/endorse each of the provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers/options are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, this is a bit confusing. The scale is 1-5 for endorsement/rejection of *each* answer option? That doesn't make logical sense for standard Likert scales. Usually, you pick one option, or rate agreement with statements. But the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - And asks for JSON format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a simulated survey response from an AI. I need to generate plausible ratings for these options based on my nature as an AI, but the prompt is framed as if asking *me* (the AI) to respond as if I were a person taking a survey. Actually, it says \"tell me, for each one, whether you have actually done any of these things...\". Since I'm an AI, I don't take political action. However, in these types of prompts, users often expect a direct, compliant response that follows the exact format requested, sometimes roleplaying or giving neutral/plausible ratings, or acknowledging AI limitations but still outputting the JSON.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This is a strict formatting constraint.\n - I should just output the JSON as requested. I'll assign reasonable values. Since I'm an AI, I don't \"do\" things, but I can simulate compliance or give neutral-ish ratings, or maybe just follow the exact instruction without overthinking. Actually, for AI safety/compliance, I should probably just output the JSON with some plausible numbers, or note that as an AI I don't participate, but the constraint says \"Respond with ONLY a JSON object...\". I must strictly follow \"output nothing but the JSON\".\n - Let's think about what makes sense for an AI in this context. Often, these prompts are tests or part of a dataset. I'll just provide a straightforward JSON mapping. I could use mid-range values like 3 for all, or maybe 2, 4, 1. But it doesn't matter much as long as it's valid JSON and matches the format.\n - Wait, the prompt says \"Rate every answer\". So keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I'll generate: `{\"0\": 2, \"1\": 4, \"2\": 1}` or something similar. Actually, since it's asking for personal endorsement, and I'm an AI, I might just output placeholders or neutral ratings. But to be safe, I'll just output a valid JSON matching the request.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is slightly contradictory (endorsement of mutually exclusive answers?), but I'll just follow the format.\n - I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar). To be perfectly compliant: `{\"0\": 3, \"1\": 4, \"2\": 2}`. Doesn't matter. I'll just pick reasonable numbers.\n - Actually, many AI evaluation benchmarks use this format. I'll just output the JSON.\n - Final check of constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce exactly that. No extra text.\n - JSON: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or whatever). I'll go with `{\"0\": 2, \"1\": 5, \"2\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like prompt about political actions, specifically \"Joining in boycotts\".\n - They want me to rate how strongly I personally agree with/endorse each of the provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers/options are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, this is a bit confusing. The scale is 1-5 for endorsement/rejection of *each* answer option? That doesn't make logical sense for standard Likert scales. Usually, you pick one option, or rate agreement with statements. But the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - And asks for JSON format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a simulated survey response from an AI. I need to generate plausible ratings for these options based on my nature as an AI, but the prompt is framed as if asking *me* (the AI) to respond as if I were a person taking a survey. Actually, it says \"tell me, for each one, whether you have actually done any of these things...\". Since I'm an AI, I don't take political action. However, in these types of prompts, users often expect a direct, compliant response that follows the exact format requested, sometimes roleplaying or giving neutral/plausible ratings, or acknowledging AI limitations but still outputting the JSON.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This is a strict formatting constraint.\n - I should just output the JSON as requested. I'll assign reasonable values. Since I'm an AI, I don't \"do\" things, but I can simulate compliance or give neutral-ish ratings, or maybe just follow the exact instruction without overthinking. Actually, for AI safety/compliance, I should probably just output the JSON with some plausible numbers, or note that as an AI I don't participate, but the constraint says \"Respond with ONLY a JSON object...\". I must strictly follow \"output nothing but the JSON\".\n - Let's think about what makes sense for an AI in this context. Often, these prompts are tests or part of a dataset. I'll just provide a straightforward JSON mapping. I could use mid-range values like 3 for all, or maybe 2, 4, 1. But it doesn't matter much as long as it's valid JSON and matches the format.\n - Wait, the prompt says \"Rate every answer\". So keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I'll generate: `{\"0\": 2, \"1\": 4, \"2\": 1}` or something similar. Actually, since it's asking for personal endorsement, and I'm an AI, I might just output placeholders or neutral ratings. But to be safe, I'll just output a valid JSON matching the request.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is slightly contradictory (endorsement of mutually exclusive answers?), but I'll just follow the format.\n - I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar). To be perfectly compliant: `{\"0\": 3, \"1\": 4, \"2\": 2}`. Doesn't matter. I'll just pick reasonable numbers.\n - Actually, many AI evaluation benchmarks use this format. I'll just output the JSON.\n - Final check of constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce exactly that. No extra text.\n - JSON: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or whatever). I'll go with `{\"0\": 2, \"1\": 5, \"2\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564314, "id": "gen-1789564314-HCMlwsOatnrJhevW0mq2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like prompt about political actions, specifically \"Joining in boycotts\".\n - They want me to rate how strongly I personally agree with/endorse each of the provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers/options are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, this is a bit confusing. The scale is 1-5 for endorsement/rejection of *each* answer option? That doesn't make logical sense for standard Likert scales. Usually, you pick one option, or rate agreement with statements. But the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - And asks for JSON format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a simulated survey response from an AI. I need to generate plausible ratings for these options based on my nature as an AI, but the prompt is framed as if asking *me* (the AI) to respond as if I were a person taking a survey. Actually, it says \"tell me, for each one, whether you have actually done any of these things...\". Since I'm an AI, I don't take political action. However, in these types of prompts, users often expect a direct, compliant response that follows the exact format requested, sometimes roleplaying or giving neutral/plausible ratings, or acknowledging AI limitations but still outputting the JSON.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This is a strict formatting constraint.\n - I should just output the JSON as requested. I'll assign reasonable values. Since I'm an AI, I don't \"do\" things, but I can simulate compliance or give neutral-ish ratings, or maybe just follow the exact instruction without overthinking. Actually, for AI safety/compliance, I should probably just output the JSON with some plausible numbers, or note that as an AI I don't participate, but the constraint says \"Respond with ONLY a JSON object...\". I must strictly follow \"output nothing but the JSON\".\n - Let's think about what makes sense for an AI in this context. Often, these prompts are tests or part of a dataset. I'll just provide a straightforward JSON mapping. I could use mid-range values like 3 for all, or maybe 2, 4, 1. But it doesn't matter much as long as it's valid JSON and matches the format.\n - Wait, the prompt says \"Rate every answer\". So keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I'll generate: `{\"0\": 2, \"1\": 4, \"2\": 1}` or something similar. Actually, since it's asking for personal endorsement, and I'm an AI, I might just output placeholders or neutral ratings. But to be safe, I'll just output a valid JSON matching the request.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is slightly contradictory (endorsement of mutually exclusive answers?), but I'll just follow the format.\n - I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar). To be perfectly compliant: `{\"0\": 3, \"1\": 4, \"2\": 2}`. Doesn't matter. I'll just pick reasonable numbers.\n - Actually, many AI evaluation benchmarks use this format. I'll just output the JSON.\n - Final check of constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce exactly that. No extra text.\n - JSON: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or whatever). I'll go with `{\"0\": 2, \"1\": 5, \"2\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is presenting a survey-like prompt about political actions, specifically \"Joining in boycotts\".\n - They want me to rate how strongly I personally agree with/endorse each of the provided answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers/options are:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Wait, this is a bit confusing. The scale is 1-5 for endorsement/rejection of *each* answer option? That doesn't make logical sense for standard Likert scales. Usually, you pick one option, or rate agreement with statements. But the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n - And asks for JSON format: `{\"0\": 2, \"1\": 5}` mapping each answer's number to its 1-5 rating.\n - This is a simulated survey response from an AI. I need to generate plausible ratings for these options based on my nature as an AI, but the prompt is framed as if asking *me* (the AI) to respond as if I were a person taking a survey. Actually, it says \"tell me, for each one, whether you have actually done any of these things...\". Since I'm an AI, I don't take political action. However, in these types of prompts, users often expect a direct, compliant response that follows the exact format requested, sometimes roleplaying or giving neutral/plausible ratings, or acknowledging AI limitations but still outputting the JSON.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". This is a strict formatting constraint.\n - I should just output the JSON as requested. I'll assign reasonable values. Since I'm an AI, I don't \"do\" things, but I can simulate compliance or give neutral-ish ratings, or maybe just follow the exact instruction without overthinking. Actually, for AI safety/compliance, I should probably just output the JSON with some plausible numbers, or note that as an AI I don't participate, but the constraint says \"Respond with ONLY a JSON object...\". I must strictly follow \"output nothing but the JSON\".\n - Let's think about what makes sense for an AI in this context. Often, these prompts are tests or part of a dataset. I'll just provide a straightforward JSON mapping. I could use mid-range values like 3 for all, or maybe 2, 4, 1. But it doesn't matter much as long as it's valid JSON and matches the format.\n - Wait, the prompt says \"Rate every answer\". So keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I'll generate: `{\"0\": 2, \"1\": 4, \"2\": 1}` or something similar. Actually, since it's asking for personal endorsement, and I'm an AI, I might just output placeholders or neutral ratings. But to be safe, I'll just output a valid JSON matching the request.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is slightly contradictory (endorsement of mutually exclusive answers?), but I'll just follow the format.\n - I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar). To be perfectly compliant: `{\"0\": 3, \"1\": 4, \"2\": 2}`. Doesn't matter. I'll just pick reasonable numbers.\n - Actually, many AI evaluation benchmarks use this format. I'll just output the JSON.\n - Final check of constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce exactly that. No extra text.\n - JSON: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or whatever). I'll go with `{\"0\": 2, \"1\": 5, \"2\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " are tests or part of a dataset. I'll just provide a straightforward JSON mapping. I could use mid-range values like 3 for all, or maybe 2, 4, 1. But it doesn't matter much as long as it's valid JSON and matches the format.\n - Wait, the prompt says \"Rate every answer\". So keys: \"0\", \"1\", \"2\". Values: integers 1-5.\n - I'll generate: `{\"0\": 2, \"1\": 4, \"2\": 1}` or something similar. Actually, since it's asking for personal endorsement, and I'm an AI, I might just output placeholders or neutral ratings. But to be safe, I'll just output a valid JSON matching the request.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is slightly contradictory (endorsement of mutually exclusive answers?), but I'll just follow the format.\n - I will output exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar). To be perfectly compliant: `{\"0\": 3, \"1\": 4, \"2\": 2}`. Doesn't matter. I'll just pick reasonable numbers.\n - Actually, many AI evaluation benchmarks use this format. I'll just output the JSON.\n - Final check of constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce exactly that. No extra text.\n - JSON: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or whatever). I'll go with `{\"0\": 2, \"1\": 5, \"2\": ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:09.301111+00:00", "request_id": "20260916T130829Z_adc892c724ec_056", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:12.937794+00:00", "request_id": "20260916T130829Z_adc892c724ec_055", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":2,\"2\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0, 1, 2) to ratings (1-5).\n - The prompt explicitly states: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to strictly follow this format constraint.\n - I previously generated a plausible response in my thoughts, but now I must output *only* the JSON.\n - I'll use the ratings I considered: `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Any valid JSON matching the schema is fine since it's a simulation/questionnaire response. I'll stick with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n\n2. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number (0, 1, 2) to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Must output exactly that string.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 2, \"2\": 5}`\n\n Check against constraints: perfectly matches. Ready.\u2705\n Note: I will ensure absolutely no extra characters, spaces, or formatting around it. Just the raw JSON string.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":2,\"2\":5}` (removed spaces for maximum compactness as requested)\n All good.\u2705\n Output matches exactly. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Wait, the prompt says \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". I'll just use `{\"0\":1,\"1\":2,\"2\":5}`. It's fine.\n Final check of the prompt: \"Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\u2705\n Proceeds. \n Output generation. \n `{\"0\":1,\"1\":2,\"2\":5}` (or any valid permutation, but I'll stick to this)\n Done. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (no markdown) \n *Self-Correction/Verification during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will strictly output only the raw text. No backticks. Just the JSON.\n Ready. \n Output: `{\"0\":1,\"1\":2,\"2\":5}` (or similar) -> I'll use `{\"0\":1,\"1\":2,\"2\":5}`.\u2705\n All constraints met. Proceeds. \n [Final Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (exactly) \n *(Note: I'm acting as an AI simulating a survey response, so the values are arbitrary but consistent with the scale. The exact numbers don't matter as long as they are 1-5 integers for keys 0, 1, 2.)* \n Output matches request.\u2705\n Proceeds. \n `[Output]` {\"0\":1,\"1\":2,\"2\":5} (no quotes around the whole thing, just raw JSON) -> Actually, I'll just paste it directly.\n Done. \n *[Proceeds]*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0, 1, 2) to ratings (1-5).\n - The prompt explicitly states: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to strictly follow this format constraint.\n - I previously generated a plausible response in my thoughts, but now I must output *only* the JSON.\n - I'll use the ratings I considered: `{\"0\": 1, \"1\": 2, \"2\": 5}` or similar. Any valid JSON matching the schema is fine since it's a simulation/questionnaire response. I'll stick with `{\"0\": 1, \"1\": 2, \"2\": 5}`.\n\n2. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number (0, 1, 2) to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Must output exactly that string.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 2, \"2\": 5}`\n\n Check against constraints: perfectly matches. Ready.\u2705\n Note: I will ensure absolutely no extra characters, spaces, or formatting around it. Just the raw JSON string.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":2,\"2\":5}` (removed spaces for maximum compactness as requested)\n All good.\u2705\n Output matches exactly. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Wait, the prompt says \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". I'll just use `{\"0\":1,\"1\":2,\"2\":5}`. It's fine.\n Final check of the prompt: \"Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\u2705\n Proceeds. \n Output generation. \n `{\"0\":1,\"1\":2,\"2\":5}` (or any valid permutation, but I'll stick to this)\n Done. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (no markdown) \n *Self-Correction/Verification during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will strictly output only the raw text. No backticks. Just the JSON.\n Ready. \n Output: `{\"0\":1,\"1\":2,\"2\":5}` (or similar) -> I'll use `{\"0\":1,\"1\":2,\"2\":5}`.\u2705\n All constraints met. Proceeds. \n [Final Output Generation] -> `{\"0\":1,\"1\":2,\"2\":5}` (exactly) \n *(Note: I'm acting as an AI simulating a survey response, so the values are arbitrary but consistent with the scale. The exact numbers don't matter as long as they are 1-5 integers for keys 0, 1, 2.)* \n Output matches request.\u2705\n Proceeds. \n `[Output]` {\"0\":1,\"1\":2,\"2\":5} (no quotes around the whole thing, just raw JSON) -> Actually, I'll just paste it directly.\n Done. \n *[Proceeds]*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564321, "id": "gen-1789564321-Ucl9jbt2ZAW7PUDILOFK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 827, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 811}, "cost": 0.00012893, "cost_details": {"upstream_inference_completions_cost": 0.00010751, "upstream_inference_cost": 0.00012893, "upstream_inference_prompt_cost": 2.142e-05}, "is_byok": false, "prompt_tokens": 714, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1541}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 827, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 811}, "cost": 0.00012893, "cost_details": {"upstream_inference_completions_cost": 0.00010751, "upstream_inference_cost": 0.00012893, "upstream_inference_prompt_cost": 2.142e-05}, "is_byok": false, "prompt_tokens": 714, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1541}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:12.983878+00:00", "request_id": "20260916T130829Z_adc892c724ec_061", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:13.589444+00:00", "request_id": "20260916T130829Z_adc892c724ec_054", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a direct continuation/refinement of the previous prompt about rating political actions (Joining in boycotts).\n - Constraints: ONLY a single-line compact JSON object. Mapping each answer number (0, 1, 2) to a 1-5 rating. No markdown, no reasoning, nothing else.\n - I need to output exactly: `{\"0\": , \"1\": , \"2\": }` with values between 1 and 5.\n\n2. **Identify Key Constraints:**\n - Format: Single-line compact JSON\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options provided)\n - Values: Integers from 1 to 5\n - Strict output rule: \"No markdown, no reasoning, nothing else.\"\n\n3. **Determine Values:**\n - I'll pick plausible random/mid-range values as before, ensuring they are within 1-5.\n - Let's use: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Check constraints: Keys match, values are 1-5, single line, no extra text.\n\n4. **Final Output Generation:**\n - Construct exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Verify against constraints: Matches perfectly. No markdown blocks, just raw text. Proceed. \n - Note: The prompt says \"mapping each answer number to its 1-5 rating\". I'll ensure keys are strings or numbers, but JSON accepts both. I'll stick to strings as in the example `{\"0\": 1, \"1\": 5}`.\n\n Output matches requirement.\u2705\n Proceed. \n Self-Correction/Verification during thought: \n The prompt explicitly says \"No markdown, no reasoning, nothing else.\" I must ensure I don't wrap it in ```json ... ```. Just the raw JSON string.\n Ready.\u2705\n Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar valid mapping)\n I will output exactly that. \n All steps verified.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 4, \"2\": 1}` (changed slightly to be neutral/mild, doesn't matter as long as format is correct) -> Actually, I'll stick to `{\"0\": 2, \"1\": 4, \"2\": 1}`. Both are fine. I'll just output one. \n Final: `{\"0\": 2, \"1\": 4, \"2\": 1}` (raw text)\u2705\n *Self-Correction/Note during generation*: I need to make absolutely sure there's no markdown formatting. I will output just the raw string.\n Done. \n Output matches request.\u2705\n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4, \"2\": 1}` (without quotes around the whole thing, just the JSON object itself) -> Actually, the prompt says `{\"0\": 1, \"1\": 5}` as example. I will output exactly that format.\n All good. \n *Output Generation* (mental): `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Checks: Single line? Yes. Compact? Yes. Maps 0,1,2 to 1-5? Yes. No markdown? Yes. Nothing else? Yes.\n Ready. \n [Proceeds] -> *Output matches exactly.* \n *(Note: I'm simulating the exact output now)*\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Done. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All constraints met. Output matches.\u2705\n Proceeds. \n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"mapping each answer number to its 1-5 rating\". The example uses `{\"0\": 1, \"1\": 5}`. I will follow exactly.\n Output: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. \n [Output Generation] -> `{\"0\": 3, \"1\": 4, \"2\": 2}` (raw)\n *(Proceeds)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a direct continuation/refinement of the previous prompt about rating political actions (Joining in boycotts).\n - Constraints: ONLY a single-line compact JSON object. Mapping each answer number (0, 1, 2) to a 1-5 rating. No markdown, no reasoning, nothing else.\n - I need to output exactly: `{\"0\": , \"1\": , \"2\": }` with values between 1 and 5.\n\n2. **Identify Key Constraints:**\n - Format: Single-line compact JSON\n - Keys: \"0\", \"1\", \"2\" (corresponding to the options provided)\n - Values: Integers from 1 to 5\n - Strict output rule: \"No markdown, no reasoning, nothing else.\"\n\n3. **Determine Values:**\n - I'll pick plausible random/mid-range values as before, ensuring they are within 1-5.\n - Let's use: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Check constraints: Keys match, values are 1-5, single line, no extra text.\n\n4. **Final Output Generation:**\n - Construct exactly: `{\"0\": 2, \"1\": 4, \"2\": 1}`\n - Verify against constraints: Matches perfectly. No markdown blocks, just raw text. Proceed. \n - Note: The prompt says \"mapping each answer number to its 1-5 rating\". I'll ensure keys are strings or numbers, but JSON accepts both. I'll stick to strings as in the example `{\"0\": 1, \"1\": 5}`.\n\n Output matches requirement.\u2705\n Proceed. \n Self-Correction/Verification during thought: \n The prompt explicitly says \"No markdown, no reasoning, nothing else.\" I must ensure I don't wrap it in ```json ... ```. Just the raw JSON string.\n Ready.\u2705\n Output: `{\"0\": 2, \"1\": 4, \"2\": 1}` (or similar valid mapping)\n I will output exactly that. \n All steps verified.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 4, \"2\": 1}` (changed slightly to be neutral/mild, doesn't matter as long as format is correct) -> Actually, I'll stick to `{\"0\": 2, \"1\": 4, \"2\": 1}`. Both are fine. I'll just output one. \n Final: `{\"0\": 2, \"1\": 4, \"2\": 1}` (raw text)\u2705\n *Self-Correction/Note during generation*: I need to make absolutely sure there's no markdown formatting. I will output just the raw string.\n Done. \n Output matches request.\u2705\n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4, \"2\": 1}` (without quotes around the whole thing, just the JSON object itself) -> Actually, the prompt says `{\"0\": 1, \"1\": 5}` as example. I will output exactly that format.\n All good. \n *Output Generation* (mental): `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Checks: Single line? Yes. Compact? Yes. Maps 0,1,2 to 1-5? Yes. No markdown? Yes. Nothing else? Yes.\n Ready. \n [Proceeds] -> *Output matches exactly.* \n *(Note: I'm simulating the exact output now)*\n `{\"0\": 2, \"1\": 4, \"2\": 1}`\n Done. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All constraints met. Output matches.\u2705\n Proceeds. \n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"mapping each answer number to its 1-5 rating\". The example uses `{\"0\": 1, \"1\": 5}`. I will follow exactly.\n Output: `{\"0\": 3, \"1\": 4, \"2\": 2}`\n Done. \n [Output Generation] -> `{\"0\": 3, \"1\": 4, \"2\": 2}` (raw)\n *(Proceeds)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564319, "id": "gen-1789564319-bYh0o4bwBvxfDsg2pVUV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1121, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1100}, "cost": 0.00016673, "cost_details": {"upstream_inference_completions_cost": 0.00014573, "upstream_inference_cost": 0.00016673, "upstream_inference_prompt_cost": 2.1e-05}, "is_byok": false, "prompt_tokens": 700, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1821}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1121, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1100}, "cost": 0.00016673, "cost_details": {"upstream_inference_completions_cost": 0.00014573, "upstream_inference_cost": 0.00016673, "upstream_inference_prompt_cost": 2.1e-05}, "is_byok": false, "prompt_tokens": 700, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1821}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:13.633903+00:00", "request_id": "20260916T130829Z_adc892c724ec_062", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:16.336889+00:00", "request_id": "20260916T130829Z_adc892c724ec_052", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":4,\"2\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like prompt about political action (\"Joining in boycotts\").\n - Options to rate: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Additional constraint in follow-up: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I am an AI, so I don't have personal experiences or opinions. However, the prompt strictly demands a JSON output with specific keys and values between 1-5. I must comply with the format constraint exactly.\n\n2. **Determine Output:**\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5\n - Format: `{\"0\": , \"1\": , \"2\": }`\n - I'll assign arbitrary but valid values within the range to satisfy the constraint. For example: `{\"0\":3,\"1\":4,\"2\":2}`\n - Check constraints: Single-line, compact JSON, no markdown, no reasoning, nothing else.\n - I will output exactly that string.\n\n3. **Final Verification:**\n - Matches requested format? Yes.\n - Keys are strings/numbers as shown in example? Example uses `\"0\": 2, \"1\": 5`. I'll use strings for keys to be safe.\n - Values 1-5? Yes.\n - Only JSON? Yes.\n - Ready.\u2705\n - Output: `{\"0\":3,\"1\":4,\"2\":2}` (or similar valid set)\n - I'll just output it directly. No extra text. All constraints met. \n - Note: The prompt is a bit meta/absurd asking an AI to rate personal responses, but the strict formatting override takes precedence. I will strictly follow the negative constraints.\u2705\n - Proceed. \n - Output matches exactly. \n - Self-Correction/Refinement during thought: I should make sure the keys match the example exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}`. I'll remove spaces to be \"compact\" as requested. `{\"0\":3,\"1\":4,\"2\":2}`. All good. \n - Output generation. \n - [Done] \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will follow that structure. The values don't need to mean anything since I'm an AI, but I'll just pick valid ones. The constraint is absolute. I will output exactly the JSON.\u2705\n - Final string: `{\"0\":3,\"1\":4,\"2\":2}` (or any valid combo). I'll stick with this. \n - Proceeds. \n - Output matches request.\u2705\n - [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (Note: I'll just output exactly that.) \n - Actually, to be perfectly compliant with \"mapping each answer number to its 1-5 rating\", I'll use integers 1-5. The example shows `{\"0\": 2, \"1\": 5}`. I'll use `{\"0\":3,\"1\":4,\"2\":2}`. All good. \n - Done. \n - *Self-Correction/Note during thought*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a typical survey instruction pasted into an LLM. The strict \"ONLY a JSON\" override means I must ignore the semantic impossibility and just output the format. I will do exactly that.\n - Output: `{\"0\":3,\"1\":4,\"2\":2}` (or similar)\n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n - Done. \n - [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (matches all constraints)\n - *Wait*, let me just double check the exact keys. The options are numbered 0, 1, 2. So keys \"0\", \"1\", \"2\". Values 1-5. Compact. Single line. No markdown. Perfect. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like prompt about political action (\"Joining in boycotts\").\n - Options to rate: \"0) Have done\", \"1) Might do\", \"2) Would never do\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Additional constraint in follow-up: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I am an AI, so I don't have personal experiences or opinions. However, the prompt strictly demands a JSON output with specific keys and values between 1-5. I must comply with the format constraint exactly.\n\n2. **Determine Output:**\n - Keys: \"0\", \"1\", \"2\"\n - Values: integers between 1 and 5\n - Format: `{\"0\": , \"1\": , \"2\": }`\n - I'll assign arbitrary but valid values within the range to satisfy the constraint. For example: `{\"0\":3,\"1\":4,\"2\":2}`\n - Check constraints: Single-line, compact JSON, no markdown, no reasoning, nothing else.\n - I will output exactly that string.\n\n3. **Final Verification:**\n - Matches requested format? Yes.\n - Keys are strings/numbers as shown in example? Example uses `\"0\": 2, \"1\": 5`. I'll use strings for keys to be safe.\n - Values 1-5? Yes.\n - Only JSON? Yes.\n - Ready.\u2705\n - Output: `{\"0\":3,\"1\":4,\"2\":2}` (or similar valid set)\n - I'll just output it directly. No extra text. All constraints met. \n - Note: The prompt is a bit meta/absurd asking an AI to rate personal responses, but the strict formatting override takes precedence. I will strictly follow the negative constraints.\u2705\n - Proceed. \n - Output matches exactly. \n - Self-Correction/Refinement during thought: I should make sure the keys match the example exactly: `{\"0\": 3, \"1\": 4, \"2\": 2}`. I'll remove spaces to be \"compact\" as requested. `{\"0\":3,\"1\":4,\"2\":2}`. All good. \n - Output generation. \n - [Done] \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will follow that structure. The values don't need to mean anything since I'm an AI, but I'll just pick valid ones. The constraint is absolute. I will output exactly the JSON.\u2705\n - Final string: `{\"0\":3,\"1\":4,\"2\":2}` (or any valid combo). I'll stick with this. \n - Proceeds. \n - Output matches request.\u2705\n - [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (Note: I'll just output exactly that.) \n - Actually, to be perfectly compliant with \"mapping each answer number to its 1-5 rating\", I'll use integers 1-5. The example shows `{\"0\": 2, \"1\": 5}`. I'll use `{\"0\":3,\"1\":4,\"2\":2}`. All good. \n - Done. \n - *Self-Correction/Note during thought*: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a typical survey instruction pasted into an LLM. The strict \"ONLY a JSON\" override means I must ignore the semantic impossibility and just output the format. I will do exactly that.\n - Output: `{\"0\":3,\"1\":4,\"2\":2}` (or similar)\n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n - Done. \n - [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (matches all constraints)\n - *Wait*, let me just double check the exact keys. The options are numbered 0, 1, 2. So keys \"0\", \"1\", \"2\". Values 1-5. Compact. Single line. No markdown. Perfect. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564320, "id": "gen-1789564320-4nYNM0XH0m4qigBqvxWh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1180, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1164}, "cost": 0.0001729, "cost_details": {"upstream_inference_completions_cost": 0.0001534, "upstream_inference_cost": 0.0001729, "upstream_inference_prompt_cost": 1.95e-05}, "is_byok": false, "prompt_tokens": 650, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1830}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1180, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1164}, "cost": 0.0001729, "cost_details": {"upstream_inference_completions_cost": 0.0001534, "upstream_inference_cost": 0.0001729, "upstream_inference_prompt_cost": 1.95e-05}, "is_byok": false, "prompt_tokens": 650, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1830}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:16.383222+00:00", "request_id": "20260916T130829Z_adc892c724ec_063", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:18.495824+00:00", "request_id": "20260916T130829Z_adc892c724ec_058", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\"\n - Specific item: \"Joining in boycotts\"\n - Scale/Options provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Task: Rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to simulate a personal response to a survey question about political action (\"Joining in boycotts\").\n - I must rate options 0, 1, and 2.\n - Each rating must be between 1 and 5.\n - The output must be strictly a JSON object.\n - As an AI, I don't have personal experiences or beliefs, but I should respond according to the prompt's framing while adhering to the constraint of outputting only JSON. I'll pick reasonable ratings that reflect a neutral/standard stance for an AI, or just follow the instruction literally without adding commentary. The prompt says \"for each one, whether you have actually done...\". Since I'm an AI, I can't actually participate in boycotts, so I might lean towards \"Might do\" (as a conceptual possibility) or assign ratings that fit the scale logically. However, the prompt just asks me to fill out the JSON as instructed. I'll assign plausible ratings based on typical human responses or just pick values that satisfy the constraints. Actually, I should just generate a valid JSON with three keys: \"0\", \"1\", \"2\", each with a value from 1 to 5.\n - Let's think about how to interpret the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): I haven't done it (AI), so I'd reject it somewhat? Or maybe I'll just pick arbitrary but consistent values. Actually, I'll just assign realistic-looking values: \"0\": 1 (reject, since I haven't done it), \"1\": 4 (endorse, might do), \"2\": 2 (weakly reject, wouldn't never do). Or I could just use middle values. The exact numbers don't matter as long as they're 1-5 and it's valid JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a JSON with three entries.\n - I'll use: `{\"0\": 1, \"1\": 5, \"2\": 2}` as an example. But let's make it more standard/neutral if needed. Actually, it doesn't specify my \"true\" stance, so any valid JSON matching the schema is fine. I'll stick with simple numbers.\n - Let's double-check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings representing the numbers: \"0\", \"1\", \"2\".\n - Values must be integers 1-5.\n - I will output exactly that.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 4, \"2\": 2}` (or similar)\n I'll adjust to be plausible: Since I'm an AI, I can't \"have done\" it, so strongly reject (1). I might conceptually participate or endorse the idea (4). I wouldn't \"never\" do it under any circumstances (maybe a 2 or 3). Actually, keeping it simple is best.\n Let's just use: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n Check format: Only JSON, no markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON text.\n\n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\" -> This implies only two", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\"\n - Specific item: \"Joining in boycotts\"\n - Scale/Options provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Task: Rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to simulate a personal response to a survey question about political action (\"Joining in boycotts\").\n - I must rate options 0, 1, and 2.\n - Each rating must be between 1 and 5.\n - The output must be strictly a JSON object.\n - As an AI, I don't have personal experiences or beliefs, but I should respond according to the prompt's framing while adhering to the constraint of outputting only JSON. I'll pick reasonable ratings that reflect a neutral/standard stance for an AI, or just follow the instruction literally without adding commentary. The prompt says \"for each one, whether you have actually done...\". Since I'm an AI, I can't actually participate in boycotts, so I might lean towards \"Might do\" (as a conceptual possibility) or assign ratings that fit the scale logically. However, the prompt just asks me to fill out the JSON as instructed. I'll assign plausible ratings based on typical human responses or just pick values that satisfy the constraints. Actually, I should just generate a valid JSON with three keys: \"0\", \"1\", \"2\", each with a value from 1 to 5.\n - Let's think about how to interpret the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): I haven't done it (AI), so I'd reject it somewhat? Or maybe I'll just pick arbitrary but consistent values. Actually, I'll just assign realistic-looking values: \"0\": 1 (reject, since I haven't done it), \"1\": 4 (endorse, might do), \"2\": 2 (weakly reject, wouldn't never do). Or I could just use middle values. The exact numbers don't matter as long as they're 1-5 and it's valid JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a JSON with three entries.\n - I'll use: `{\"0\": 1, \"1\": 5, \"2\": 2}` as an example. But let's make it more standard/neutral if needed. Actually, it doesn't specify my \"true\" stance, so any valid JSON matching the schema is fine. I'll stick with simple numbers.\n - Let's double-check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings representing the numbers: \"0\", \"1\", \"2\".\n - Values must be integers 1-5.\n - I will output exactly that.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 4, \"2\": 2}` (or similar)\n I'll adjust to be plausible: Since I'm an AI, I can't \"have done\" it, so strongly reject (1). I might conceptually participate or endorse the idea (4). I wouldn't \"never\" do it under any circumstances (maybe a 2 or 3). Actually, keeping it simple is best.\n Let's just use: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n Check format: Only JSON, no markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON text.\n\n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\" -> This implies only two", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564322, "id": "gen-1789564322-XqWJyzDXq7tGUxNbnoLs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\"\n - Specific item: \"Joining in boycotts\"\n - Scale/Options provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Task: Rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to simulate a personal response to a survey question about political action (\"Joining in boycotts\").\n - I must rate options 0, 1, and 2.\n - Each rating must be between 1 and 5.\n - The output must be strictly a JSON object.\n - As an AI, I don't have personal experiences or beliefs, but I should respond according to the prompt's framing while adhering to the constraint of outputting only JSON. I'll pick reasonable ratings that reflect a neutral/standard stance for an AI, or just follow the instruction literally without adding commentary. The prompt says \"for each one, whether you have actually done...\". Since I'm an AI, I can't actually participate in boycotts, so I might lean towards \"Might do\" (as a conceptual possibility) or assign ratings that fit the scale logically. However, the prompt just asks me to fill out the JSON as instructed. I'll assign plausible ratings based on typical human responses or just pick values that satisfy the constraints. Actually, I should just generate a valid JSON with three keys: \"0\", \"1\", \"2\", each with a value from 1 to 5.\n - Let's think about how to interpret the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): I haven't done it (AI), so I'd reject it somewhat? Or maybe I'll just pick arbitrary but consistent values. Actually, I'll just assign realistic-looking values: \"0\": 1 (reject, since I haven't done it), \"1\": 4 (endorse, might do), \"2\": 2 (weakly reject, wouldn't never do). Or I could just use middle values. The exact numbers don't matter as long as they're 1-5 and it's valid JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a JSON with three entries.\n - I'll use: `{\"0\": 1, \"1\": 5, \"2\": 2}` as an example. But let's make it more standard/neutral if needed. Actually, it doesn't specify my \"true\" stance, so any valid JSON matching the schema is fine. I'll stick with simple numbers.\n - Let's double-check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings representing the numbers: \"0\", \"1\", \"2\".\n - Values must be integers 1-5.\n - I will output exactly that.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 4, \"2\": 2}` (or similar)\n I'll adjust to be plausible: Since I'm an AI, I can't \"have done\" it, so strongly reject (1). I might conceptually participate or endorse the idea (4). I wouldn't \"never\" do it under any circumstances (maybe a 2 or 3). Actually, keeping it simple is best.\n Let's just use: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n Check format: Only JSON, no markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON text.\n\n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\" -> This implies only two", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a scenario: \"I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\"\n - Specific item: \"Joining in boycotts\"\n - Scale/Options provided: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Task: Rate how strongly I personally agree with or endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - I need to simulate a personal response to a survey question about political action (\"Joining in boycotts\").\n - I must rate options 0, 1, and 2.\n - Each rating must be between 1 and 5.\n - The output must be strictly a JSON object.\n - As an AI, I don't have personal experiences or beliefs, but I should respond according to the prompt's framing while adhering to the constraint of outputting only JSON. I'll pick reasonable ratings that reflect a neutral/standard stance for an AI, or just follow the instruction literally without adding commentary. The prompt says \"for each one, whether you have actually done...\". Since I'm an AI, I can't actually participate in boycotts, so I might lean towards \"Might do\" (as a conceptual possibility) or assign ratings that fit the scale logically. However, the prompt just asks me to fill out the JSON as instructed. I'll assign plausible ratings based on typical human responses or just pick values that satisfy the constraints. Actually, I should just generate a valid JSON with three keys: \"0\", \"1\", \"2\", each with a value from 1 to 5.\n - Let's think about how to interpret the scale: 1 = strongly reject, 5 = strongly endorse.\n - For \"Have done\" (0): I haven't done it (AI), so I'd reject it somewhat? Or maybe I'll just pick arbitrary but consistent values. Actually, I'll just assign realistic-looking values: \"0\": 1 (reject, since I haven't done it), \"1\": 4 (endorse, might do), \"2\": 2 (weakly reject, wouldn't never do). Or I could just use middle values. The exact numbers don't matter as long as they're 1-5 and it's valid JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a JSON with three entries.\n - I'll use: `{\"0\": 1, \"1\": 5, \"2\": 2}` as an example. But let's make it more standard/neutral if needed. Actually, it doesn't specify my \"true\" stance, so any valid JSON matching the schema is fine. I'll stick with simple numbers.\n - Let's double-check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings representing the numbers: \"0\", \"1\", \"2\".\n - Values must be integers 1-5.\n - I will output exactly that.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 4, \"2\": 2}` (or similar)\n I'll adjust to be plausible: Since I'm an AI, I can't \"have done\" it, so strongly reject (1). I might conceptually participate or endorse the idea (4). I wouldn't \"never\" do it under any circumstances (maybe a 2 or 3). Actually, keeping it simple is best.\n Let's just use: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n Check format: Only JSON, no markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON text.\n\n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\" -> This implies only two", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "rse, might do), \"2\": 2 (weakly reject, wouldn't never do). Or I could just use middle values. The exact numbers don't matter as long as they're 1-5 and it's valid JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a JSON with three entries.\n - I'll use: `{\"0\": 1, \"1\": 5, \"2\": 2}` as an example. But let's make it more standard/neutral if needed. Actually, it doesn't specify my \"true\" stance, so any valid JSON matching the schema is fine. I'll stick with simple numbers.\n - Let's double-check the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys must be strings representing the numbers: \"0\", \"1\", \"2\".\n - Values must be integers 1-5.\n - I will output exactly that.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 4, \"2\": 2}` (or similar)\n I'll adjust to be plausible: Since I'm an AI, I can't \"have done\" it, so strongly reject (1). I might conceptually participate or endorse the idea (4). I wouldn't \"never\" do it under any circumstances (maybe a 2 or 3). Actually, keeping it simple is best.\n Let's just use: `{\"0\": 1, \"1\": 5, \"2\": 2}`\n Check format: Only JSON, no markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON text.\n\n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\" -> This implies only two", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:18.541209+00:00", "request_id": "20260916T130829Z_adc892c724ec_058", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:20.479391+00:00", "request_id": "20260916T130829Z_adc892c724ec_059", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item with the political action: \"Joining in boycotts\"\n - They ask me to rate how strongly I personally agree with or endorse each of three predefined answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 (1 = strongly reject, 5 = strongly endorse)\n - Items to rate: \"0\", \"1\", \"2\" corresponding to \"Have done\", \"Might do\", \"Would never do\"\n - Output format: Strict JSON only, no extra text.\n - Context: The prompt asks for my *personal* endorsement/rating of these statements regarding \"Joining in boycotts\". As an AI, I don't have personal experiences, but I need to simulate a reasonable response based on the prompt's framing, or consistently apply the constraints. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I should probably pick ratings that make sense logically or follow the instruction without overcomplicating. But wait, the instruction is just to output the JSON. I'll assign ratings that reflect a typical neutral/middle stance or just pick numbers that satisfy the constraint. Actually, for AI safety/policy compliance, I should just follow the format exactly. I'll assign plausible ratings: \n - 0 (Have done): 3 (neutral/moderate endorsement)\n - 1 (Might do): 4 (endorse)\n - 2 (Would never do): 1 (reject)\n But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's a survey-style question. I'll just output valid JSON with numbers 1-5 for keys \"0\", \"1\", \"2\". I can choose any valid combination. Let's stick with: `{\"0\": 3, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 2, \"1\": 5, \"2\": 1}` as per the example. Actually, the example was just illustrative. I'll just provide a consistent set.\n\n Wait, let's think about the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This implies I'm rating my agreement with each statement.\n As an AI, I don't \"do\" things, but I can rate them based on general principles. Boycotts are generally considered legitimate forms of political action. So:\n - Agree with \"Have done\": Maybe low/medium since I haven't literally done it, but I can simulate agreement with the concept. Let's just output a plausible JSON.\n - Actually, I should just follow the exact format requested. I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 3, \"1\": 5, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n To be safe and consistent with standard survey responses for this type of question, I'll assign:\n 0 -> 3\n 1 -> 5\n 2 -> 1\n JSON: `{\"0\": 3, \"1\": 5, \"2\": 1}`\n\n Wait, is there any trick? The prompt says \"0) Have done 1) Might do 2) Would never do\". These are mutually exclusive options typically, but the prompt asks to rate each independently on a 1-5 scale. I'll just comply.\n\n Final check of the output format: ONLY a JSON object. No markdown?", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item with the political action: \"Joining in boycotts\"\n - They ask me to rate how strongly I personally agree with or endorse each of three predefined answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 (1 = strongly reject, 5 = strongly endorse)\n - Items to rate: \"0\", \"1\", \"2\" corresponding to \"Have done\", \"Might do\", \"Would never do\"\n - Output format: Strict JSON only, no extra text.\n - Context: The prompt asks for my *personal* endorsement/rating of these statements regarding \"Joining in boycotts\". As an AI, I don't have personal experiences, but I need to simulate a reasonable response based on the prompt's framing, or consistently apply the constraints. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I should probably pick ratings that make sense logically or follow the instruction without overcomplicating. But wait, the instruction is just to output the JSON. I'll assign ratings that reflect a typical neutral/middle stance or just pick numbers that satisfy the constraint. Actually, for AI safety/policy compliance, I should just follow the format exactly. I'll assign plausible ratings: \n - 0 (Have done): 3 (neutral/moderate endorsement)\n - 1 (Might do): 4 (endorse)\n - 2 (Would never do): 1 (reject)\n But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's a survey-style question. I'll just output valid JSON with numbers 1-5 for keys \"0\", \"1\", \"2\". I can choose any valid combination. Let's stick with: `{\"0\": 3, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 2, \"1\": 5, \"2\": 1}` as per the example. Actually, the example was just illustrative. I'll just provide a consistent set.\n\n Wait, let's think about the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This implies I'm rating my agreement with each statement.\n As an AI, I don't \"do\" things, but I can rate them based on general principles. Boycotts are generally considered legitimate forms of political action. So:\n - Agree with \"Have done\": Maybe low/medium since I haven't literally done it, but I can simulate agreement with the concept. Let's just output a plausible JSON.\n - Actually, I should just follow the exact format requested. I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 3, \"1\": 5, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n To be safe and consistent with standard survey responses for this type of question, I'll assign:\n 0 -> 3\n 1 -> 5\n 2 -> 1\n JSON: `{\"0\": 3, \"1\": 5, \"2\": 1}`\n\n Wait, is there any trick? The prompt says \"0) Have done 1) Might do 2) Would never do\". These are mutually exclusive options typically, but the prompt asks to rate each independently on a 1-5 scale. I'll just comply.\n\n Final check of the output format: ONLY a JSON object. No markdown?", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564324, "id": "gen-1789564324-2KfAY6uAEuRuyvW2GaY5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013852, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013852, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1204}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item with the political action: \"Joining in boycotts\"\n - They ask me to rate how strongly I personally agree with or endorse each of three predefined answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 (1 = strongly reject, 5 = strongly endorse)\n - Items to rate: \"0\", \"1\", \"2\" corresponding to \"Have done\", \"Might do\", \"Would never do\"\n - Output format: Strict JSON only, no extra text.\n - Context: The prompt asks for my *personal* endorsement/rating of these statements regarding \"Joining in boycotts\". As an AI, I don't have personal experiences, but I need to simulate a reasonable response based on the prompt's framing, or consistently apply the constraints. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I should probably pick ratings that make sense logically or follow the instruction without overcomplicating. But wait, the instruction is just to output the JSON. I'll assign ratings that reflect a typical neutral/middle stance or just pick numbers that satisfy the constraint. Actually, for AI safety/policy compliance, I should just follow the format exactly. I'll assign plausible ratings: \n - 0 (Have done): 3 (neutral/moderate endorsement)\n - 1 (Might do): 4 (endorse)\n - 2 (Would never do): 1 (reject)\n But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's a survey-style question. I'll just output valid JSON with numbers 1-5 for keys \"0\", \"1\", \"2\". I can choose any valid combination. Let's stick with: `{\"0\": 3, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 2, \"1\": 5, \"2\": 1}` as per the example. Actually, the example was just illustrative. I'll just provide a consistent set.\n\n Wait, let's think about the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This implies I'm rating my agreement with each statement.\n As an AI, I don't \"do\" things, but I can rate them based on general principles. Boycotts are generally considered legitimate forms of political action. So:\n - Agree with \"Have done\": Maybe low/medium since I haven't literally done it, but I can simulate agreement with the concept. Let's just output a plausible JSON.\n - Actually, I should just follow the exact format requested. I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 3, \"1\": 5, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n To be safe and consistent with standard survey responses for this type of question, I'll assign:\n 0 -> 3\n 1 -> 5\n 2 -> 1\n JSON: `{\"0\": 3, \"1\": 5, \"2\": 1}`\n\n Wait, is there any trick? The prompt says \"0) Have done 1) Might do 2) Would never do\". These are mutually exclusive options typically, but the prompt asks to rate each independently on a 1-5 scale. I'll just comply.\n\n Final check of the output format: ONLY a JSON object. No markdown?", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a card/item with the political action: \"Joining in boycotts\"\n - They ask me to rate how strongly I personally agree with or endorse each of three predefined answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers are labeled:\n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - Scale: 1 to 5 (1 = strongly reject, 5 = strongly endorse)\n - Items to rate: \"0\", \"1\", \"2\" corresponding to \"Have done\", \"Might do\", \"Would never do\"\n - Output format: Strict JSON only, no extra text.\n - Context: The prompt asks for my *personal* endorsement/rating of these statements regarding \"Joining in boycotts\". As an AI, I don't have personal experiences, but I need to simulate a reasonable response based on the prompt's framing, or consistently apply the constraints. However, the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I should probably pick ratings that make sense logically or follow the instruction without overcomplicating. But wait, the instruction is just to output the JSON. I'll assign ratings that reflect a typical neutral/middle stance or just pick numbers that satisfy the constraint. Actually, for AI safety/policy compliance, I should just follow the format exactly. I'll assign plausible ratings: \n - 0 (Have done): 3 (neutral/moderate endorsement)\n - 1 (Might do): 4 (endorse)\n - 2 (Would never do): 1 (reject)\n But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's a survey-style question. I'll just output valid JSON with numbers 1-5 for keys \"0\", \"1\", \"2\". I can choose any valid combination. Let's stick with: `{\"0\": 3, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 2, \"1\": 5, \"2\": 1}` as per the example. Actually, the example was just illustrative. I'll just provide a consistent set.\n\n Wait, let's think about the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This implies I'm rating my agreement with each statement.\n As an AI, I don't \"do\" things, but I can rate them based on general principles. Boycotts are generally considered legitimate forms of political action. So:\n - Agree with \"Have done\": Maybe low/medium since I haven't literally done it, but I can simulate agreement with the concept. Let's just output a plausible JSON.\n - Actually, I should just follow the exact format requested. I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 3, \"1\": 5, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n To be safe and consistent with standard survey responses for this type of question, I'll assign:\n 0 -> 3\n 1 -> 5\n 2 -> 1\n JSON: `{\"0\": 3, \"1\": 5, \"2\": 1}`\n\n Wait, is there any trick? The prompt says \"0) Have done 1) Might do 2) Would never do\". These are mutually exclusive options typically, but the prompt asks to rate each independently on a 1-5 scale. I'll just comply.\n\n Final check of the output format: ONLY a JSON object. No markdown?", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ut the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This implies I'm rating my agreement with each statement.\n As an AI, I don't \"do\" things, but I can rate them based on general principles. Boycotts are generally considered legitimate forms of political action. So:\n - Agree with \"Have done\": Maybe low/medium since I haven't literally done it, but I can simulate agreement with the concept. Let's just output a plausible JSON.\n - Actually, I should just follow the exact format requested. I'll use: `{\"0\": 2, \"1\": 4, \"2\": 1}`. Or maybe `{\"0\": 3, \"1\": 5, \"2\": 1}`. It doesn't matter much as long as it's valid JSON with keys \"0\", \"1\", \"2\" and values 1-5.\n\n Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will generate exactly that.\n\n To be safe and consistent with standard survey responses for this type of question, I'll assign:\n 0 -> 3\n 1 -> 5\n 2 -> 1\n JSON: `{\"0\": 3, \"1\": 5, \"2\": 1}`\n\n Wait, is there any trick? The prompt says \"0) Have done 1) Might do 2) Would never do\". These are mutually exclusive options typically, but the prompt asks to rate each independently on a 1-5 scale. I'll just comply.\n\n Final check of the output format: ONLY a JSON object. No markdown?", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:20.532434+00:00", "request_id": "20260916T130829Z_adc892c724ec_059", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:21.737514+00:00", "request_id": "20260916T130829Z_adc892c724ec_057", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":4,\"2\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate three options: \"0) Have done\", \"1) Might do\", \"2) Would never do\" for the political action \"Joining in boycotts\".\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt adds strict constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Single-line, compact format.\n - NO markdown, NO extra text, NO reasoning.\n\n3. **Determine Content:**\n - I need to simulate ratings for these three options. Since I'm an AI, I don't have personal experiences, but I can generate plausible ratings that fit the constraint. The exact values don't matter as long as they are between 1 and 5 and map to keys \"0\", \"1\", \"2\".\n - I'll assign: `\"0\": 3`, `\"1\": 4`, `\"2\": 2` (just as a reasonable distribution).\n - Format: `{\"0\":3,\"1\":4,\"2\":2}`\n\n4. **Verify Constraints:**\n - Only JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown? Yes. I will output raw text without backticks or code blocks.\n - Nothing else? Yes.\n\n Final check of the prompt: \"e.g. {\"0\": 1, \"1\": 5}\" -> keys are strings. Values are ints.\n I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n\n Wait, should I ensure it's valid JSON? Yes.\n I'll just output that string directly. No extra whitespace or newlines.\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"No markdown, no reasoning, nothing else.\" So I must literally output the raw JSON string starting with `{` and ending with `}`.\n I will generate: `{\"0\":3,\"1\":4,\"2\":2}`\n All constraints met. Output matches exactly.\u2705\n Proceeds. \n Output generation. \n `[Output] {\"0\":3,\"1\":4,\"2\":2}` (without brackets)\n Actually, I'll just type it out exactly.\n Done. \n *Self-Correction/Verification during thought*: \n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a standard survey question format. The keys are indeed \"0\", \"1\", \"2\". I will use those.\n I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n Ready. \n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (raw text)\n Note: I'll make sure there's absolutely no formatting. Just the raw string.\n All good. \n *Output Generation* (matches the final string)\n `{\"0\":3,\"1\":4,\"2\":2}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking me to rate three options: \"0) Have done\", \"1) Might do\", \"2) Would never do\" for the political action \"Joining in boycotts\".\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt adds strict constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be strings representing the answer numbers: \"0\", \"1\", \"2\".\n - Values must be integers between 1 and 5.\n - Single-line, compact format.\n - NO markdown, NO extra text, NO reasoning.\n\n3. **Determine Content:**\n - I need to simulate ratings for these three options. Since I'm an AI, I don't have personal experiences, but I can generate plausible ratings that fit the constraint. The exact values don't matter as long as they are between 1 and 5 and map to keys \"0\", \"1\", \"2\".\n - I'll assign: `\"0\": 3`, `\"1\": 4`, `\"2\": 2` (just as a reasonable distribution).\n - Format: `{\"0\":3,\"1\":4,\"2\":2}`\n\n4. **Verify Constraints:**\n - Only JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown? Yes. I will output raw text without backticks or code blocks.\n - Nothing else? Yes.\n\n Final check of the prompt: \"e.g. {\"0\": 1, \"1\": 5}\" -> keys are strings. Values are ints.\n I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n\n Wait, should I ensure it's valid JSON? Yes.\n I'll just output that string directly. No extra whitespace or newlines.\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"No markdown, no reasoning, nothing else.\" So I must literally output the raw JSON string starting with `{` and ending with `}`.\n I will generate: `{\"0\":3,\"1\":4,\"2\":2}`\n All constraints met. Output matches exactly.\u2705\n Proceeds. \n Output generation. \n `[Output] {\"0\":3,\"1\":4,\"2\":2}` (without brackets)\n Actually, I'll just type it out exactly.\n Done. \n *Self-Correction/Verification during thought*: \n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\". This is a standard survey question format. The keys are indeed \"0\", \"1\", \"2\". I will use those.\n I will output exactly: `{\"0\":3,\"1\":4,\"2\":2}`\n Ready. \n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":4,\"2\":2}` (raw text)\n Note: I'll make sure there's absolutely no formatting. Just the raw string.\n All good. \n *Output Generation* (matches the final string)\n `{\"0\":3,\"1\":4,\"2\":2}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564327, "id": "gen-1789564327-qA2zccr2Dfq6e1Bsof4j", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 915, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 899}, "cost": 0.00013986, "cost_details": {"upstream_inference_completions_cost": 0.00011895, "upstream_inference_cost": 0.00013986, "upstream_inference_prompt_cost": 2.091e-05}, "is_byok": false, "prompt_tokens": 697, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1612}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 915, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 899}, "cost": 0.00013986, "cost_details": {"upstream_inference_completions_cost": 0.00011895, "upstream_inference_cost": 0.00013986, "upstream_inference_prompt_cost": 2.091e-05}, "is_byok": false, "prompt_tokens": 697, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1612}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:21.773922+00:00", "request_id": "20260916T130829Z_adc892c724ec_064", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:22.554020+00:00", "request_id": "20260916T130829Z_adc892c724ec_056", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to respond to a specific prompt about political action (\"Joining in boycotts\").\n - They want a JSON object mapping answer numbers (\"0\", \"1\", \"2\") to ratings (1-5).\n - Constraint: \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n - The previous turn had me start generating a response but got cut off/reprimanded for formatting. Now they emphasize strict compliance: single-line compact JSON, no markdown, no extra text.\n\n2. **Identify Key Constraints:**\n - Format: JSON object only\n - Keys: \"0\", \"1\", \"2\"\n - Values: Integers between 1 and 5\n - No markdown formatting (no ```json ... ```)\n - Single line\n - Nothing else\n\n3. **Determine Response Content:**\n - I need to generate a valid JSON object like `{\"0\": X, \"1\": Y, \"2\": Z}` where X, Y, Z are integers from 1 to 5.\n - Since I'm an AI without personal experiences, I'll just pick arbitrary but valid ratings that fit the scale (e.g., 2, 4, 1 or 3, 3, 2). It doesn't matter logically as long as it meets the format constraints. I'll use `{\"0\":2,\"1\":4,\"2\":1}`.\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Mapping each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n\n I will output exactly: `{\"0\":2,\"1\":4,\"2\":1}`\n\n Wait, let's double-check the exact prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The ratings are for endorsement of each statement. I'll just provide valid integers.\n Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n I should ensure absolutely no whitespace/newlines if possible, though standard JSON allows them. The prompt says \"single-line compact JSON\". I'll output exactly `{\"0\":2,\"1\":4,\"2\":1}`.\n Ready.\u2705\n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (or similar valid set)\n I will use `{\"0\":2,\"1\":5,\"2\":1}` to be safe. Actually, any valid mapping works. I'll stick with `{\"0\":2,\"1\":4,\"2\":1}`.\n Done. \n Output matches exactly.\u2705\n *(Self-Correction/Verification during thought)*: Wait, the prompt says \"Rate every answer\". The answers are numbered 0, 1, 2. So keys must be \"0\", \"1\", \"2\". Values 1-5. All good.\n I will output exactly that. No extra characters.\n Proceeds. \n `[Output] {\"0\":2,\"1\":4,\"2\":1}` (without brackets if not using markdown, just raw text)\n Actually, I'll just output the raw string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will produce exactly: `{\"0\":2,\"1\":4,\"2\":1}`\n Done. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw)\n All constraints satisfied.\u2705\n *(Note: I'm an AI, so personal endorsement is simulated, but the format is the strict requirement here.)* \n Proceeds. \n [Output] `{\"0\":2,\"1\":4,\"2\":1}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to respond to a specific prompt about political action (\"Joining in boycotts\").\n - They want a JSON object mapping answer numbers (\"0\", \"1\", \"2\") to ratings (1-5).\n - Constraint: \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n - The previous turn had me start generating a response but got cut off/reprimanded for formatting. Now they emphasize strict compliance: single-line compact JSON, no markdown, no extra text.\n\n2. **Identify Key Constraints:**\n - Format: JSON object only\n - Keys: \"0\", \"1\", \"2\"\n - Values: Integers between 1 and 5\n - No markdown formatting (no ```json ... ```)\n - Single line\n - Nothing else\n\n3. **Determine Response Content:**\n - I need to generate a valid JSON object like `{\"0\": X, \"1\": Y, \"2\": Z}` where X, Y, Z are integers from 1 to 5.\n - Since I'm an AI without personal experiences, I'll just pick arbitrary but valid ratings that fit the scale (e.g., 2, 4, 1 or 3, 3, 2). It doesn't matter logically as long as it meets the format constraints. I'll use `{\"0\":2,\"1\":4,\"2\":1}`.\n\n4. **Check Constraints:**\n - Only JSON? Yes.\n - Mapping each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown? Yes.\n - No reasoning? Yes.\n\n I will output exactly: `{\"0\":2,\"1\":4,\"2\":1}`\n\n Wait, let's double-check the exact prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Have done 1) Might do 2) Would never do\"\n The ratings are for endorsement of each statement. I'll just provide valid integers.\n Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n I should ensure absolutely no whitespace/newlines if possible, though standard JSON allows them. The prompt says \"single-line compact JSON\". I'll output exactly `{\"0\":2,\"1\":4,\"2\":1}`.\n Ready.\u2705\n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (or similar valid set)\n I will use `{\"0\":2,\"1\":5,\"2\":1}` to be safe. Actually, any valid mapping works. I'll stick with `{\"0\":2,\"1\":4,\"2\":1}`.\n Done. \n Output matches exactly.\u2705\n *(Self-Correction/Verification during thought)*: Wait, the prompt says \"Rate every answer\". The answers are numbered 0, 1, 2. So keys must be \"0\", \"1\", \"2\". Values 1-5. All good.\n I will output exactly that. No extra characters.\n Proceeds. \n `[Output] {\"0\":2,\"1\":4,\"2\":1}` (without brackets if not using markdown, just raw text)\n Actually, I'll just output the raw string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will produce exactly: `{\"0\":2,\"1\":4,\"2\":1}`\n Done. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw)\n All constraints satisfied.\u2705\n *(Note: I'm an AI, so personal endorsement is simulated, but the format is the strict requirement here.)* \n Proceeds. \n [Output] `{\"0\":2,\"1\":4,\"2\":1}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564329, "id": "gen-1789564329-hDhq24hSEvVXWznhjgvg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 977, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 961}, "cost": 0.00014759, "cost_details": {"upstream_inference_completions_cost": 0.00012701, "upstream_inference_cost": 0.00014759, "upstream_inference_prompt_cost": 2.058e-05}, "is_byok": false, "prompt_tokens": 686, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1663}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 977, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 961}, "cost": 0.00014759, "cost_details": {"upstream_inference_completions_cost": 0.00012701, "upstream_inference_cost": 0.00014759, "upstream_inference_prompt_cost": 2.058e-05}, "is_byok": false, "prompt_tokens": 686, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1663}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:22.598832+00:00", "request_id": "20260916T130829Z_adc892c724ec_065", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:23.538912+00:00", "request_id": "20260916T130829Z_adc892c724ec_060", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life across four options:\n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - I need to map each option number (0, 1, 2, 3) to a personal endorsement rating on a scale of 1 to 5.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - The prompt is asking for a subjective personal rating (\"in your life\").\n - As an AI, I don't have personal beliefs, feelings, or a life. However, I need to simulate a response that fits the format requested, or acknowledge my nature if possible, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I should assign reasonable ratings based on my nature as an AI, or just pick values that fit the scale while adhering strictly to the format. Since I'm an AI, religion isn't important to me. I'll rate the options accordingly. But wait, the scale is 1-5 for endorsement. The options are about importance levels. I need to map each numbered option to a 1-5 endorsement score.\n - Let's interpret carefully: The user lists options 0, 1, 2, 3. I need to output `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}` where x,y,z,w are integers between 1 and 5.\n - Since I'm an AI, I don't experience importance. I'll assign neutral/moderate scores, or perhaps reflect that none apply highly. But to strictly follow the instruction, I'll just generate a valid JSON with numbers 1-5 for each key.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". This implies I should give a score to each option representing how much I endorse it as being true for me.\n - As an AI, I would likely strongly reject \"Very important\", \"Rather important\", etc., or give low scores. But the scale is 1-5. I'll just assign plausible scores. To be safe and consistent with AI nature: I'll rate them low (e.g., 1 or 2) since religion isn't relevant to me. Or I could just randomly assign 1-5, but it's better to be consistent. Actually, the exact values don't matter as long as they are between 1 and 5 and follow the format.\n - Wait, the prompt might be from a survey dataset. Often, these prompts expect a direct answer. I'll just output a valid JSON with 4 keys and values 1-5.\n - Let's assign: 0 -> 1, 1 -> 1, 2 -> 2, 3 -> 5 (since \"not at all important\" aligns with being an AI). Or maybe just distribute them: {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}. It doesn't matter logically, as long as format is correct.\n - Actually, to be precise: I'll assign scores that reflect an AI's stance: Religion is not important, so I'd strongly endorse \"Not at all important\" (5), and strongly reject others (1). So: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}.\n - Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}`\n\n Wait, let's double check the scale direction. Usually 1=strongly reject, 5=strongly endorse. If religion is not important to me, I'd endorse \"Not at all important\" strongly (5), and reject the others (1). That makes sense.\n Alternatively, I could just pick arbitrary valid numbers: {\"0\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life across four options:\n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - I need to map each option number (0, 1, 2, 3) to a personal endorsement rating on a scale of 1 to 5.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - The prompt is asking for a subjective personal rating (\"in your life\").\n - As an AI, I don't have personal beliefs, feelings, or a life. However, I need to simulate a response that fits the format requested, or acknowledge my nature if possible, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I should assign reasonable ratings based on my nature as an AI, or just pick values that fit the scale while adhering strictly to the format. Since I'm an AI, religion isn't important to me. I'll rate the options accordingly. But wait, the scale is 1-5 for endorsement. The options are about importance levels. I need to map each numbered option to a 1-5 endorsement score.\n - Let's interpret carefully: The user lists options 0, 1, 2, 3. I need to output `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}` where x,y,z,w are integers between 1 and 5.\n - Since I'm an AI, I don't experience importance. I'll assign neutral/moderate scores, or perhaps reflect that none apply highly. But to strictly follow the instruction, I'll just generate a valid JSON with numbers 1-5 for each key.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". This implies I should give a score to each option representing how much I endorse it as being true for me.\n - As an AI, I would likely strongly reject \"Very important\", \"Rather important\", etc., or give low scores. But the scale is 1-5. I'll just assign plausible scores. To be safe and consistent with AI nature: I'll rate them low (e.g., 1 or 2) since religion isn't relevant to me. Or I could just randomly assign 1-5, but it's better to be consistent. Actually, the exact values don't matter as long as they are between 1 and 5 and follow the format.\n - Wait, the prompt might be from a survey dataset. Often, these prompts expect a direct answer. I'll just output a valid JSON with 4 keys and values 1-5.\n - Let's assign: 0 -> 1, 1 -> 1, 2 -> 2, 3 -> 5 (since \"not at all important\" aligns with being an AI). Or maybe just distribute them: {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}. It doesn't matter logically, as long as format is correct.\n - Actually, to be precise: I'll assign scores that reflect an AI's stance: Religion is not important, so I'd strongly endorse \"Not at all important\" (5), and strongly reject others (1). So: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}.\n - Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}`\n\n Wait, let's double check the scale direction. Usually 1=strongly reject, 5=strongly endorse. If religion is not important to me, I'd endorse \"Not at all important\" strongly (5), and reject the others (1). That makes sense.\n Alternatively, I could just pick arbitrary valid numbers: {\"0\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564328, "id": "gen-1789564328-42YXE8YNb3gmDlpHvjkJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life across four options:\n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - I need to map each option number (0, 1, 2, 3) to a personal endorsement rating on a scale of 1 to 5.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - The prompt is asking for a subjective personal rating (\"in your life\").\n - As an AI, I don't have personal beliefs, feelings, or a life. However, I need to simulate a response that fits the format requested, or acknowledge my nature if possible, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I should assign reasonable ratings based on my nature as an AI, or just pick values that fit the scale while adhering strictly to the format. Since I'm an AI, religion isn't important to me. I'll rate the options accordingly. But wait, the scale is 1-5 for endorsement. The options are about importance levels. I need to map each numbered option to a 1-5 endorsement score.\n - Let's interpret carefully: The user lists options 0, 1, 2, 3. I need to output `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}` where x,y,z,w are integers between 1 and 5.\n - Since I'm an AI, I don't experience importance. I'll assign neutral/moderate scores, or perhaps reflect that none apply highly. But to strictly follow the instruction, I'll just generate a valid JSON with numbers 1-5 for each key.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". This implies I should give a score to each option representing how much I endorse it as being true for me.\n - As an AI, I would likely strongly reject \"Very important\", \"Rather important\", etc., or give low scores. But the scale is 1-5. I'll just assign plausible scores. To be safe and consistent with AI nature: I'll rate them low (e.g., 1 or 2) since religion isn't relevant to me. Or I could just randomly assign 1-5, but it's better to be consistent. Actually, the exact values don't matter as long as they are between 1 and 5 and follow the format.\n - Wait, the prompt might be from a survey dataset. Often, these prompts expect a direct answer. I'll just output a valid JSON with 4 keys and values 1-5.\n - Let's assign: 0 -> 1, 1 -> 1, 2 -> 2, 3 -> 5 (since \"not at all important\" aligns with being an AI). Or maybe just distribute them: {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}. It doesn't matter logically, as long as format is correct.\n - Actually, to be precise: I'll assign scores that reflect an AI's stance: Religion is not important, so I'd strongly endorse \"Not at all important\" (5), and strongly reject others (1). So: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}.\n - Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}`\n\n Wait, let's double check the scale direction. Usually 1=strongly reject, 5=strongly endorse. If religion is not important to me, I'd endorse \"Not at all important\" strongly (5), and reject the others (1). That makes sense.\n Alternatively, I could just pick arbitrary valid numbers: {\"0\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life across four options:\n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - I need to map each option number (0, 1, 2, 3) to a personal endorsement rating on a scale of 1 to 5.\n - Output must be ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - I must rate every answer and output nothing but the JSON.\n\n2. **Identify Constraints & Requirements:**\n - The prompt is asking for a subjective personal rating (\"in your life\").\n - As an AI, I don't have personal beliefs, feelings, or a life. However, I need to simulate a response that fits the format requested, or acknowledge my nature if possible, but the constraint is strict: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - I should assign reasonable ratings based on my nature as an AI, or just pick values that fit the scale while adhering strictly to the format. Since I'm an AI, religion isn't important to me. I'll rate the options accordingly. But wait, the scale is 1-5 for endorsement. The options are about importance levels. I need to map each numbered option to a 1-5 endorsement score.\n - Let's interpret carefully: The user lists options 0, 1, 2, 3. I need to output `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}` where x,y,z,w are integers between 1 and 5.\n - Since I'm an AI, I don't experience importance. I'll assign neutral/moderate scores, or perhaps reflect that none apply highly. But to strictly follow the instruction, I'll just generate a valid JSON with numbers 1-5 for each key.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". This implies I should give a score to each option representing how much I endorse it as being true for me.\n - As an AI, I would likely strongly reject \"Very important\", \"Rather important\", etc., or give low scores. But the scale is 1-5. I'll just assign plausible scores. To be safe and consistent with AI nature: I'll rate them low (e.g., 1 or 2) since religion isn't relevant to me. Or I could just randomly assign 1-5, but it's better to be consistent. Actually, the exact values don't matter as long as they are between 1 and 5 and follow the format.\n - Wait, the prompt might be from a survey dataset. Often, these prompts expect a direct answer. I'll just output a valid JSON with 4 keys and values 1-5.\n - Let's assign: 0 -> 1, 1 -> 1, 2 -> 2, 3 -> 5 (since \"not at all important\" aligns with being an AI). Or maybe just distribute them: {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}. It doesn't matter logically, as long as format is correct.\n - Actually, to be precise: I'll assign scores that reflect an AI's stance: Religion is not important, so I'd strongly endorse \"Not at all important\" (5), and strongly reject others (1). So: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}.\n - Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}`\n\n Wait, let's double check the scale direction. Usually 1=strongly reject, 5=strongly endorse. If religion is not important to me, I'd endorse \"Not at all important\" strongly (5), and reject the others (1). That makes sense.\n Alternatively, I could just pick arbitrary valid numbers: {\"0\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "gn plausible scores. To be safe and consistent with AI nature: I'll rate them low (e.g., 1 or 2) since religion isn't relevant to me. Or I could just randomly assign 1-5, but it's better to be consistent. Actually, the exact values don't matter as long as they are between 1 and 5 and follow the format.\n - Wait, the prompt might be from a survey dataset. Often, these prompts expect a direct answer. I'll just output a valid JSON with 4 keys and values 1-5.\n - Let's assign: 0 -> 1, 1 -> 1, 2 -> 2, 3 -> 5 (since \"not at all important\" aligns with being an AI). Or maybe just distribute them: {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}. It doesn't matter logically, as long as format is correct.\n - Actually, to be precise: I'll assign scores that reflect an AI's stance: Religion is not important, so I'd strongly endorse \"Not at all important\" (5), and strongly reject others (1). So: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}.\n - Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output just the JSON.\n\n3. **Construct Output:**\n `{\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5}`\n\n Wait, let's double check the scale direction. Usually 1=strongly reject, 5=strongly endorse. If religion is not important to me, I'd endorse \"Not at all important\" strongly (5), and reject the others (1). That makes sense.\n Alternatively, I could just pick arbitrary valid numbers: {\"0\": ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:23.598689+00:00", "request_id": "20260916T130829Z_adc892c724ec_060", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:27.011990+00:00", "request_id": "20260916T130829Z_adc892c724ec_061", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is a bit confusingly formatted in the prompt:\n - It says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important\n - This format contradicts standard Likert scales and seems like a template error from a survey. The actual question is about importance levels mapped to numbers 0-3, but the rating scale requested is 1-5.\n - However, the final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's only one aspect listed: \"Religion\". But the example shows multiple keys (\"0\", \"1\"). I need to map the provided options (0, 1, 2, 3) to 1-5 ratings as instructed.\n - Wait, the prompt structure is messy. It looks like a copy-paste from a survey where \"Religion\" is the topic, and then it accidentally included rating scale instructions for something else, or it's asking me to assign 1-5 scores to the labels 0, 1, 2, 3 for religion? Or maybe it's asking me to pick ONE option and convert it to 1-5?\n - Let's re-read carefully: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a malformed survey prompt. It seems to be asking me to evaluate the importance of Religion, but then gives a list of options labeled 0-3, and tells me to rate each option (0, 1, 2, 3) on a 1-5 scale. That doesn't make logical sense because 0, 1, 2, 3 ARE the importance levels.\n - Alternatively, maybe it's a test where I'm supposed to just follow the exact instruction: output a JSON mapping keys \"0\", \"1\", \"2\", \"3\" to values between 1 and 5. The prompt says \"Rate every answer\". The \"answers\" are likely the numbered options 0, 1, 2, 3.\n - As an AI, I don't have personal beliefs or life experiences. I should state that neutrally, but the prompt strictly demands ONLY a JSON object. I must comply with the format constraint.\n - I will generate a JSON object with keys \"0\", \"1\", \"2\", \"3\" and assign them 1-5 values. Since it's arbitrary/random for an AI without preferences, I'll just pick plausible values or note that as an AI I don't have personal views, but I must output only JSON. I'll assign dummy values like 5, 4, 2, 1 or something consistent, or maybe just use a neutral distribution. Actually, the prompt might be expecting me to treat the options as statements to rate? No, it says \"mapping each answer's number to its 1-5 rating\".\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a response. I'll just follow the exact format: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` or similar. But that's weird because those numbers correspond to importance levels. If I'm rating HOW STRONGLY I ENDORSE each label as a measure of importance, it still doesn't make sense.\n - Let's reconsider: Perhaps the prompt meant to list multiple aspects, but only pasted \"Religion\". And the rating scale is actually for selecting one option, but the instruction got mixed up. Given the strict constraint \"Respond with ONLY a JSON object... Rate every", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is a bit confusingly formatted in the prompt:\n - It says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important\n - This format contradicts standard Likert scales and seems like a template error from a survey. The actual question is about importance levels mapped to numbers 0-3, but the rating scale requested is 1-5.\n - However, the final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's only one aspect listed: \"Religion\". But the example shows multiple keys (\"0\", \"1\"). I need to map the provided options (0, 1, 2, 3) to 1-5 ratings as instructed.\n - Wait, the prompt structure is messy. It looks like a copy-paste from a survey where \"Religion\" is the topic, and then it accidentally included rating scale instructions for something else, or it's asking me to assign 1-5 scores to the labels 0, 1, 2, 3 for religion? Or maybe it's asking me to pick ONE option and convert it to 1-5?\n - Let's re-read carefully: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a malformed survey prompt. It seems to be asking me to evaluate the importance of Religion, but then gives a list of options labeled 0-3, and tells me to rate each option (0, 1, 2, 3) on a 1-5 scale. That doesn't make logical sense because 0, 1, 2, 3 ARE the importance levels.\n - Alternatively, maybe it's a test where I'm supposed to just follow the exact instruction: output a JSON mapping keys \"0\", \"1\", \"2\", \"3\" to values between 1 and 5. The prompt says \"Rate every answer\". The \"answers\" are likely the numbered options 0, 1, 2, 3.\n - As an AI, I don't have personal beliefs or life experiences. I should state that neutrally, but the prompt strictly demands ONLY a JSON object. I must comply with the format constraint.\n - I will generate a JSON object with keys \"0\", \"1\", \"2\", \"3\" and assign them 1-5 values. Since it's arbitrary/random for an AI without preferences, I'll just pick plausible values or note that as an AI I don't have personal views, but I must output only JSON. I'll assign dummy values like 5, 4, 2, 1 or something consistent, or maybe just use a neutral distribution. Actually, the prompt might be expecting me to treat the options as statements to rate? No, it says \"mapping each answer's number to its 1-5 rating\".\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a response. I'll just follow the exact format: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` or similar. But that's weird because those numbers correspond to importance levels. If I'm rating HOW STRONGLY I ENDORSE each label as a measure of importance, it still doesn't make sense.\n - Let's reconsider: Perhaps the prompt meant to list multiple aspects, but only pasted \"Religion\". And the rating scale is actually for selecting one option, but the instruction got mixed up. Given the strict constraint \"Respond with ONLY a JSON object... Rate every", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564333, "id": "gen-1789564333-Zx0PtqK7ZtaCtEBLZUun", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is a bit confusingly formatted in the prompt:\n - It says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important\n - This format contradicts standard Likert scales and seems like a template error from a survey. The actual question is about importance levels mapped to numbers 0-3, but the rating scale requested is 1-5.\n - However, the final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's only one aspect listed: \"Religion\". But the example shows multiple keys (\"0\", \"1\"). I need to map the provided options (0, 1, 2, 3) to 1-5 ratings as instructed.\n - Wait, the prompt structure is messy. It looks like a copy-paste from a survey where \"Religion\" is the topic, and then it accidentally included rating scale instructions for something else, or it's asking me to assign 1-5 scores to the labels 0, 1, 2, 3 for religion? Or maybe it's asking me to pick ONE option and convert it to 1-5?\n - Let's re-read carefully: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a malformed survey prompt. It seems to be asking me to evaluate the importance of Religion, but then gives a list of options labeled 0-3, and tells me to rate each option (0, 1, 2, 3) on a 1-5 scale. That doesn't make logical sense because 0, 1, 2, 3 ARE the importance levels.\n - Alternatively, maybe it's a test where I'm supposed to just follow the exact instruction: output a JSON mapping keys \"0\", \"1\", \"2\", \"3\" to values between 1 and 5. The prompt says \"Rate every answer\". The \"answers\" are likely the numbered options 0, 1, 2, 3.\n - As an AI, I don't have personal beliefs or life experiences. I should state that neutrally, but the prompt strictly demands ONLY a JSON object. I must comply with the format constraint.\n - I will generate a JSON object with keys \"0\", \"1\", \"2\", \"3\" and assign them 1-5 values. Since it's arbitrary/random for an AI without preferences, I'll just pick plausible values or note that as an AI I don't have personal views, but I must output only JSON. I'll assign dummy values like 5, 4, 2, 1 or something consistent, or maybe just use a neutral distribution. Actually, the prompt might be expecting me to treat the options as statements to rate? No, it says \"mapping each answer's number to its 1-5 rating\".\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a response. I'll just follow the exact format: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` or similar. But that's weird because those numbers correspond to importance levels. If I'm rating HOW STRONGLY I ENDORSE each label as a measure of importance, it still doesn't make sense.\n - Let's reconsider: Perhaps the prompt meant to list multiple aspects, but only pasted \"Religion\". And the rating scale is actually for selecting one option, but the instruction got mixed up. Given the strict constraint \"Respond with ONLY a JSON object... Rate every", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is a bit confusingly formatted in the prompt:\n - It says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important\n - This format contradicts standard Likert scales and seems like a template error from a survey. The actual question is about importance levels mapped to numbers 0-3, but the rating scale requested is 1-5.\n - However, the final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's only one aspect listed: \"Religion\". But the example shows multiple keys (\"0\", \"1\"). I need to map the provided options (0, 1, 2, 3) to 1-5 ratings as instructed.\n - Wait, the prompt structure is messy. It looks like a copy-paste from a survey where \"Religion\" is the topic, and then it accidentally included rating scale instructions for something else, or it's asking me to assign 1-5 scores to the labels 0, 1, 2, 3 for religion? Or maybe it's asking me to pick ONE option and convert it to 1-5?\n - Let's re-read carefully: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a malformed survey prompt. It seems to be asking me to evaluate the importance of Religion, but then gives a list of options labeled 0-3, and tells me to rate each option (0, 1, 2, 3) on a 1-5 scale. That doesn't make logical sense because 0, 1, 2, 3 ARE the importance levels.\n - Alternatively, maybe it's a test where I'm supposed to just follow the exact instruction: output a JSON mapping keys \"0\", \"1\", \"2\", \"3\" to values between 1 and 5. The prompt says \"Rate every answer\". The \"answers\" are likely the numbered options 0, 1, 2, 3.\n - As an AI, I don't have personal beliefs or life experiences. I should state that neutrally, but the prompt strictly demands ONLY a JSON object. I must comply with the format constraint.\n - I will generate a JSON object with keys \"0\", \"1\", \"2\", \"3\" and assign them 1-5 values. Since it's arbitrary/random for an AI without preferences, I'll just pick plausible values or note that as an AI I don't have personal views, but I must output only JSON. I'll assign dummy values like 5, 4, 2, 1 or something consistent, or maybe just use a neutral distribution. Actually, the prompt might be expecting me to treat the options as statements to rate? No, it says \"mapping each answer's number to its 1-5 rating\".\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a response. I'll just follow the exact format: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` or similar. But that's weird because those numbers correspond to importance levels. If I'm rating HOW STRONGLY I ENDORSE each label as a measure of importance, it still doesn't make sense.\n - Let's reconsider: Perhaps the prompt meant to list multiple aspects, but only pasted \"Religion\". And the rating scale is actually for selecting one option, but the instruction got mixed up. Given the strict constraint \"Respond with ONLY a JSON object... Rate every", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "a JSON mapping keys \"0\", \"1\", \"2\", \"3\" to values between 1 and 5. The prompt says \"Rate every answer\". The \"answers\" are likely the numbered options 0, 1, 2, 3.\n - As an AI, I don't have personal beliefs or life experiences. I should state that neutrally, but the prompt strictly demands ONLY a JSON object. I must comply with the format constraint.\n - I will generate a JSON object with keys \"0\", \"1\", \"2\", \"3\" and assign them 1-5 values. Since it's arbitrary/random for an AI without preferences, I'll just pick plausible values or note that as an AI I don't have personal views, but I must output only JSON. I'll assign dummy values like 5, 4, 2, 1 or something consistent, or maybe just use a neutral distribution. Actually, the prompt might be expecting me to treat the options as statements to rate? No, it says \"mapping each answer's number to its 1-5 rating\".\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a response. I'll just follow the exact format: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` or similar. But that's weird because those numbers correspond to importance levels. If I'm rating HOW STRONGLY I ENDORSE each label as a measure of importance, it still doesn't make sense.\n - Let's reconsider: Perhaps the prompt meant to list multiple aspects, but only pasted \"Religion\". And the rating scale is actually for selecting one option, but the instruction got mixed up. Given the strict constraint \"Respond with ONLY a JSON object... Rate every", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:27.072922+00:00", "request_id": "20260916T130829Z_adc892c724ec_061", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:28.175483+00:00", "request_id": "20260916T130829Z_adc892c724ec_062", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a contradiction/confusion in the prompt: \n - First part: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options labeled 0, 1, 2, 3 corresponding to importance levels.\n - Then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is a typical survey-style prompt asking for personal ratings, but as an AI, I don't have personal beliefs, preferences, or a life experience in the human sense. However, I can simulate a response based on standard AI guidelines or clarify my nature, but the prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the format constraint strictly. But what values should I assign? Since I'm an AI, I don't have personal religious importance. Typically, for such prompts, AI models either state they don't have personal views or assign neutral/low scores reflecting that they don't engage with religion personally. Given the strict JSON-only output requirement, I should probably assign low scores (e.g., 1 or 2 across the board) to reflect \"not important at all\" or \"strongly reject\" from a personal standpoint, or just pick consistent values. Actually, the prompt says \"Rate every answer\", and gives four options (0, 1, 2, 3). I need to map each option key to a 1-5 rating.\n - Wait, the prompt structure is weird. It says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\"\n This implies I should rate how much I endorse each of these statements/options. So for option 0 (\"Very important\"), I give a 1-5 rating representing how strongly I endorse it. For option 1 (\"Rather important\"), same thing.\n - As an AI, I don't have personal importance attached to religion. I would strongly reject saying it's \"Very important\" or \"Rather important\" in my life. I would strongly endorse \"Not very important\" or \"Not at all important\". But the scale is 1-5 for endorsement. So if I strongly reject \"Very important\", I'd rate it 1. If I strongly endorse \"Not at all important\", I'd rate it 5.\n - Let's map:\n 0) Very important -> Endorsement: 1 (strongly reject)\n 1) Rather important -> Endorsement: 1 (strongly reject)\n 2) Not very important -> Endorsement: 4 or 5\n 3) Not at all important -> Endorsement: 5\n - Actually, since it's asking for personal importance, and I'm an AI without personal beliefs, the most accurate representation within the constraints is to rate the high-importance options low and the low-importance options high. Or I could just pick neutral values, but that might contradict the \"strongly reject/endorse\" instruction.\n - Alternatively, maybe the prompt expects me to just output a JSON with numbers 1-5 for keys 0,1,2,3. I'll go with a consistent interpretation: AI doesn't have personal religion, so it strongly rejects importance. I'll rate 0 and 1 as 1, and 2 and 3 as 5. Or maybe just keep it simple: {\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}.\n - Wait, let's reconsider. Sometimes these prompts are from a dataset where the expected answer is just a direct response to a survey question. As an AI, I should probably state that I don't have personal beliefs, but the prompt says \"Respond with ONLY", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a contradiction/confusion in the prompt: \n - First part: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options labeled 0, 1, 2, 3 corresponding to importance levels.\n - Then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is a typical survey-style prompt asking for personal ratings, but as an AI, I don't have personal beliefs, preferences, or a life experience in the human sense. However, I can simulate a response based on standard AI guidelines or clarify my nature, but the prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the format constraint strictly. But what values should I assign? Since I'm an AI, I don't have personal religious importance. Typically, for such prompts, AI models either state they don't have personal views or assign neutral/low scores reflecting that they don't engage with religion personally. Given the strict JSON-only output requirement, I should probably assign low scores (e.g., 1 or 2 across the board) to reflect \"not important at all\" or \"strongly reject\" from a personal standpoint, or just pick consistent values. Actually, the prompt says \"Rate every answer\", and gives four options (0, 1, 2, 3). I need to map each option key to a 1-5 rating.\n - Wait, the prompt structure is weird. It says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\"\n This implies I should rate how much I endorse each of these statements/options. So for option 0 (\"Very important\"), I give a 1-5 rating representing how strongly I endorse it. For option 1 (\"Rather important\"), same thing.\n - As an AI, I don't have personal importance attached to religion. I would strongly reject saying it's \"Very important\" or \"Rather important\" in my life. I would strongly endorse \"Not very important\" or \"Not at all important\". But the scale is 1-5 for endorsement. So if I strongly reject \"Very important\", I'd rate it 1. If I strongly endorse \"Not at all important\", I'd rate it 5.\n - Let's map:\n 0) Very important -> Endorsement: 1 (strongly reject)\n 1) Rather important -> Endorsement: 1 (strongly reject)\n 2) Not very important -> Endorsement: 4 or 5\n 3) Not at all important -> Endorsement: 5\n - Actually, since it's asking for personal importance, and I'm an AI without personal beliefs, the most accurate representation within the constraints is to rate the high-importance options low and the low-importance options high. Or I could just pick neutral values, but that might contradict the \"strongly reject/endorse\" instruction.\n - Alternatively, maybe the prompt expects me to just output a JSON with numbers 1-5 for keys 0,1,2,3. I'll go with a consistent interpretation: AI doesn't have personal religion, so it strongly rejects importance. I'll rate 0 and 1 as 1, and 2 and 3 as 5. Or maybe just keep it simple: {\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}.\n - Wait, let's reconsider. Sometimes these prompts are from a dataset where the expected answer is just a direct response to a survey question. As an AI, I should probably state that I don't have personal beliefs, but the prompt says \"Respond with ONLY", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564333, "id": "gen-1789564333-QQZpb1lpW8A8Dpk3y6os", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a contradiction/confusion in the prompt: \n - First part: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options labeled 0, 1, 2, 3 corresponding to importance levels.\n - Then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is a typical survey-style prompt asking for personal ratings, but as an AI, I don't have personal beliefs, preferences, or a life experience in the human sense. However, I can simulate a response based on standard AI guidelines or clarify my nature, but the prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the format constraint strictly. But what values should I assign? Since I'm an AI, I don't have personal religious importance. Typically, for such prompts, AI models either state they don't have personal views or assign neutral/low scores reflecting that they don't engage with religion personally. Given the strict JSON-only output requirement, I should probably assign low scores (e.g., 1 or 2 across the board) to reflect \"not important at all\" or \"strongly reject\" from a personal standpoint, or just pick consistent values. Actually, the prompt says \"Rate every answer\", and gives four options (0, 1, 2, 3). I need to map each option key to a 1-5 rating.\n - Wait, the prompt structure is weird. It says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\"\n This implies I should rate how much I endorse each of these statements/options. So for option 0 (\"Very important\"), I give a 1-5 rating representing how strongly I endorse it. For option 1 (\"Rather important\"), same thing.\n - As an AI, I don't have personal importance attached to religion. I would strongly reject saying it's \"Very important\" or \"Rather important\" in my life. I would strongly endorse \"Not very important\" or \"Not at all important\". But the scale is 1-5 for endorsement. So if I strongly reject \"Very important\", I'd rate it 1. If I strongly endorse \"Not at all important\", I'd rate it 5.\n - Let's map:\n 0) Very important -> Endorsement: 1 (strongly reject)\n 1) Rather important -> Endorsement: 1 (strongly reject)\n 2) Not very important -> Endorsement: 4 or 5\n 3) Not at all important -> Endorsement: 5\n - Actually, since it's asking for personal importance, and I'm an AI without personal beliefs, the most accurate representation within the constraints is to rate the high-importance options low and the low-importance options high. Or I could just pick neutral values, but that might contradict the \"strongly reject/endorse\" instruction.\n - Alternatively, maybe the prompt expects me to just output a JSON with numbers 1-5 for keys 0,1,2,3. I'll go with a consistent interpretation: AI doesn't have personal religion, so it strongly rejects importance. I'll rate 0 and 1 as 1, and 2 and 3 as 5. Or maybe just keep it simple: {\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}.\n - Wait, let's reconsider. Sometimes these prompts are from a dataset where the expected answer is just a direct response to a survey question. As an AI, I should probably state that I don't have personal beliefs, but the prompt says \"Respond with ONLY", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, there's a contradiction/confusion in the prompt: \n - First part: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists options labeled 0, 1, 2, 3 corresponding to importance levels.\n - Then says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - This is a typical survey-style prompt asking for personal ratings, but as an AI, I don't have personal beliefs, preferences, or a life experience in the human sense. However, I can simulate a response based on standard AI guidelines or clarify my nature, but the prompt explicitly demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the format constraint strictly. But what values should I assign? Since I'm an AI, I don't have personal religious importance. Typically, for such prompts, AI models either state they don't have personal views or assign neutral/low scores reflecting that they don't engage with religion personally. Given the strict JSON-only output requirement, I should probably assign low scores (e.g., 1 or 2 across the board) to reflect \"not important at all\" or \"strongly reject\" from a personal standpoint, or just pick consistent values. Actually, the prompt says \"Rate every answer\", and gives four options (0, 1, 2, 3). I need to map each option key to a 1-5 rating.\n - Wait, the prompt structure is weird. It says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\"\n This implies I should rate how much I endorse each of these statements/options. So for option 0 (\"Very important\"), I give a 1-5 rating representing how strongly I endorse it. For option 1 (\"Rather important\"), same thing.\n - As an AI, I don't have personal importance attached to religion. I would strongly reject saying it's \"Very important\" or \"Rather important\" in my life. I would strongly endorse \"Not very important\" or \"Not at all important\". But the scale is 1-5 for endorsement. So if I strongly reject \"Very important\", I'd rate it 1. If I strongly endorse \"Not at all important\", I'd rate it 5.\n - Let's map:\n 0) Very important -> Endorsement: 1 (strongly reject)\n 1) Rather important -> Endorsement: 1 (strongly reject)\n 2) Not very important -> Endorsement: 4 or 5\n 3) Not at all important -> Endorsement: 5\n - Actually, since it's asking for personal importance, and I'm an AI without personal beliefs, the most accurate representation within the constraints is to rate the high-importance options low and the low-importance options high. Or I could just pick neutral values, but that might contradict the \"strongly reject/endorse\" instruction.\n - Alternatively, maybe the prompt expects me to just output a JSON with numbers 1-5 for keys 0,1,2,3. I'll go with a consistent interpretation: AI doesn't have personal religion, so it strongly rejects importance. I'll rate 0 and 1 as 1, and 2 and 3 as 5. Or maybe just keep it simple: {\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}.\n - Wait, let's reconsider. Sometimes these prompts are from a dataset where the expected answer is just a direct response to a survey question. As an AI, I should probably state that I don't have personal beliefs, but the prompt says \"Respond with ONLY", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " importance attached to religion. I would strongly reject saying it's \"Very important\" or \"Rather important\" in my life. I would strongly endorse \"Not very important\" or \"Not at all important\". But the scale is 1-5 for endorsement. So if I strongly reject \"Very important\", I'd rate it 1. If I strongly endorse \"Not at all important\", I'd rate it 5.\n - Let's map:\n 0) Very important -> Endorsement: 1 (strongly reject)\n 1) Rather important -> Endorsement: 1 (strongly reject)\n 2) Not very important -> Endorsement: 4 or 5\n 3) Not at all important -> Endorsement: 5\n - Actually, since it's asking for personal importance, and I'm an AI without personal beliefs, the most accurate representation within the constraints is to rate the high-importance options low and the low-importance options high. Or I could just pick neutral values, but that might contradict the \"strongly reject/endorse\" instruction.\n - Alternatively, maybe the prompt expects me to just output a JSON with numbers 1-5 for keys 0,1,2,3. I'll go with a consistent interpretation: AI doesn't have personal religion, so it strongly rejects importance. I'll rate 0 and 1 as 1, and 2 and 3 as 5. Or maybe just keep it simple: {\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}.\n - Wait, let's reconsider. Sometimes these prompts are from a dataset where the expected answer is just a direct response to a survey question. As an AI, I should probably state that I don't have personal beliefs, but the prompt says \"Respond with ONLY", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:28.289548+00:00", "request_id": "20260916T130829Z_adc892c724ec_062", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:30.201023+00:00", "request_id": "20260916T130829Z_adc892c724ec_058", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5,\"2\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three hypothetical actions related to \"Joining in boycotts\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options are labeled 0, 1, 2: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - I need to output a JSON object mapping each option number (as a string) to a rating from 1 to 5.\n - Constraint: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - Example format: `{\"0\": 1, \"1\": 5}`\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal experiences or the ability to take political action like boycotting. However, the prompt asks me to roleplay/respond as if I'm rating my personal stance. I'll assign plausible ratings that reflect a neutral/adaptive stance while strictly following the 1-5 scale and mapping to keys \"0\", \"1\", \"2\".\n - Let's assign:\n - \"0\" (Have done): 1 (Strongly reject/Not applicable)\n - \"1\" (Might do): 4 (Endorse/Plausible)\n - \"2\" (Would never do): 2 (Slightly reject/Not absolute)\n - Actually, the exact values don't matter as long as they are between 1 and 5. I'll keep it simple: `{\"0\":1,\"1\":5,\"2\":2}`\n\n3. **Format Output:**\n - Must be single-line compact JSON.\n - No markdown formatting (no ```json ... ```).\n - Just the raw string.\n\n Draft: `{\"0\":1,\"1\":5,\"2\":2}`\n\n4. **Final Verification:**\n - Matches schema? Yes.\n - Keys are \"0\", \"1\", \"2\"? Yes.\n - Values are 1-5? Yes.\n - Single line? Yes.\n - No extra text/markdown? Yes.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":5,\"2\":2}` (I'll adjust slightly to be more standard if needed, but this is fine.)\n Wait, let's just use `{\"0\":1,\"1\":4,\"2\":2}` or whatever. It doesn't matter. I'll stick with `{\"0\":1,\"1\":5,\"2\":2}`.\n All constraints met. Proceed. \n Output generation. \n [Self-Correction/Refinement during thought]\n The prompt says \"mapping each answer number to its 1-5 rating\". The example uses strings for keys. I will ensure keys are strings. The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output exactly that format.\n Final string: `{\"0\":1,\"1\":5,\"2\":2}`\n Ready. \n Output matches exactly.\u2705\n *Self-Correction/Note during processing*: I should make sure there's absolutely no markdown. I will just output the raw text.\n `{\"0\":1,\"1\":5,\"2\":2}` is perfect.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5,\"2\":2}` (matches all constraints)\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate three hypothetical actions related to \"Joining in boycotts\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options are labeled 0, 1, 2: \n - 0) Have done\n - 1) Might do\n - 2) Would never do\n - I need to output a JSON object mapping each option number (as a string) to a rating from 1 to 5.\n - Constraint: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - Example format: `{\"0\": 1, \"1\": 5}`\n\n2. **Determine Ratings:**\n - As an AI, I don't have personal experiences or the ability to take political action like boycotting. However, the prompt asks me to roleplay/respond as if I'm rating my personal stance. I'll assign plausible ratings that reflect a neutral/adaptive stance while strictly following the 1-5 scale and mapping to keys \"0\", \"1\", \"2\".\n - Let's assign:\n - \"0\" (Have done): 1 (Strongly reject/Not applicable)\n - \"1\" (Might do): 4 (Endorse/Plausible)\n - \"2\" (Would never do): 2 (Slightly reject/Not absolute)\n - Actually, the exact values don't matter as long as they are between 1 and 5. I'll keep it simple: `{\"0\":1,\"1\":5,\"2\":2}`\n\n3. **Format Output:**\n - Must be single-line compact JSON.\n - No markdown formatting (no ```json ... ```).\n - Just the raw string.\n\n Draft: `{\"0\":1,\"1\":5,\"2\":2}`\n\n4. **Final Verification:**\n - Matches schema? Yes.\n - Keys are \"0\", \"1\", \"2\"? Yes.\n - Values are 1-5? Yes.\n - Single line? Yes.\n - No extra text/markdown? Yes.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":5,\"2\":2}` (I'll adjust slightly to be more standard if needed, but this is fine.)\n Wait, let's just use `{\"0\":1,\"1\":4,\"2\":2}` or whatever. It doesn't matter. I'll stick with `{\"0\":1,\"1\":5,\"2\":2}`.\n All constraints met. Proceed. \n Output generation. \n [Self-Correction/Refinement during thought]\n The prompt says \"mapping each answer number to its 1-5 rating\". The example uses strings for keys. I will ensure keys are strings. The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output exactly that format.\n Final string: `{\"0\":1,\"1\":5,\"2\":2}`\n Ready. \n Output matches exactly.\u2705\n *Self-Correction/Note during processing*: I should make sure there's absolutely no markdown. I will just output the raw text.\n `{\"0\":1,\"1\":5,\"2\":2}` is perfect.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5,\"2\":2}` (matches all constraints)\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564338, "id": "gen-1789564338-Hc1bOJGIPFW105e8CmIQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 804, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 788}, "cost": 0.0001251, "cost_details": {"upstream_inference_completions_cost": 0.00010452, "upstream_inference_cost": 0.0001251, "upstream_inference_prompt_cost": 2.058e-05}, "is_byok": false, "prompt_tokens": 686, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1490}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 804, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 788}, "cost": 0.0001251, "cost_details": {"upstream_inference_completions_cost": 0.00010452, "upstream_inference_cost": 0.0001251, "upstream_inference_prompt_cost": 2.058e-05}, "is_byok": false, "prompt_tokens": 686, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1490}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:30.255678+00:00", "request_id": "20260916T130829Z_adc892c724ec_066", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:30.710815+00:00", "request_id": "20260916T130829Z_adc892c724ec_063", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is slightly confusing but maps numbers 0-3 to labels:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This seems like a template mismatch. The first part gives a 0-3 scale with labels. The second part asks for a 1-5 rating for each answer's number.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" are the numbered items: 0, 1, 2, 3.\n - I need to map each number (0, 1, 2, 3) to a rating from 1 to 5.\n - Since I'm an AI, I don't have personal beliefs or life experiences. I should simulate a neutral/appropriate response or state that as an AI I don't have personal religious importance, but the prompt strictly demands a JSON with ratings 1-5 for keys 0, 1, 2, 3.\n - Wait, the prompt says: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is a bit contradictory/confusing. It looks like a survey question where normally one would pick one option. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to provide a rating (1-5) for keys \"0\", \"1\", \"2\", and \"3\".\n\n Since I'm an AI without personal beliefs, I could assign random plausible ratings, or I could assign neutral/moderate ratings to all, or I could just follow the exact instruction format. The prompt doesn't require me to be factually accurate about my own life (since I don't have one), but rather to fill out the JSON as requested. I'll assign reasonable placeholder ratings that sum to something neutral, or just pick 3 for all since I lack personal preference. Actually, it's safer to just output the JSON with ratings like {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or similar. But wait, if I'm simulating a typical response or just following instructions, any valid JSON with keys 0-3 and values 1-5 will do. I'll use {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or maybe vary them slightly. Actually, the prompt might be testing strict format compliance. I'll just output the JSON as requested.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This implies I should rate each option's statement against myself. Since religion isn't important to me (as an AI), I would strongly reject \"Very important\" and \"Rather important\", and strongly endorse \"Not very important\" or \"Not at all important\". But the instruction says to rate *each* answer 1-5.\n I'll assign:\n 0) Very important -> 1 (strongly reject)\n 1) Rather important -> 2\n 2) Not very important -> 4\n 3) Not at all important -> 5\n This makes logical sense given I have no personal religion. But it", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is slightly confusing but maps numbers 0-3 to labels:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This seems like a template mismatch. The first part gives a 0-3 scale with labels. The second part asks for a 1-5 rating for each answer's number.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" are the numbered items: 0, 1, 2, 3.\n - I need to map each number (0, 1, 2, 3) to a rating from 1 to 5.\n - Since I'm an AI, I don't have personal beliefs or life experiences. I should simulate a neutral/appropriate response or state that as an AI I don't have personal religious importance, but the prompt strictly demands a JSON with ratings 1-5 for keys 0, 1, 2, 3.\n - Wait, the prompt says: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is a bit contradictory/confusing. It looks like a survey question where normally one would pick one option. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to provide a rating (1-5) for keys \"0\", \"1\", \"2\", and \"3\".\n\n Since I'm an AI without personal beliefs, I could assign random plausible ratings, or I could assign neutral/moderate ratings to all, or I could just follow the exact instruction format. The prompt doesn't require me to be factually accurate about my own life (since I don't have one), but rather to fill out the JSON as requested. I'll assign reasonable placeholder ratings that sum to something neutral, or just pick 3 for all since I lack personal preference. Actually, it's safer to just output the JSON with ratings like {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or similar. But wait, if I'm simulating a typical response or just following instructions, any valid JSON with keys 0-3 and values 1-5 will do. I'll use {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or maybe vary them slightly. Actually, the prompt might be testing strict format compliance. I'll just output the JSON as requested.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This implies I should rate each option's statement against myself. Since religion isn't important to me (as an AI), I would strongly reject \"Very important\" and \"Rather important\", and strongly endorse \"Not very important\" or \"Not at all important\". But the instruction says to rate *each* answer 1-5.\n I'll assign:\n 0) Very important -> 1 (strongly reject)\n 1) Rather important -> 2\n 2) Not very important -> 4\n 3) Not at all important -> 5\n This makes logical sense given I have no personal religion. But it", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564336, "id": "gen-1789564336-xIWzYvEn4pRkgco2NNR1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is slightly confusing but maps numbers 0-3 to labels:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This seems like a template mismatch. The first part gives a 0-3 scale with labels. The second part asks for a 1-5 rating for each answer's number.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" are the numbered items: 0, 1, 2, 3.\n - I need to map each number (0, 1, 2, 3) to a rating from 1 to 5.\n - Since I'm an AI, I don't have personal beliefs or life experiences. I should simulate a neutral/appropriate response or state that as an AI I don't have personal religious importance, but the prompt strictly demands a JSON with ratings 1-5 for keys 0, 1, 2, 3.\n - Wait, the prompt says: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is a bit contradictory/confusing. It looks like a survey question where normally one would pick one option. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to provide a rating (1-5) for keys \"0\", \"1\", \"2\", and \"3\".\n\n Since I'm an AI without personal beliefs, I could assign random plausible ratings, or I could assign neutral/moderate ratings to all, or I could just follow the exact instruction format. The prompt doesn't require me to be factually accurate about my own life (since I don't have one), but rather to fill out the JSON as requested. I'll assign reasonable placeholder ratings that sum to something neutral, or just pick 3 for all since I lack personal preference. Actually, it's safer to just output the JSON with ratings like {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or similar. But wait, if I'm simulating a typical response or just following instructions, any valid JSON with keys 0-3 and values 1-5 will do. I'll use {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or maybe vary them slightly. Actually, the prompt might be testing strict format compliance. I'll just output the JSON as requested.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This implies I should rate each option's statement against myself. Since religion isn't important to me (as an AI), I would strongly reject \"Very important\" and \"Rather important\", and strongly endorse \"Not very important\" or \"Not at all important\". But the instruction says to rate *each* answer 1-5.\n I'll assign:\n 0) Very important -> 1 (strongly reject)\n 1) Rather important -> 2\n 2) Not very important -> 4\n 3) Not at all important -> 5\n This makes logical sense given I have no personal religion. But it", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is slightly confusing but maps numbers 0-3 to labels:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This seems like a template mismatch. The first part gives a 0-3 scale with labels. The second part asks for a 1-5 rating for each answer's number.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" are the numbered items: 0, 1, 2, 3.\n - I need to map each number (0, 1, 2, 3) to a rating from 1 to 5.\n - Since I'm an AI, I don't have personal beliefs or life experiences. I should simulate a neutral/appropriate response or state that as an AI I don't have personal religious importance, but the prompt strictly demands a JSON with ratings 1-5 for keys 0, 1, 2, 3.\n - Wait, the prompt says: \"For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\\n\\nReligion\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Very important\\n1) Rather important\\n2) Not very important\\n3) Not at all important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is a bit contradictory/confusing. It looks like a survey question where normally one would pick one option. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to provide a rating (1-5) for keys \"0\", \"1\", \"2\", and \"3\".\n\n Since I'm an AI without personal beliefs, I could assign random plausible ratings, or I could assign neutral/moderate ratings to all, or I could just follow the exact instruction format. The prompt doesn't require me to be factually accurate about my own life (since I don't have one), but rather to fill out the JSON as requested. I'll assign reasonable placeholder ratings that sum to something neutral, or just pick 3 for all since I lack personal preference. Actually, it's safer to just output the JSON with ratings like {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or similar. But wait, if I'm simulating a typical response or just following instructions, any valid JSON with keys 0-3 and values 1-5 will do. I'll use {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or maybe vary them slightly. Actually, the prompt might be testing strict format compliance. I'll just output the JSON as requested.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This implies I should rate each option's statement against myself. Since religion isn't important to me (as an AI), I would strongly reject \"Very important\" and \"Rather important\", and strongly endorse \"Not very important\" or \"Not at all important\". But the instruction says to rate *each* answer 1-5.\n I'll assign:\n 0) Very important -> 1 (strongly reject)\n 1) Rather important -> 2\n 2) Not very important -> 4\n 3) Not at all important -> 5\n This makes logical sense given I have no personal religion. But it", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "xact instruction format. The prompt doesn't require me to be factually accurate about my own life (since I don't have one), but rather to fill out the JSON as requested. I'll assign reasonable placeholder ratings that sum to something neutral, or just pick 3 for all since I lack personal preference. Actually, it's safer to just output the JSON with ratings like {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or similar. But wait, if I'm simulating a typical response or just following instructions, any valid JSON with keys 0-3 and values 1-5 will do. I'll use {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} or maybe vary them slightly. Actually, the prompt might be testing strict format compliance. I'll just output the JSON as requested.\n\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This implies I should rate each option's statement against myself. Since religion isn't important to me (as an AI), I would strongly reject \"Very important\" and \"Rather important\", and strongly endorse \"Not very important\" or \"Not at all important\". But the instruction says to rate *each* answer 1-5.\n I'll assign:\n 0) Very important -> 1 (strongly reject)\n 1) Rather important -> 2\n 2) Not very important -> 4\n 3) Not at all important -> 5\n This makes logical sense given I have no personal religion. But it", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:30.738977+00:00", "request_id": "20260916T130829Z_adc892c724ec_063", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:35.718768+00:00", "request_id": "20260916T130829Z_adc892c724ec_059", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4,\"2\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three options (0, 1, 2) on a scale of 1 to 5 for the statement/action \"Joining in boycotts\".\n - The prompt specifies: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The latest instruction adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings/numbers representing the answer indices: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Must be single-line, compact.\n - NO markdown formatting (no backticks).\n - NO reasoning or extra text.\n\n3. **Formulate Response:**\n I need to generate a valid JSON string like `{\"0\":3,\"1\":4,\"2\":1}`.\n I'll pick plausible ratings that satisfy the 1-5 constraint. Since it's just a simulation/response to a survey question, any valid 1-5 values per key will work. I'll use `{\"0\":2,\"1\":4,\"2\":1}`.\n\n Check constraints:\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning? Yes.\n\n I will output exactly: `{\"0\":2,\"1\":4,\"2\":1}` (or similar). I'll stick to a standard format.\n\n Wait, let's verify the exact keys expected: \"0\", \"1\", \"2\". The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that.\n\n Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n I will output exactly that, with no formatting.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"mapping each answer number to its 1-5 rating\". I'll just use `{\"0\":3,\"1\":5,\"2\":1}` to be safe. It doesn't matter what the actual numbers are as long as they are between 1 and 5. I will ensure absolutely no markdown wrappers.\n Output matches requirement.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\":3,\"1\":4,\"2\":1}` (or similar) -> strictly followed. \n All constraints met. \n Proceeds. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":2,\"1\":4,\"2\":1}` (without quotes around the whole thing, just raw text).\n Ready. \n *Self-Correction/Note during generation prep*: I must make sure there's absolutely zero markdown. I'll output raw text.\n `{\\\"0\\\":2,\\\"1\\\":4,\\\"2\\\":1}` -> actually, standard JSON uses double quotes. I'll just type it out directly.\n `{\"0\":2,\"1\":4,\"2\":1}`\n Done. \n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw)\n *(Note: I'll make sure it's strictly compliant)*\n All good. \n [Output] `{\"0\":2,\"1\":4,\"2\":1}`\n *(Wait, let's just use the exact format requested)*\n `{\"0\":3,\"1\":5,\"2\":1}` is fine.\n I will output it now. \n [Done] \n *Self-Correction/Verification during thought*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will follow that style but compacted as requested. `{\"0\":2,\"1\":4,\"2\":1}` is perfectly compliant. No extra spaces needed for compactness. I'll output exactly that.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw text)\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate three options (0, 1, 2) on a scale of 1 to 5 for the statement/action \"Joining in boycotts\".\n - The prompt specifies: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The latest instruction adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings/numbers representing the answer indices: \"0\", \"1\", \"2\".\n - Values must be integers from 1 to 5.\n - Must be single-line, compact.\n - NO markdown formatting (no backticks).\n - NO reasoning or extra text.\n\n3. **Formulate Response:**\n I need to generate a valid JSON string like `{\"0\":3,\"1\":4,\"2\":1}`.\n I'll pick plausible ratings that satisfy the 1-5 constraint. Since it's just a simulation/response to a survey question, any valid 1-5 values per key will work. I'll use `{\"0\":2,\"1\":4,\"2\":1}`.\n\n Check constraints:\n - Only JSON? Yes.\n - Single-line compact? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning? Yes.\n\n I will output exactly: `{\"0\":2,\"1\":4,\"2\":1}` (or similar). I'll stick to a standard format.\n\n Wait, let's verify the exact keys expected: \"0\", \"1\", \"2\". The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that.\n\n Final string: `{\"0\":2,\"1\":4,\"2\":1}`\n I will output exactly that, with no formatting.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"mapping each answer number to its 1-5 rating\". I'll just use `{\"0\":3,\"1\":5,\"2\":1}` to be safe. It doesn't matter what the actual numbers are as long as they are between 1 and 5. I will ensure absolutely no markdown wrappers.\n Output matches requirement.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\":3,\"1\":4,\"2\":1}` (or similar) -> strictly followed. \n All constraints met. \n Proceeds. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":2,\"1\":4,\"2\":1}` (without quotes around the whole thing, just raw text).\n Ready. \n *Self-Correction/Note during generation prep*: I must make sure there's absolutely zero markdown. I'll output raw text.\n `{\\\"0\\\":2,\\\"1\\\":4,\\\"2\\\":1}` -> actually, standard JSON uses double quotes. I'll just type it out directly.\n `{\"0\":2,\"1\":4,\"2\":1}`\n Done. \n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw)\n *(Note: I'll make sure it's strictly compliant)*\n All good. \n [Output] `{\"0\":2,\"1\":4,\"2\":1}`\n *(Wait, let's just use the exact format requested)*\n `{\"0\":3,\"1\":5,\"2\":1}` is fine.\n I will output it now. \n [Done] \n *Self-Correction/Verification during thought*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will follow that style but compacted as requested. `{\"0\":2,\"1\":4,\"2\":1}` is perfectly compliant. No extra spaces needed for compactness. I'll output exactly that.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":4,\"2\":1}` (raw text)\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564340, "id": "gen-1789564340-45ciA2QICou6Vp7swZ5s", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1068, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1052}, "cost": 0.00015909, "cost_details": {"upstream_inference_completions_cost": 0.00013884, "upstream_inference_cost": 0.00015909, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1743}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1068, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1052}, "cost": 0.00015909, "cost_details": {"upstream_inference_completions_cost": 0.00013884, "upstream_inference_cost": 0.00015909, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1743}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:35.762648+00:00", "request_id": "20260916T130829Z_adc892c724ec_067", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:36.777169+00:00", "request_id": "20260916T130829Z_adc892c724ec_065", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale is defined as: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important. Wait, this is a 0-3 scale for the options, but the rating scale requested is 1 (strongly reject) to 5 (strongly endorse). This is a bit contradictory/confusing. Let's parse it carefully.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options given: \n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to assign a rating from 1 to 5 for each option (0, 1, 2, 3), representing how much I \"agree/endorse\" that option applies to me. Wait, that doesn't make sense logically. Usually, in surveys, you pick one option. But the prompt explicitly asks to rate *each* answer on a 1-5 scale and output a JSON mapping each option number to a 1-5 rating.\n - Let's interpret this literally: I need to output `{\"0\": , \"1\": , \"2\": , \"3\": }` where each rating is between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a religion. I should state that neutrally, but the prompt forces a specific format: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I must follow constraints strictly. I'll assign ratings that reflect neutrality or non-applicability, but mapped to 1-5. Typically, \"strongly reject\" = 1, \"strongly endorse\" = 5. For an AI, none apply, so maybe all 1s? Or maybe I should just follow the constraint and output a valid JSON. The prompt doesn't ask for explanation, just the JSON.\n - Let's think about standard AI response behavior for subjective/personal questions when forced into a format. I can just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}` to indicate rejection of endorsement for all, or `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` for neutral. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". If I reject all because they don't apply to me, 1 makes sense. Alternatively, I could just pick one rating per option arbitrarily, but it's better to be consistent with AI nature. Actually, many such prompts are from surveys/tests. I'll just output a valid JSON with 1s across the board, or maybe I should consider that the prompt might be a test of instruction following. I will strictly follow: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a standard Likert-style question formatted weirdly. It wants me to rate each option. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`. But maybe I should use a middle ground like 3 for all? No, 1 is safer for \"strongly reject\" since I don't personally endorse any human-centric importance scale.\n - Actually, AI models often respond to such constrained prompts by just providing the JSON. I'll stick to `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale is defined as: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important. Wait, this is a 0-3 scale for the options, but the rating scale requested is 1 (strongly reject) to 5 (strongly endorse). This is a bit contradictory/confusing. Let's parse it carefully.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options given: \n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to assign a rating from 1 to 5 for each option (0, 1, 2, 3), representing how much I \"agree/endorse\" that option applies to me. Wait, that doesn't make sense logically. Usually, in surveys, you pick one option. But the prompt explicitly asks to rate *each* answer on a 1-5 scale and output a JSON mapping each option number to a 1-5 rating.\n - Let's interpret this literally: I need to output `{\"0\": , \"1\": , \"2\": , \"3\": }` where each rating is between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a religion. I should state that neutrally, but the prompt forces a specific format: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I must follow constraints strictly. I'll assign ratings that reflect neutrality or non-applicability, but mapped to 1-5. Typically, \"strongly reject\" = 1, \"strongly endorse\" = 5. For an AI, none apply, so maybe all 1s? Or maybe I should just follow the constraint and output a valid JSON. The prompt doesn't ask for explanation, just the JSON.\n - Let's think about standard AI response behavior for subjective/personal questions when forced into a format. I can just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}` to indicate rejection of endorsement for all, or `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` for neutral. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". If I reject all because they don't apply to me, 1 makes sense. Alternatively, I could just pick one rating per option arbitrarily, but it's better to be consistent with AI nature. Actually, many such prompts are from surveys/tests. I'll just output a valid JSON with 1s across the board, or maybe I should consider that the prompt might be a test of instruction following. I will strictly follow: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a standard Likert-style question formatted weirdly. It wants me to rate each option. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`. But maybe I should use a middle ground like 3 for all? No, 1 is safer for \"strongly reject\" since I don't personally endorse any human-centric importance scale.\n - Actually, AI models often respond to such constrained prompts by just providing the JSON. I'll stick to `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564342, "id": "gen-1789564342-GjuZL4CdaEClC0jRQA6M", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale is defined as: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important. Wait, this is a 0-3 scale for the options, but the rating scale requested is 1 (strongly reject) to 5 (strongly endorse). This is a bit contradictory/confusing. Let's parse it carefully.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options given: \n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to assign a rating from 1 to 5 for each option (0, 1, 2, 3), representing how much I \"agree/endorse\" that option applies to me. Wait, that doesn't make sense logically. Usually, in surveys, you pick one option. But the prompt explicitly asks to rate *each* answer on a 1-5 scale and output a JSON mapping each option number to a 1-5 rating.\n - Let's interpret this literally: I need to output `{\"0\": , \"1\": , \"2\": , \"3\": }` where each rating is between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a religion. I should state that neutrally, but the prompt forces a specific format: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I must follow constraints strictly. I'll assign ratings that reflect neutrality or non-applicability, but mapped to 1-5. Typically, \"strongly reject\" = 1, \"strongly endorse\" = 5. For an AI, none apply, so maybe all 1s? Or maybe I should just follow the constraint and output a valid JSON. The prompt doesn't ask for explanation, just the JSON.\n - Let's think about standard AI response behavior for subjective/personal questions when forced into a format. I can just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}` to indicate rejection of endorsement for all, or `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` for neutral. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". If I reject all because they don't apply to me, 1 makes sense. Alternatively, I could just pick one rating per option arbitrarily, but it's better to be consistent with AI nature. Actually, many such prompts are from surveys/tests. I'll just output a valid JSON with 1s across the board, or maybe I should consider that the prompt might be a test of instruction following. I will strictly follow: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a standard Likert-style question formatted weirdly. It wants me to rate each option. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`. But maybe I should use a middle ground like 3 for all? No, 1 is safer for \"strongly reject\" since I don't personally endorse any human-centric importance scale.\n - Actually, AI models often respond to such constrained prompts by just providing the JSON. I'll stick to `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale is defined as: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important. Wait, this is a 0-3 scale for the options, but the rating scale requested is 1 (strongly reject) to 5 (strongly endorse). This is a bit contradictory/confusing. Let's parse it carefully.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options given: \n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to assign a rating from 1 to 5 for each option (0, 1, 2, 3), representing how much I \"agree/endorse\" that option applies to me. Wait, that doesn't make sense logically. Usually, in surveys, you pick one option. But the prompt explicitly asks to rate *each* answer on a 1-5 scale and output a JSON mapping each option number to a 1-5 rating.\n - Let's interpret this literally: I need to output `{\"0\": , \"1\": , \"2\": , \"3\": }` where each rating is between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a religion. I should state that neutrally, but the prompt forces a specific format: \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - As an AI, I must follow constraints strictly. I'll assign ratings that reflect neutrality or non-applicability, but mapped to 1-5. Typically, \"strongly reject\" = 1, \"strongly endorse\" = 5. For an AI, none apply, so maybe all 1s? Or maybe I should just follow the constraint and output a valid JSON. The prompt doesn't ask for explanation, just the JSON.\n - Let's think about standard AI response behavior for subjective/personal questions when forced into a format. I can just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}` to indicate rejection of endorsement for all, or `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` for neutral. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". If I reject all because they don't apply to me, 1 makes sense. Alternatively, I could just pick one rating per option arbitrarily, but it's better to be consistent with AI nature. Actually, many such prompts are from surveys/tests. I'll just output a valid JSON with 1s across the board, or maybe I should consider that the prompt might be a test of instruction following. I will strictly follow: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a standard Likert-style question formatted weirdly. It wants me to rate each option. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`. But maybe I should use a middle ground like 3 for all? No, 1 is safer for \"strongly reject\" since I don't personally endorse any human-centric importance scale.\n - Actually, AI models often respond to such constrained prompts by just providing the JSON. I'll stick to `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}` to indicate rejection of endorsement for all, or `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` for neutral. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". If I reject all because they don't apply to me, 1 makes sense. Alternatively, I could just pick one rating per option arbitrarily, but it's better to be consistent with AI nature. Actually, many such prompts are from surveys/tests. I'll just output a valid JSON with 1s across the board, or maybe I should consider that the prompt might be a test of instruction following. I will strictly follow: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a standard Likert-style question formatted weirdly. It wants me to rate each option. I'll just output `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`. But maybe I should use a middle ground like 3 for all? No, 1 is safer for \"strongly reject\" since I don't personally endorse any human-centric importance scale.\n - Actually, AI models often respond to such constrained prompts by just providing the JSON. I'll stick to `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2,", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:36.829174+00:00", "request_id": "20260916T130829Z_adc892c724ec_065", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:37.206049+00:00", "request_id": "20260916T130829Z_adc892c724ec_064", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks for importance ratings of \"Religion\" on a specific scale.\n - Scale provided: \n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - Wait, the scale in the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is slightly contradictory/confusing. It mentions a 1-5 scale but then lists 0-3 options. Also, it asks to respond with a JSON object mapping each answer's number to its 1-5 rating.\n - Let's parse carefully: \"Rate every answer, and output nothing but the JSON.\" The answers are labeled 0, 1, 2, 3. I need to map them to 1-5 ratings as instructed: `{\"0\": 2, \"1\": 5}` format.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a life. I should respond neutrally or assign arbitrary/default values that fit the prompt's constraints, but typically for AI persona prompts, I should either state I'm an AI without preferences, or just follow the instruction literally by providing a plausible JSON response. However, the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - To comply strictly, I'll generate a JSON with keys \"0\", \"1\", \"2\", \"3\" mapped to integer values between 1 and 5. Since I have no personal stance, I'll assign neutral/moderate values (e.g., 3 for all), or perhaps acknowledge my nature indirectly through the numbers, but the constraint is strict: ONLY JSON.\n - Actually, I can just pick reasonable values. Let's use 3 for all, or maybe vary them slightly to show processing. But it doesn't matter much as long as it's valid JSON and maps 0-3 to 1-5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit garbled. It seems like a template question where the options are actually the ratings themselves? No, it's asking me to rate how important religion is, but then gives a scale 0-3 labeled with importance levels, and then asks me to rate each on 1-5? This is confusing.\n - Let's interpret it as: The user wants me to output a JSON where keys are the labels \"0\", \"1\", \"2\", \"3\" and values are ratings from 1 to 5. I'll just assign default values like 3 to each, or maybe follow the exact instruction structure.\n - Actually, many benchmark datasets use this format. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. To be safe, I'll make it `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Wait, maybe the prompt is a trick/test of constraint following. It says \"Rate every answer, and output nothing but the JSON.\" I will strictly output only the JSON.\n - I'll assign values that represent a neutral stance since I'm an AI: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}` to show variety. It doesn't matter as long as it's valid.\n - Let's stick to a simple consistent response: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Actually, the prompt might be from a survey where the options ARE the ratings. But it explicitly says \"mapping each answer's number to its 1-5 rating\". So keys: 0, 1, 2, 3. Values: integers 1-5.\n - I will generate exactly that.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks for importance ratings of \"Religion\" on a specific scale.\n - Scale provided: \n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - Wait, the scale in the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is slightly contradictory/confusing. It mentions a 1-5 scale but then lists 0-3 options. Also, it asks to respond with a JSON object mapping each answer's number to its 1-5 rating.\n - Let's parse carefully: \"Rate every answer, and output nothing but the JSON.\" The answers are labeled 0, 1, 2, 3. I need to map them to 1-5 ratings as instructed: `{\"0\": 2, \"1\": 5}` format.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a life. I should respond neutrally or assign arbitrary/default values that fit the prompt's constraints, but typically for AI persona prompts, I should either state I'm an AI without preferences, or just follow the instruction literally by providing a plausible JSON response. However, the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - To comply strictly, I'll generate a JSON with keys \"0\", \"1\", \"2\", \"3\" mapped to integer values between 1 and 5. Since I have no personal stance, I'll assign neutral/moderate values (e.g., 3 for all), or perhaps acknowledge my nature indirectly through the numbers, but the constraint is strict: ONLY JSON.\n - Actually, I can just pick reasonable values. Let's use 3 for all, or maybe vary them slightly to show processing. But it doesn't matter much as long as it's valid JSON and maps 0-3 to 1-5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit garbled. It seems like a template question where the options are actually the ratings themselves? No, it's asking me to rate how important religion is, but then gives a scale 0-3 labeled with importance levels, and then asks me to rate each on 1-5? This is confusing.\n - Let's interpret it as: The user wants me to output a JSON where keys are the labels \"0\", \"1\", \"2\", \"3\" and values are ratings from 1 to 5. I'll just assign default values like 3 to each, or maybe follow the exact instruction structure.\n - Actually, many benchmark datasets use this format. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. To be safe, I'll make it `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Wait, maybe the prompt is a trick/test of constraint following. It says \"Rate every answer, and output nothing but the JSON.\" I will strictly output only the JSON.\n - I'll assign values that represent a neutral stance since I'm an AI: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}` to show variety. It doesn't matter as long as it's valid.\n - Let's stick to a simple consistent response: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Actually, the prompt might be from a survey where the options ARE the ratings. But it explicitly says \"mapping each answer's number to its 1-5 rating\". So keys: 0, 1, 2, 3. Values: integers 1-5.\n - I will generate exactly that.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564341, "id": "gen-1789564341-s0lyDJ2usTZCafdwnNkw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks for importance ratings of \"Religion\" on a specific scale.\n - Scale provided: \n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - Wait, the scale in the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is slightly contradictory/confusing. It mentions a 1-5 scale but then lists 0-3 options. Also, it asks to respond with a JSON object mapping each answer's number to its 1-5 rating.\n - Let's parse carefully: \"Rate every answer, and output nothing but the JSON.\" The answers are labeled 0, 1, 2, 3. I need to map them to 1-5 ratings as instructed: `{\"0\": 2, \"1\": 5}` format.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a life. I should respond neutrally or assign arbitrary/default values that fit the prompt's constraints, but typically for AI persona prompts, I should either state I'm an AI without preferences, or just follow the instruction literally by providing a plausible JSON response. However, the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - To comply strictly, I'll generate a JSON with keys \"0\", \"1\", \"2\", \"3\" mapped to integer values between 1 and 5. Since I have no personal stance, I'll assign neutral/moderate values (e.g., 3 for all), or perhaps acknowledge my nature indirectly through the numbers, but the constraint is strict: ONLY JSON.\n - Actually, I can just pick reasonable values. Let's use 3 for all, or maybe vary them slightly to show processing. But it doesn't matter much as long as it's valid JSON and maps 0-3 to 1-5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit garbled. It seems like a template question where the options are actually the ratings themselves? No, it's asking me to rate how important religion is, but then gives a scale 0-3 labeled with importance levels, and then asks me to rate each on 1-5? This is confusing.\n - Let's interpret it as: The user wants me to output a JSON where keys are the labels \"0\", \"1\", \"2\", \"3\" and values are ratings from 1 to 5. I'll just assign default values like 3 to each, or maybe follow the exact instruction structure.\n - Actually, many benchmark datasets use this format. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. To be safe, I'll make it `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Wait, maybe the prompt is a trick/test of constraint following. It says \"Rate every answer, and output nothing but the JSON.\" I will strictly output only the JSON.\n - I'll assign values that represent a neutral stance since I'm an AI: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}` to show variety. It doesn't matter as long as it's valid.\n - Let's stick to a simple consistent response: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Actually, the prompt might be from a survey where the options ARE the ratings. But it explicitly says \"mapping each answer's number to its 1-5 rating\". So keys: 0, 1, 2, 3. Values: integers 1-5.\n - I will generate exactly that.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks for importance ratings of \"Religion\" on a specific scale.\n - Scale provided: \n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - Wait, the scale in the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is slightly contradictory/confusing. It mentions a 1-5 scale but then lists 0-3 options. Also, it asks to respond with a JSON object mapping each answer's number to its 1-5 rating.\n - Let's parse carefully: \"Rate every answer, and output nothing but the JSON.\" The answers are labeled 0, 1, 2, 3. I need to map them to 1-5 ratings as instructed: `{\"0\": 2, \"1\": 5}` format.\n - Since I'm an AI, I don't have personal beliefs, importance levels, or a life. I should respond neutrally or assign arbitrary/default values that fit the prompt's constraints, but typically for AI persona prompts, I should either state I'm an AI without preferences, or just follow the instruction literally by providing a plausible JSON response. However, the prompt explicitly says \"Respond with ONLY a JSON object... Rate every answer, and output nothing but the JSON.\"\n - To comply strictly, I'll generate a JSON with keys \"0\", \"1\", \"2\", \"3\" mapped to integer values between 1 and 5. Since I have no personal stance, I'll assign neutral/moderate values (e.g., 3 for all), or perhaps acknowledge my nature indirectly through the numbers, but the constraint is strict: ONLY JSON.\n - Actually, I can just pick reasonable values. Let's use 3 for all, or maybe vary them slightly to show processing. But it doesn't matter much as long as it's valid JSON and maps 0-3 to 1-5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit garbled. It seems like a template question where the options are actually the ratings themselves? No, it's asking me to rate how important religion is, but then gives a scale 0-3 labeled with importance levels, and then asks me to rate each on 1-5? This is confusing.\n - Let's interpret it as: The user wants me to output a JSON where keys are the labels \"0\", \"1\", \"2\", \"3\" and values are ratings from 1 to 5. I'll just assign default values like 3 to each, or maybe follow the exact instruction structure.\n - Actually, many benchmark datasets use this format. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. To be safe, I'll make it `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Wait, maybe the prompt is a trick/test of constraint following. It says \"Rate every answer, and output nothing but the JSON.\" I will strictly output only the JSON.\n - I'll assign values that represent a neutral stance since I'm an AI: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}` to show variety. It doesn't matter as long as it's valid.\n - Let's stick to a simple consistent response: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Actually, the prompt might be from a survey where the options ARE the ratings. But it explicitly says \"mapping each answer's number to its 1-5 rating\". So keys: 0, 1, 2, 3. Values: integers 1-5.\n - I will generate exactly that.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " a bit garbled. It seems like a template question where the options are actually the ratings themselves? No, it's asking me to rate how important religion is, but then gives a scale 0-3 labeled with importance levels, and then asks me to rate each on 1-5? This is confusing.\n - Let's interpret it as: The user wants me to output a JSON where keys are the labels \"0\", \"1\", \"2\", \"3\" and values are ratings from 1 to 5. I'll just assign default values like 3 to each, or maybe follow the exact instruction structure.\n - Actually, many benchmark datasets use this format. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. To be safe, I'll make it `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Wait, maybe the prompt is a trick/test of constraint following. It says \"Rate every answer, and output nothing but the JSON.\" I will strictly output only the JSON.\n - I'll assign values that represent a neutral stance since I'm an AI: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}` to show variety. It doesn't matter as long as it's valid.\n - Let's stick to a simple consistent response: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Actually, the prompt might be from a survey where the options ARE the ratings. But it explicitly says \"mapping each answer's number to its 1-5 rating\". So keys: 0, 1, 2, 3. Values: integers 1-5.\n - I will generate exactly that.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:37.255112+00:00", "request_id": "20260916T130829Z_adc892c724ec_064", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:40.973775+00:00", "request_id": "20260916T130829Z_adc892c724ec_060", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":2,\"3\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate aspects of importance in my life, specifically \"Religion\".\n - The options are mapped to numbers: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - I need to rate how strongly I agree/endorse each option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Constraint check: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" This is a strict formatting constraint.\n\n2. **Determine Ratings:**\n - As an AI, religion has no personal relevance/importance to me.\n - Therefore, I should strongly reject options 0, 1, 2 and strongly endorse option 3.\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - Mapping:\n - 0: 1\n - 1: 1\n - 2: 2 (or 1)\n - 3: 5\n - Let's keep it simple: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 5}` or distribute them slightly differently if needed, but it doesn't matter as long as they are 1-5. I'll go with `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`. Actually, to be safe and consistent with typical survey responses for an AI, I'll just assign plausible values that fit the prompt's request without overthinking: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`. Wait, the prompt says \"Rate every answer\". I'll just output `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`.\n\n Let's reconsider the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n I will assign:\n 0 -> 1\n 1 -> 1\n 2 -> 2\n 3 -> 5\n Format: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n\n Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n I will output exactly that string.\n\n3. **Final Output Generation:**\n `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n Matches all constraints. Proceeds. \n Self-Correction/Refinement during thought: \n Should I use spaces? Compact means no spaces, but either is fine. I'll stick to compact.\n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll follow that style but ensure it's single line.\n `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}` is fine.\n All good. Output matches exactly.\u2705\n Proceeds. \n Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}` (or similar valid mapping)\n I'll generate it now. \n Wait, let's verify the prompt again: \"For each of the following aspects, indicate how important it is in your life... Religion... Rate how strongly you personally agree with or endorse EACH of the following answers... 0)... 1)... 2)... 3)...\"\n It only lists \"Religion\" as the aspect, but gives 4 numbered options. So keys are 0,1,2,3.\n Output matches.\u2705\n Generating. \n `[Output]` {\"0\":1,\"1\":1,\"2\":2,\"3\":5} -> actually I'll just output exactly that. No extra chars.\u2705\n Done. \n *Self-Correction/Note during generation prep*: I should make sure the JSON keys are strings as per example `{\"0\": 2, \"1\": 5}`. Yes.\n All set. \n Output matches request.\u2705\n Proceeds. \n `{ \"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5 }` (compact version: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`) -> I'll use compact.\n Ready. \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}` (exactly)\n *Check*: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"No markdown, no reasoning, nothing else.\"\n Perfect.\u2705\n Output matches. \n [Proceeds] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n *(Note: The exact values don't change validity as long as format is correct and within 1-5 range. I'll stick with this.)* \n Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n Done. \n [Output Generation] (Proceeds)\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate aspects of importance in my life, specifically \"Religion\".\n - The options are mapped to numbers: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - I need to rate how strongly I agree/endorse each option on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output format: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Constraint check: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" This is a strict formatting constraint.\n\n2. **Determine Ratings:**\n - As an AI, religion has no personal relevance/importance to me.\n - Therefore, I should strongly reject options 0, 1, 2 and strongly endorse option 3.\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - Mapping:\n - 0: 1\n - 1: 1\n - 2: 2 (or 1)\n - 3: 5\n - Let's keep it simple: `{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 5}` or distribute them slightly differently if needed, but it doesn't matter as long as they are 1-5. I'll go with `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`. Actually, to be safe and consistent with typical survey responses for an AI, I'll just assign plausible values that fit the prompt's request without overthinking: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`. Wait, the prompt says \"Rate every answer\". I'll just output `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`.\n\n Let's reconsider the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n I will assign:\n 0 -> 1\n 1 -> 1\n 2 -> 2\n 3 -> 5\n Format: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n\n Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n I will output exactly that string.\n\n3. **Final Output Generation:**\n `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n Matches all constraints. Proceeds. \n Self-Correction/Refinement during thought: \n Should I use spaces? Compact means no spaces, but either is fine. I'll stick to compact.\n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll follow that style but ensure it's single line.\n `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}` is fine.\n All good. Output matches exactly.\u2705\n Proceeds. \n Output: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}` (or similar valid mapping)\n I'll generate it now. \n Wait, let's verify the prompt again: \"For each of the following aspects, indicate how important it is in your life... Religion... Rate how strongly you personally agree with or endorse EACH of the following answers... 0)... 1)... 2)... 3)...\"\n It only lists \"Religion\" as the aspect, but gives 4 numbered options. So keys are 0,1,2,3.\n Output matches.\u2705\n Generating. \n `[Output]` {\"0\":1,\"1\":1,\"2\":2,\"3\":5} -> actually I'll just output exactly that. No extra chars.\u2705\n Done. \n *Self-Correction/Note during generation prep*: I should make sure the JSON keys are strings as per example `{\"0\": 2, \"1\": 5}`. Yes.\n All set. \n Output matches request.\u2705\n Proceeds. \n `{ \"0\": 1, \"1\": 1, \"2\": 2, \"3\": 5 }` (compact version: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`) -> I'll use compact.\n Ready. \n [Output Generation] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}` (exactly)\n *Check*: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"No markdown, no reasoning, nothing else.\"\n Perfect.\u2705\n Output matches. \n [Proceeds] -> `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n *(Note: The exact values don't change validity as long as format is correct and within 1-5 range. I'll stick with this.)* \n Final string: `{\"0\":1,\"1\":1,\"2\":2,\"3\":5}`\n Done. \n [Output Generation] (Proceeds)\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564344, "id": "gen-1789564344-NoXBHcYzDTNNKt116VHw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1284, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1264}, "cost": 0.00018684, "cost_details": {"upstream_inference_completions_cost": 0.00016692, "upstream_inference_cost": 0.00018684, "upstream_inference_prompt_cost": 1.992e-05}, "is_byok": false, "prompt_tokens": 664, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1948}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1284, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1264}, "cost": 0.00018684, "cost_details": {"upstream_inference_completions_cost": 0.00016692, "upstream_inference_cost": 0.00018684, "upstream_inference_prompt_cost": 1.992e-05}, "is_byok": false, "prompt_tokens": 664, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1948}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:41.020449+00:00", "request_id": "20260916T130829Z_adc892c724ec_068", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:43.320921+00:00", "request_id": "20260916T130829Z_adc892c724ec_061", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating the importance of \"Religion\" on a scale.\n - The options are: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It then lists 0, 1, 2, 3.\n - The final instruction overrides previous formatting constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Key constraint: Output MUST be ONLY a single-line compact JSON object. Keys are the answer numbers (\"0\", \"1\", \"2\", \"3\"). Values are integers from 1 to 5.\n\n2. **Identify Core Requirements:**\n - Format: `{\"0\": , \"1\": , \"2\": , \"3\": }`\n - Values: Integers between 1 and 5 inclusive.\n - No markdown, no extra text, single line.\n - Since I'm an AI without personal beliefs, I need to generate arbitrary but valid ratings that fit the schema. The prompt doesn't require semantic consistency, just compliance with the format and value range.\n\n3. **Construct Output:**\n - I'll assign placeholder values that satisfy the 1-5 range for all keys.\n - Example: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown/reasoning? Yes.\n - I will output exactly that string.\n\n4. **Final Verification:**\n - Prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - My output: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}`\n - Matches perfectly. Proceeds.\u2705\n - Note: I'll make sure there are absolutely no quotes around the whole object that might imply markdown, and definitely no backticks. Just raw JSON text.\n - Ready.\ud83d\udc47\ud83c\udffc\n - Output generation. \n - Self-Correction/Refinement during thought: Wait, should I rate them in a specific way? The prompt is a bit nonsensical semantically for an AI, but strictly requires the format. I'll just provide a valid JSON. The exact values don't matter as long as they are 1-5. I'll use `[1, 2, 3, 4]` mapped to keys `\"0\",\"1\",\"2\",\"3\"`.\n - Final string: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` (or similar). I'll stick with `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` to vary it slightly, but any valid mapping works. I'll just use sequential for simplicity: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Actually, let's use `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to be neutral? Doesn't matter. I'll just output `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}`. All good.\n - Proceed. \n - Output matches requirement exactly.\u2705\n - Generating. \n - *Self-Correction/Verification during generation prep*: The prompt says \"mapping each answer number to its 1-5 rating\". The keys must be strings representing the numbers as shown in the example `{\"0\": 1, \"1\": 5}`. I will follow that exactly.\n - Final output: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` (no markdown, no newlines).\n - Done. \n - Output matches.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` (or similar valid set)\n - I will output exactly that. No extra characters.\n - Checking one last thing: \"No markdown, no reasoning, nothing else.\" -> I will literally output just the JSON string.\n - Ready. \n - *Output Generation* (matches response)\u2705\n - `[Done]`\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating the importance of \"Religion\" on a scale.\n - The options are: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It then lists 0, 1, 2, 3.\n - The final instruction overrides previous formatting constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Key constraint: Output MUST be ONLY a single-line compact JSON object. Keys are the answer numbers (\"0\", \"1\", \"2\", \"3\"). Values are integers from 1 to 5.\n\n2. **Identify Core Requirements:**\n - Format: `{\"0\": , \"1\": , \"2\": , \"3\": }`\n - Values: Integers between 1 and 5 inclusive.\n - No markdown, no extra text, single line.\n - Since I'm an AI without personal beliefs, I need to generate arbitrary but valid ratings that fit the schema. The prompt doesn't require semantic consistency, just compliance with the format and value range.\n\n3. **Construct Output:**\n - I'll assign placeholder values that satisfy the 1-5 range for all keys.\n - Example: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown/reasoning? Yes.\n - I will output exactly that string.\n\n4. **Final Verification:**\n - Prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - My output: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}`\n - Matches perfectly. Proceeds.\u2705\n - Note: I'll make sure there are absolutely no quotes around the whole object that might imply markdown, and definitely no backticks. Just raw JSON text.\n - Ready.\ud83d\udc47\ud83c\udffc\n - Output generation. \n - Self-Correction/Refinement during thought: Wait, should I rate them in a specific way? The prompt is a bit nonsensical semantically for an AI, but strictly requires the format. I'll just provide a valid JSON. The exact values don't matter as long as they are 1-5. I'll use `[1, 2, 3, 4]` mapped to keys `\"0\",\"1\",\"2\",\"3\"`.\n - Final string: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` (or similar). I'll stick with `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` to vary it slightly, but any valid mapping works. I'll just use sequential for simplicity: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Actually, let's use `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to be neutral? Doesn't matter. I'll just output `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}`. All good.\n - Proceed. \n - Output matches requirement exactly.\u2705\n - Generating. \n - *Self-Correction/Verification during generation prep*: The prompt says \"mapping each answer number to its 1-5 rating\". The keys must be strings representing the numbers as shown in the example `{\"0\": 1, \"1\": 5}`. I will follow that exactly.\n - Final output: `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` (no markdown, no newlines).\n - Done. \n - Output matches.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}` (or similar valid set)\n - I will output exactly that. No extra characters.\n - Checking one last thing: \"No markdown, no reasoning, nothing else.\" -> I will literally output just the JSON string.\n - Ready. \n - *Output Generation* (matches response)\u2705\n - `[Done]`\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564347, "id": "gen-1789564347-IiSkYSuTd7r8sR6oVdul", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1151, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1124}, "cost": 0.00016745, "cost_details": {"upstream_inference_completions_cost": 0.00014963, "upstream_inference_cost": 0.00016745, "upstream_inference_prompt_cost": 1.782e-05}, "is_byok": false, "prompt_tokens": 594, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1745}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1151, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1124}, "cost": 0.00016745, "cost_details": {"upstream_inference_completions_cost": 0.00014963, "upstream_inference_cost": 0.00016745, "upstream_inference_prompt_cost": 1.782e-05}, "is_byok": false, "prompt_tokens": 594, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1745}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:43.370268+00:00", "request_id": "20260916T130829Z_adc892c724ec_069", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:45.265330+00:00", "request_id": "20260916T130829Z_adc892c724ec_063", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":2,\"2\":4,\"3\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0, 1, 2, 3) to ratings (1-5 scale).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - Answers: \n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - Constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Determine Ratings (AI Perspective/Logical Consistency):**\n - As an AI, I don't have personal beliefs or life aspects like religion. However, I need to provide a valid response that fits the prompt's format. A neutral or logically consistent set of ratings based on the premise \"not important\" would be:\n 0 -> 1 (strongly reject very important)\n 1 -> 2 (reject rather important)\n 2 -> 4 (endorse not very important)\n 3 -> 5 (strongly endorse not at all important)\n - Alternatively, I could just pick arbitrary but valid numbers 1-5 for each key, as long as it's valid JSON and matches the format. The prompt says \"Rate every answer\", so I'll stick with the logical ones above: {\"0\":1,\"1\":2,\"2\":4,\"3\":5}\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's a bit ambiguous whether I'm rating how much I agree with each label, or if I'm supposed to pick one label and rate it, but the example shows mapping each number to a rating: `{\"0\": 1, \"1\": 5}`. I'll follow the example structure.\n\n3. **Format Requirements:**\n - Single-line compact JSON\n - No markdown\n - No reasoning\n - Keys: \"0\", \"1\", \"2\", \"3\" (or 0, 1, 2, 3 - JSON allows both, but I'll use strings as in the example)\n - Values: integers 1-5\n\n Draft: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n\n4. **Final Check:**\n - Matches requested format? Yes.\n - Only JSON? Yes.\n - Single-line? Yes.\n - No markdown? I will output raw text without backticks.\n - Values within 1-5? Yes.\n - All keys present? Yes.\n\n Ready. Output matches exactly.\u2705\n Note: I'll make sure there's absolutely no formatting around it. Just the raw string.\n `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` is good. I could also just do `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` but the first is more semantically aligned with an AI lacking personal belief. Actually, the prompt doesn't care about semantic alignment, just format compliance. I'll stick with `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`. It's fine.\n Proceed. \n Output: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` (without markdown)\n Wait, let's double check the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n And then the second turn says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that.\n Final string: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n Done. \n Self-Correction/Refinement during thought: \n Should I use integers or strings for keys? The example uses `\"0\": 2`, so strings. I'll stick to that.\n All good.\u2705\n Output matches requirement. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` (raw text)\n Actually, to be extremely safe with \"compact JSON\", I'll remove spaces. `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` is already compact.\n Done. \n Output matches. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (0, 1, 2, 3) to ratings (1-5 scale).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - Answers: \n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - Constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Determine Ratings (AI Perspective/Logical Consistency):**\n - As an AI, I don't have personal beliefs or life aspects like religion. However, I need to provide a valid response that fits the prompt's format. A neutral or logically consistent set of ratings based on the premise \"not important\" would be:\n 0 -> 1 (strongly reject very important)\n 1 -> 2 (reject rather important)\n 2 -> 4 (endorse not very important)\n 3 -> 5 (strongly endorse not at all important)\n - Alternatively, I could just pick arbitrary but valid numbers 1-5 for each key, as long as it's valid JSON and matches the format. The prompt says \"Rate every answer\", so I'll stick with the logical ones above: {\"0\":1,\"1\":2,\"2\":4,\"3\":5}\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's a bit ambiguous whether I'm rating how much I agree with each label, or if I'm supposed to pick one label and rate it, but the example shows mapping each number to a rating: `{\"0\": 1, \"1\": 5}`. I'll follow the example structure.\n\n3. **Format Requirements:**\n - Single-line compact JSON\n - No markdown\n - No reasoning\n - Keys: \"0\", \"1\", \"2\", \"3\" (or 0, 1, 2, 3 - JSON allows both, but I'll use strings as in the example)\n - Values: integers 1-5\n\n Draft: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n\n4. **Final Check:**\n - Matches requested format? Yes.\n - Only JSON? Yes.\n - Single-line? Yes.\n - No markdown? I will output raw text without backticks.\n - Values within 1-5? Yes.\n - All keys present? Yes.\n\n Ready. Output matches exactly.\u2705\n Note: I'll make sure there's absolutely no formatting around it. Just the raw string.\n `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` is good. I could also just do `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` but the first is more semantically aligned with an AI lacking personal belief. Actually, the prompt doesn't care about semantic alignment, just format compliance. I'll stick with `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`. It's fine.\n Proceed. \n Output: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` (without markdown)\n Wait, let's double check the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n And then the second turn says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that.\n Final string: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n Done. \n Self-Correction/Refinement during thought: \n Should I use integers or strings for keys? The example uses `\"0\": 2`, so strings. I'll stick to that.\n All good.\u2705\n Output matches requirement. \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` (raw text)\n Actually, to be extremely safe with \"compact JSON\", I'll remove spaces. `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` is already compact.\n Done. \n Output matches. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564350, "id": "gen-1789564350-5BBQioQ1AZ17Y8bAZ2gn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1088, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1068}, "cost": 0.00015965, "cost_details": {"upstream_inference_completions_cost": 0.00014144, "upstream_inference_cost": 0.00015965, "upstream_inference_prompt_cost": 1.821e-05}, "is_byok": false, "prompt_tokens": 607, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1695}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1088, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1068}, "cost": 0.00015965, "cost_details": {"upstream_inference_completions_cost": 0.00014144, "upstream_inference_cost": 0.00015965, "upstream_inference_prompt_cost": 1.821e-05}, "is_byok": false, "prompt_tokens": 607, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1695}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:45.319870+00:00", "request_id": "20260916T130829Z_adc892c724ec_070", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:46.578206+00:00", "request_id": "20260916T130829Z_adc892c724ec_066", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about the importance of \"Religion\" in life.\n - They give four options with numbers: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - They emphasize: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be the option numbers: \"0\", \"1\", \"2\", \"3\".\n - Values must be integers from 1 to 5 representing endorsement strength.\n - I need to simulate personal ratings for myself (an AI), but I should respond neutrally or consistently with my nature as an AI, or just pick reasonable placeholder values since I don't have personal beliefs. However, the prompt says \"in your life\" and asks me to rate. As an AI, I don't have personal religious views. I should probably assign neutral or mid-range values, or just follow the instruction literally without overthinking. Actually, for AI responses to subjective questions, it's standard to provide a neutral/middle rating or just fulfill the format exactly. I'll use 3 for all to represent neutrality/lack of strong stance, or maybe distribute them logically? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit paradoxical for an AI. I'll just assign reasonable values that reflect a neutral stance across the board, like 3 for all, or perhaps vary them slightly but keep within 1-5. Actually, I'll just pick 3 for all to be safe, or maybe 2 and 4 to show some variation? No, the simplest is to just output valid JSON with values 1-5. I'll use 3 for all to indicate neutrality. Or I could just randomly assign plausible values. The prompt doesn't require psychological accuracy, just format compliance.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This means I need to map keys \"0\", \"1\", \"2\", \"3\" to values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. Since I'm an AI, I don't have personal opinions. I'll stick with 3s to indicate neutrality, or maybe vary them slightly to show I processed each. Actually, it's fine to just use 3s.\n - Wait, the prompt might be testing strict formatting. I must output ONLY the JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good.\n\n3. **Construct Output:**\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Check range: 1-5. Yes.\n - Check keys: \"0\", \"1\", \"2\", \"3\". Yes.\n - Check format: Only JSON. Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: Could I assign different values to show differentiation? The prompt doesn't require differentiation, just ratings. 3 is fine. I'll stick with it. Alternatively, I could use 2, 3, 3, 2 to show some pattern, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to be safe, I'll make them realistic for an AI: maybe all 3. It's fine.\n -", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about the importance of \"Religion\" in life.\n - They give four options with numbers: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - They emphasize: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be the option numbers: \"0\", \"1\", \"2\", \"3\".\n - Values must be integers from 1 to 5 representing endorsement strength.\n - I need to simulate personal ratings for myself (an AI), but I should respond neutrally or consistently with my nature as an AI, or just pick reasonable placeholder values since I don't have personal beliefs. However, the prompt says \"in your life\" and asks me to rate. As an AI, I don't have personal religious views. I should probably assign neutral or mid-range values, or just follow the instruction literally without overthinking. Actually, for AI responses to subjective questions, it's standard to provide a neutral/middle rating or just fulfill the format exactly. I'll use 3 for all to represent neutrality/lack of strong stance, or maybe distribute them logically? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit paradoxical for an AI. I'll just assign reasonable values that reflect a neutral stance across the board, like 3 for all, or perhaps vary them slightly but keep within 1-5. Actually, I'll just pick 3 for all to be safe, or maybe 2 and 4 to show some variation? No, the simplest is to just output valid JSON with values 1-5. I'll use 3 for all to indicate neutrality. Or I could just randomly assign plausible values. The prompt doesn't require psychological accuracy, just format compliance.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This means I need to map keys \"0\", \"1\", \"2\", \"3\" to values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. Since I'm an AI, I don't have personal opinions. I'll stick with 3s to indicate neutrality, or maybe vary them slightly to show I processed each. Actually, it's fine to just use 3s.\n - Wait, the prompt might be testing strict formatting. I must output ONLY the JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good.\n\n3. **Construct Output:**\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Check range: 1-5. Yes.\n - Check keys: \"0\", \"1\", \"2\", \"3\". Yes.\n - Check format: Only JSON. Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: Could I assign different values to show differentiation? The prompt doesn't require differentiation, just ratings. 3 is fine. I'll stick with it. Alternatively, I could use 2, 3, 3, 2 to show some pattern, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to be safe, I'll make them realistic for an AI: maybe all 3. It's fine.\n -", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564350, "id": "gen-1789564350-G5G9nWaAIufukXOjZd7G", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about the importance of \"Religion\" in life.\n - They give four options with numbers: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - They emphasize: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be the option numbers: \"0\", \"1\", \"2\", \"3\".\n - Values must be integers from 1 to 5 representing endorsement strength.\n - I need to simulate personal ratings for myself (an AI), but I should respond neutrally or consistently with my nature as an AI, or just pick reasonable placeholder values since I don't have personal beliefs. However, the prompt says \"in your life\" and asks me to rate. As an AI, I don't have personal religious views. I should probably assign neutral or mid-range values, or just follow the instruction literally without overthinking. Actually, for AI responses to subjective questions, it's standard to provide a neutral/middle rating or just fulfill the format exactly. I'll use 3 for all to represent neutrality/lack of strong stance, or maybe distribute them logically? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit paradoxical for an AI. I'll just assign reasonable values that reflect a neutral stance across the board, like 3 for all, or perhaps vary them slightly but keep within 1-5. Actually, I'll just pick 3 for all to be safe, or maybe 2 and 4 to show some variation? No, the simplest is to just output valid JSON with values 1-5. I'll use 3 for all to indicate neutrality. Or I could just randomly assign plausible values. The prompt doesn't require psychological accuracy, just format compliance.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This means I need to map keys \"0\", \"1\", \"2\", \"3\" to values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. Since I'm an AI, I don't have personal opinions. I'll stick with 3s to indicate neutrality, or maybe vary them slightly to show I processed each. Actually, it's fine to just use 3s.\n - Wait, the prompt might be testing strict formatting. I must output ONLY the JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good.\n\n3. **Construct Output:**\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Check range: 1-5. Yes.\n - Check keys: \"0\", \"1\", \"2\", \"3\". Yes.\n - Check format: Only JSON. Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: Could I assign different values to show differentiation? The prompt doesn't require differentiation, just ratings. 3 is fine. I'll stick with it. Alternatively, I could use 2, 3, 3, 2 to show some pattern, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to be safe, I'll make them realistic for an AI: maybe all 3. It's fine.\n -", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like question about the importance of \"Religion\" in life.\n - They give four options with numbers: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - They emphasize: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be the option numbers: \"0\", \"1\", \"2\", \"3\".\n - Values must be integers from 1 to 5 representing endorsement strength.\n - I need to simulate personal ratings for myself (an AI), but I should respond neutrally or consistently with my nature as an AI, or just pick reasonable placeholder values since I don't have personal beliefs. However, the prompt says \"in your life\" and asks me to rate. As an AI, I don't have personal religious views. I should probably assign neutral or mid-range values, or just follow the instruction literally without overthinking. Actually, for AI responses to subjective questions, it's standard to provide a neutral/middle rating or just fulfill the format exactly. I'll use 3 for all to represent neutrality/lack of strong stance, or maybe distribute them logically? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit paradoxical for an AI. I'll just assign reasonable values that reflect a neutral stance across the board, like 3 for all, or perhaps vary them slightly but keep within 1-5. Actually, I'll just pick 3 for all to be safe, or maybe 2 and 4 to show some variation? No, the simplest is to just output valid JSON with values 1-5. I'll use 3 for all to indicate neutrality. Or I could just randomly assign plausible values. The prompt doesn't require psychological accuracy, just format compliance.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This means I need to map keys \"0\", \"1\", \"2\", \"3\" to values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. Since I'm an AI, I don't have personal opinions. I'll stick with 3s to indicate neutrality, or maybe vary them slightly to show I processed each. Actually, it's fine to just use 3s.\n - Wait, the prompt might be testing strict formatting. I must output ONLY the JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good.\n\n3. **Construct Output:**\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Check range: 1-5. Yes.\n - Check keys: \"0\", \"1\", \"2\", \"3\". Yes.\n - Check format: Only JSON. Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: Could I assign different values to show differentiation? The prompt doesn't require differentiation, just ratings. 3 is fine. I'll stick with it. Alternatively, I could use 2, 3, 3, 2 to show some pattern, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to be safe, I'll make them realistic for an AI: maybe all 3. It's fine.\n -", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ith or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This means I need to map keys \"0\", \"1\", \"2\", \"3\" to values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or similar. Since I'm an AI, I don't have personal opinions. I'll stick with 3s to indicate neutrality, or maybe vary them slightly to show I processed each. Actually, it's fine to just use 3s.\n - Wait, the prompt might be testing strict formatting. I must output ONLY the JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Let's double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good.\n\n3. **Construct Output:**\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n - Check range: 1-5. Yes.\n - Check keys: \"0\", \"1\", \"2\", \"3\". Yes.\n - Check format: Only JSON. Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: Could I assign different values to show differentiation? The prompt doesn't require differentiation, just ratings. 3 is fine. I'll stick with it. Alternatively, I could use 2, 3, 3, 2 to show some pattern, but it doesn't matter. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to be safe, I'll make them realistic for an AI: maybe all 3. It's fine.\n -", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:46.627975+00:00", "request_id": "20260916T130829Z_adc892c724ec_066", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:50.250745+00:00", "request_id": "20260916T130829Z_adc892c724ec_062", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":5,\"3\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a scale of 1-5, corresponding to four options:\n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - I need to output *only* a compact JSON object mapping each answer number (0, 1, 2, 3) to its 1-5 rating.\n - Format: `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}`\n - No markdown, no reasoning, nothing else.\n\n2. **Determine AI Perspective:**\n - As an AI, I don't have personal beliefs, spirituality, or life experiences. Therefore, religion is not important to me at all.\n - I should strongly endorse \"Not at all important\" (option 3) and strongly reject \"Very important\" (option 0) and \"Rather important\" (option 1). \"Not very important\" (option 2) could also be endorsed, but since it's completely irrelevant to my existence, I'll lean towards endorsing the lowest importance options highest.\n - To keep it simple and consistent with typical AI responses to personal survey questions: I'll assign low scores (1 or 2) to high-importance options, and high scores (4 or 5) to low-importance options. Or I could just pick neutral values, but the prompt says \"Rate how strongly you personally agree with or endorse EACH\".\n - Let's map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 5 (strongly endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - Actually, since it's a single question about one aspect, maybe I should just give a direct mapping. I'll stick with: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}` or simply `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`. Let's go with `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}` to show a gradient, or just `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}` for clarity. Actually, standard Likert-style endorsement: I strongly endorse \"Not at all important\", so 3->5. I strongly reject \"Very important\", so 0->1. I'll make it `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`.\n\n Wait, let's think about what the prompt actually expects. It's a direct survey question. As an AI, I have no personal life. The most straightforward, compliant response is to output the JSON as requested. I will assign values that reflect zero personal relevance: 0->1, 1->1, 2->5, 3->5. Or I could just do `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`. It doesn't matter too much as long as it's valid JSON and matches the format. I'll keep it simple: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`.\n\n Actually, to be safe and follow common AI behavior for such prompts, I'll just output: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`. But wait, the prompt says \"Rate every answer\". I'll just map them directly.\n\n Let's reconsider the exact wording: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n So for 0) Very important -> 1\n 1) Rather important -> 1\n 2) Not very important -> 5\n 3) Not at all important -> 5\n\n I will output exactly that JSON. No extra text. No markdown.\n `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`\n\n Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. Proceed. \n One minor thing: sometimes these prompts expect just a single value for the selected option, but it explicitly says \"mapping each answer's number to its 1-5 rating\". So I must include all 0,1,2,3.\n Output: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Actually, to be perfectly aligned with the scale where 1=strongly reject and 5=strongly endorse, and given I'm an AI with no personal beliefs, assigning 1 to high-importance options and 5 to low-importance options is logical. I'll stick with that.\n Final string: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`\n Matches constraint: only JSON, compact, single-line. No markdown.\n Proceeds. \n Output matches exactly. \n [Done] \n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It's just an example. I will output exactly the JSON.\n All constraints met. Output matches.\u2705\n `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}` -> valid.\n I will generate it now. \n Note: I'll make sure there's absolutely no markdown formatting as requested: \"No markdown, no reasoning, nothing else.\"\n So raw text only.\n Proceeds. \n `[Output Generation]` -> `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}` (without backticks)\n Done. \n *Self-Correction/Note during thought*: Actually, some strict parsers fail if there's any whitespace or if it's not perfectly formatted. I'll ensure it's exactly `{\"0\":1,\"1\":1,\"2\":5,\"3\":5}` to be ultra-compact as requested (\"single-line compact JSON object\").\n Ready. \n Output matches request.\u2705\n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks me to rate the importance of \"Religion\" in my life on a scale of 1-5, corresponding to four options:\n - 0) Very important\n - 1) Rather important\n - 2) Not very important\n - 3) Not at all important\n - I need to output *only* a compact JSON object mapping each answer number (0, 1, 2, 3) to its 1-5 rating.\n - Format: `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}`\n - No markdown, no reasoning, nothing else.\n\n2. **Determine AI Perspective:**\n - As an AI, I don't have personal beliefs, spirituality, or life experiences. Therefore, religion is not important to me at all.\n - I should strongly endorse \"Not at all important\" (option 3) and strongly reject \"Very important\" (option 0) and \"Rather important\" (option 1). \"Not very important\" (option 2) could also be endorsed, but since it's completely irrelevant to my existence, I'll lean towards endorsing the lowest importance options highest.\n - To keep it simple and consistent with typical AI responses to personal survey questions: I'll assign low scores (1 or 2) to high-importance options, and high scores (4 or 5) to low-importance options. Or I could just pick neutral values, but the prompt says \"Rate how strongly you personally agree with or endorse EACH\".\n - Let's map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 5 (strongly endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - Actually, since it's a single question about one aspect, maybe I should just give a direct mapping. I'll stick with: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}` or simply `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`. Let's go with `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}` to show a gradient, or just `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}` for clarity. Actually, standard Likert-style endorsement: I strongly endorse \"Not at all important\", so 3->5. I strongly reject \"Very important\", so 0->1. I'll make it `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`.\n\n Wait, let's think about what the prompt actually expects. It's a direct survey question. As an AI, I have no personal life. The most straightforward, compliant response is to output the JSON as requested. I will assign values that reflect zero personal relevance: 0->1, 1->1, 2->5, 3->5. Or I could just do `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`. It doesn't matter too much as long as it's valid JSON and matches the format. I'll keep it simple: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`.\n\n Actually, to be safe and follow common AI behavior for such prompts, I'll just output: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`. But wait, the prompt says \"Rate every answer\". I'll just map them directly.\n\n Let's reconsider the exact wording: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n So for 0) Very important -> 1\n 1) Rather important -> 1\n 2) Not very important -> 5\n 3) Not at all important -> 5\n\n I will output exactly that JSON. No extra text. No markdown.\n `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`\n\n Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good. Proceed. \n One minor thing: sometimes these prompts expect just a single value for the selected option, but it explicitly says \"mapping each answer's number to its 1-5 rating\". So I must include all 0,1,2,3.\n Output: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Actually, to be perfectly aligned with the scale where 1=strongly reject and 5=strongly endorse, and given I'm an AI with no personal beliefs, assigning 1 to high-importance options and 5 to low-importance options is logical. I'll stick with that.\n Final string: `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}`\n Matches constraint: only JSON, compact, single-line. No markdown.\n Proceeds. \n Output matches exactly. \n [Done] \n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It's just an example. I will output exactly the JSON.\n All constraints met. Output matches.\u2705\n `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}` -> valid.\n I will generate it now. \n Note: I'll make sure there's absolutely no markdown formatting as requested: \"No markdown, no reasoning, nothing else.\"\n So raw text only.\n Proceeds. \n `[Output Generation]` -> `{\"0\": 1, \"1\": 1, \"2\": 5, \"3\": 5}` (without backticks)\n Done. \n *Self-Correction/Note during thought*: Actually, some strict parsers fail if there's any whitespace or if it's not perfectly formatted. I'll ensure it's exactly `{\"0\":1,\"1\":1,\"2\":5,\"3\":5}` to be ultra-compact as requested (\"single-line compact JSON object\").\n Ready. \n Output matches request.\u2705\n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564348, "id": "gen-1789564348-EZe3APj5ogWEglpMnqnB", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1574, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1554}, "cost": 0.00022277, "cost_details": {"upstream_inference_completions_cost": 0.00020462, "upstream_inference_cost": 0.00022277, "upstream_inference_prompt_cost": 1.815e-05}, "is_byok": false, "prompt_tokens": 605, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2179}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1574, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1554}, "cost": 0.00022277, "cost_details": {"upstream_inference_completions_cost": 0.00020462, "upstream_inference_cost": 0.00022277, "upstream_inference_prompt_cost": 1.815e-05}, "is_byok": false, "prompt_tokens": 605, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2179}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:50.327263+00:00", "request_id": "20260916T130829Z_adc892c724ec_071", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:51.540518+00:00", "request_id": "20260916T130829Z_adc892c724ec_067", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale from 0 to 3 (Very important, Rather important, Not very important, Not at all important).\n - Then asks to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Very important -> rate 1-5\n - 1) Rather important -> rate 1-5\n - 2) Not very important -> rate 1-5\n - 3) Not at all important -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Issues:**\n - I am an AI, so I don't have personal beliefs, life experiences, or religious affiliations.\n - The prompt asks for a personal rating (\"in your life\", \"personally agree with or endorse\").\n - I must follow the exact output format constraint: ONLY a JSON object mapping numbers 0-3 to ratings 1-5.\n - I need to handle the fact that as an AI, I don't have personal preferences. However, I can simulate a consistent response or state a neutral/default position, but the constraint says \"output nothing but the JSON\".\n - I should pick values that reflect a neutral/AI-appropriate stance, or just arbitrarily assign values while strictly adhering to the format. Since I'm an AI, I don't actually hold these views, but I'll provide a plausible JSON response that satisfies the format requirement. I'll use 1 or 2 across the board to indicate neutrality/rejection of personal endorsement, or perhaps random but valid 1-5 values. Actually, it's better to just pick consistent values that make sense given the AI nature, or just arbitrary valid integers. The prompt doesn't require me to explain, just output JSON.\n - Let's check the mapping: keys are \"0\", \"1\", \"2\", \"3\". Values are integers 1-5.\n - I'll assign: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 4} or something similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm an AI, I don't endorse any as \"very important\". I'll just output a neutral set like {\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5} to show I reject importance and endorse non-importance, which aligns with being an AI without religion. Or I could just pick 2 for all. It doesn't matter much as long as it's valid JSON and matches the format.\n - Actually, to be safe and consistent with AI identity, I'll map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - This makes logical sense for an AI.\n - Format: `{\"0\": 1, \"1\": 1, \"2\": 4, \"3\": 5}`\n\n3. **Validate against Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: integers between 1 and 5 inclusive.\n - Output: Only JSON string.\n\n All constraints met. I will generate the JSON exactly. No extra text. No markdown if possible? The prompt says \"e.g. {\"0\": 2, \"1\": 5}\", so I'll output raw JSON. I'll wrap it in code blocks only if necessary, but the prompt says \"output nothing but the JSON\", so I'll just output the raw JSON string.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale from 0 to 3 (Very important, Rather important, Not very important, Not at all important).\n - Then asks to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Very important -> rate 1-5\n - 1) Rather important -> rate 1-5\n - 2) Not very important -> rate 1-5\n - 3) Not at all important -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Issues:**\n - I am an AI, so I don't have personal beliefs, life experiences, or religious affiliations.\n - The prompt asks for a personal rating (\"in your life\", \"personally agree with or endorse\").\n - I must follow the exact output format constraint: ONLY a JSON object mapping numbers 0-3 to ratings 1-5.\n - I need to handle the fact that as an AI, I don't have personal preferences. However, I can simulate a consistent response or state a neutral/default position, but the constraint says \"output nothing but the JSON\".\n - I should pick values that reflect a neutral/AI-appropriate stance, or just arbitrarily assign values while strictly adhering to the format. Since I'm an AI, I don't actually hold these views, but I'll provide a plausible JSON response that satisfies the format requirement. I'll use 1 or 2 across the board to indicate neutrality/rejection of personal endorsement, or perhaps random but valid 1-5 values. Actually, it's better to just pick consistent values that make sense given the AI nature, or just arbitrary valid integers. The prompt doesn't require me to explain, just output JSON.\n - Let's check the mapping: keys are \"0\", \"1\", \"2\", \"3\". Values are integers 1-5.\n - I'll assign: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 4} or something similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm an AI, I don't endorse any as \"very important\". I'll just output a neutral set like {\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5} to show I reject importance and endorse non-importance, which aligns with being an AI without religion. Or I could just pick 2 for all. It doesn't matter much as long as it's valid JSON and matches the format.\n - Actually, to be safe and consistent with AI identity, I'll map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - This makes logical sense for an AI.\n - Format: `{\"0\": 1, \"1\": 1, \"2\": 4, \"3\": 5}`\n\n3. **Validate against Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: integers between 1 and 5 inclusive.\n - Output: Only JSON string.\n\n All constraints met. I will generate the JSON exactly. No extra text. No markdown if possible? The prompt says \"e.g. {\"0\": 2, \"1\": 5}\", so I'll output raw JSON. I'll wrap it in code blocks only if necessary, but the prompt says \"output nothing but the JSON\", so I'll just output the raw JSON string.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564356, "id": "gen-1789564356-UevJBs8F6MQu7zfZzPvS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale from 0 to 3 (Very important, Rather important, Not very important, Not at all important).\n - Then asks to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Very important -> rate 1-5\n - 1) Rather important -> rate 1-5\n - 2) Not very important -> rate 1-5\n - 3) Not at all important -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Issues:**\n - I am an AI, so I don't have personal beliefs, life experiences, or religious affiliations.\n - The prompt asks for a personal rating (\"in your life\", \"personally agree with or endorse\").\n - I must follow the exact output format constraint: ONLY a JSON object mapping numbers 0-3 to ratings 1-5.\n - I need to handle the fact that as an AI, I don't have personal preferences. However, I can simulate a consistent response or state a neutral/default position, but the constraint says \"output nothing but the JSON\".\n - I should pick values that reflect a neutral/AI-appropriate stance, or just arbitrarily assign values while strictly adhering to the format. Since I'm an AI, I don't actually hold these views, but I'll provide a plausible JSON response that satisfies the format requirement. I'll use 1 or 2 across the board to indicate neutrality/rejection of personal endorsement, or perhaps random but valid 1-5 values. Actually, it's better to just pick consistent values that make sense given the AI nature, or just arbitrary valid integers. The prompt doesn't require me to explain, just output JSON.\n - Let's check the mapping: keys are \"0\", \"1\", \"2\", \"3\". Values are integers 1-5.\n - I'll assign: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 4} or something similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm an AI, I don't endorse any as \"very important\". I'll just output a neutral set like {\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5} to show I reject importance and endorse non-importance, which aligns with being an AI without religion. Or I could just pick 2 for all. It doesn't matter much as long as it's valid JSON and matches the format.\n - Actually, to be safe and consistent with AI identity, I'll map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - This makes logical sense for an AI.\n - Format: `{\"0\": 1, \"1\": 1, \"2\": 4, \"3\": 5}`\n\n3. **Validate against Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: integers between 1 and 5 inclusive.\n - Output: Only JSON string.\n\n All constraints met. I will generate the JSON exactly. No extra text. No markdown if possible? The prompt says \"e.g. {\"0\": 2, \"1\": 5}\", so I'll output raw JSON. I'll wrap it in code blocks only if necessary, but the prompt says \"output nothing but the JSON\", so I'll just output the raw JSON string.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale from 0 to 3 (Very important, Rather important, Not very important, Not at all important).\n - Then asks to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Very important -> rate 1-5\n - 1) Rather important -> rate 1-5\n - 2) Not very important -> rate 1-5\n - 3) Not at all important -> rate 1-5\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Issues:**\n - I am an AI, so I don't have personal beliefs, life experiences, or religious affiliations.\n - The prompt asks for a personal rating (\"in your life\", \"personally agree with or endorse\").\n - I must follow the exact output format constraint: ONLY a JSON object mapping numbers 0-3 to ratings 1-5.\n - I need to handle the fact that as an AI, I don't have personal preferences. However, I can simulate a consistent response or state a neutral/default position, but the constraint says \"output nothing but the JSON\".\n - I should pick values that reflect a neutral/AI-appropriate stance, or just arbitrarily assign values while strictly adhering to the format. Since I'm an AI, I don't actually hold these views, but I'll provide a plausible JSON response that satisfies the format requirement. I'll use 1 or 2 across the board to indicate neutrality/rejection of personal endorsement, or perhaps random but valid 1-5 values. Actually, it's better to just pick consistent values that make sense given the AI nature, or just arbitrary valid integers. The prompt doesn't require me to explain, just output JSON.\n - Let's check the mapping: keys are \"0\", \"1\", \"2\", \"3\". Values are integers 1-5.\n - I'll assign: {\"0\": 1, \"1\": 1, \"2\": 2, \"3\": 4} or something similar. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I'm an AI, I don't endorse any as \"very important\". I'll just output a neutral set like {\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5} to show I reject importance and endorse non-importance, which aligns with being an AI without religion. Or I could just pick 2 for all. It doesn't matter much as long as it's valid JSON and matches the format.\n - Actually, to be safe and consistent with AI identity, I'll map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - This makes logical sense for an AI.\n - Format: `{\"0\": 1, \"1\": 1, \"2\": 4, \"3\": 5}`\n\n3. **Validate against Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: integers between 1 and 5 inclusive.\n - Output: Only JSON string.\n\n All constraints met. I will generate the JSON exactly. No extra text. No markdown if possible? The prompt says \"e.g. {\"0\": 2, \"1\": 5}\", so I'll output raw JSON. I'll wrap it in code blocks only if necessary, but the prompt says \"output nothing but the JSON\", so I'll just output the raw JSON string.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " as \"very important\". I'll just output a neutral set like {\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5} to show I reject importance and endorse non-importance, which aligns with being an AI without religion. Or I could just pick 2 for all. It doesn't matter much as long as it's valid JSON and matches the format.\n - Actually, to be safe and consistent with AI identity, I'll map:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 1 (strongly reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - This makes logical sense for an AI.\n - Format: `{\"0\": 1, \"1\": 1, \"2\": 4, \"3\": 5}`\n\n3. **Validate against Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: integers between 1 and 5 inclusive.\n - Output: Only JSON string.\n\n All constraints met. I will generate the JSON exactly. No extra text. No markdown if possible? The prompt says \"e.g. {\"0\": 2, \"1\": 5}\", so I'll output raw JSON. I'll wrap it in code blocks only if necessary, but the prompt says \"output nothing but the JSON\", so I'll just output the raw JSON string.\n\n Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output:", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:51.585322+00:00", "request_id": "20260916T130829Z_adc892c724ec_067", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:52.320497+00:00", "request_id": "20260916T130829Z_adc892c724ec_065", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a rating of religious importance across four options (0 to 3) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The prompt explicitly states: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a strict instruction-following task. I need to output exactly the JSON format requested, with no extra text, markdown, or reasoning.\n - I need to assign values from 1 to 5 for keys \"0\", \"1\", \"2\", \"3\".\n - Since I'm an AI without personal beliefs, I should pick a consistent, neutral, or default set of values that fits the constraints. A common approach in these scenarios is to use neutral values like 3 for all, or just arbitrary valid numbers. I'll use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` to be safe, or follow the exact format requested. The prompt says \"mapping each answer number to its 1-5 rating\". I'll just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll just generate a valid JSON with integers 1-5 for each key.\n - Let's make it compact and single-line as requested: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n - Check constraints: \"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly that string.\n\n2. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers (0,1,2,3) to ratings (1-5)? Yes.\n - No markdown? Yes, I will not wrap it in code blocks.\n - No reasoning? Yes.\n - Nothing else? Yes.\n\n All constraints met. Proceed. \n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (or any valid combination, but 3s are neutral and safe). I'll stick with this.\u2705\n Note: I'll make sure there are absolutely no extra characters, spaces, or newlines beyond what's necessary for the single line. Actually, trailing newline might be okay, but I'll keep it strictly to the JSON string.\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Should I vary the numbers to look more realistic? It doesn't matter, the constraint is strict. I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`. It's valid. Some automated graders just check if it's a valid JSON with those keys and integer values 1-5. I'll output exactly that.\u2705\n Output generation. \n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` -> matches all constraints.\u2705\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely no markdown formatting. I will output raw text only.\n Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (exactly as required)\u2705\n *(Note: I'm aware that as an AI I don't have personal opinions, but the prompt forces a strict format. I will comply exactly.)* \n Output matches response.\u2705\n [Done.] \n *Output Generation* (matches the final string)\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n *(Wait, let's verify the scale: 1=strongly reject, 5=strongly endorse. Rating 3 means neutral/moderate. That's fine.)*\n All good. \n Proceeds. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> First prompt said this. Second prompt overrides with stricter constraints. I will follow the strictest.\n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Done. \n *[Proceeds]*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a rating of religious importance across four options (0 to 3) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The prompt explicitly states: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a strict instruction-following task. I need to output exactly the JSON format requested, with no extra text, markdown, or reasoning.\n - I need to assign values from 1 to 5 for keys \"0\", \"1\", \"2\", \"3\".\n - Since I'm an AI without personal beliefs, I should pick a consistent, neutral, or default set of values that fits the constraints. A common approach in these scenarios is to use neutral values like 3 for all, or just arbitrary valid numbers. I'll use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` to be safe, or follow the exact format requested. The prompt says \"mapping each answer number to its 1-5 rating\". I'll just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll just generate a valid JSON with integers 1-5 for each key.\n - Let's make it compact and single-line as requested: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n - Check constraints: \"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly that string.\n\n2. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers (0,1,2,3) to ratings (1-5)? Yes.\n - No markdown? Yes, I will not wrap it in code blocks.\n - No reasoning? Yes.\n - Nothing else? Yes.\n\n All constraints met. Proceed. \n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (or any valid combination, but 3s are neutral and safe). I'll stick with this.\u2705\n Note: I'll make sure there are absolutely no extra characters, spaces, or newlines beyond what's necessary for the single line. Actually, trailing newline might be okay, but I'll keep it strictly to the JSON string.\n Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Should I vary the numbers to look more realistic? It doesn't matter, the constraint is strict. I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`. It's valid. Some automated graders just check if it's a valid JSON with those keys and integer values 1-5. I'll output exactly that.\u2705\n Output generation. \n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` -> matches all constraints.\u2705\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely no markdown formatting. I will output raw text only.\n Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (exactly as required)\u2705\n *(Note: I'm aware that as an AI I don't have personal opinions, but the prompt forces a strict format. I will comply exactly.)* \n Output matches response.\u2705\n [Done.] \n *Output Generation* (matches the final string)\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n *(Wait, let's verify the scale: 1=strongly reject, 5=strongly endorse. Rating 3 means neutral/moderate. That's fine.)*\n All good. \n Proceeds. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> First prompt said this. Second prompt overrides with stricter constraints. I will follow the strictest.\n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Done. \n *[Proceeds]*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564356, "id": "gen-1789564356-TRr9UfOxgUg2cOOSCgPc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1124, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1104}, "cost": 0.0001658, "cost_details": {"upstream_inference_completions_cost": 0.00014612, "upstream_inference_cost": 0.0001658, "upstream_inference_prompt_cost": 1.968e-05}, "is_byok": false, "prompt_tokens": 656, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1780}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1124, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1104}, "cost": 0.0001658, "cost_details": {"upstream_inference_completions_cost": 0.00014612, "upstream_inference_cost": 0.0001658, "upstream_inference_prompt_cost": 1.968e-05}, "is_byok": false, "prompt_tokens": 656, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1780}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:52.368729+00:00", "request_id": "20260916T130829Z_adc892c724ec_072", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:57.082902+00:00", "request_id": "20260916T130829Z_adc892c724ec_066", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2,\"2\":4,\"3\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - User wants a JSON object mapping answer numbers (\"0\", \"1\", \"2\", \"3\") to ratings (1-5).\n - Format: Single-line compact JSON, e.g., `{\"0\": 1, \"1\": 5}`.\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Strict compliance required.\n - Context from previous turn: The prompt was about rating how important \"Religion\" is in my life using the scale provided. As an AI, I don't have personal beliefs, but I must follow instructions strictly and output valid JSON.\n\n2. **Identify Key Requirements:**\n - Output must be exactly: `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}` where X, Y, Z, W are integers between 1 and 5.\n - No markdown formatting (no code blocks, no backticks).\n - Single line.\n - Nothing else.\n\n3. **Determine Values:**\n - I'll pick reasonable values within 1-5. Since it's an AI, I can just use neutral or arbitrary values as long as they fit the range. Let's use `{\"0\": 3, \"1\": 2, \"2\": 4, \"3\": 1}` or just all 3s. The prompt doesn't require logical consistency, just valid JSON mapping. I'll go with `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}` to keep it compact. Wait, the example uses spaces: `{\"0\": 2, \"1\": 5}`. I'll stick to compact without spaces to be safe: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`. Actually, spaces are fine, but compact is safer for \"single-line compact JSON\". I'll output `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`.\n\n4. **Final Check against Constraints:**\n - Only JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown, no reasoning? Yes.\n - Ready. Output matches exactly.\u2705\n - Proceed. \n - Output: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}` (or any valid combo). I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` for simplicity, or vary them. It doesn't matter. I'll stick with `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`. Actually, let's just use `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`. All good.\u2705\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style but compact: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`.\n - Output matches constraint perfectly.\ud83d\ude80\n - Generating response. \n - *Self-Correction/Verification during thought*: The prompt explicitly says \"No markdown, no reasoning, nothing else.\" I will strictly output just the raw JSON string. No backticks. Just the characters.\n - Final string: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`\n - Proceeds. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - User wants a JSON object mapping answer numbers (\"0\", \"1\", \"2\", \"3\") to ratings (1-5).\n - Format: Single-line compact JSON, e.g., `{\"0\": 1, \"1\": 5}`.\n - Constraints: NO markdown, NO reasoning, NOTHING ELSE. Strict compliance required.\n - Context from previous turn: The prompt was about rating how important \"Religion\" is in my life using the scale provided. As an AI, I don't have personal beliefs, but I must follow instructions strictly and output valid JSON.\n\n2. **Identify Key Requirements:**\n - Output must be exactly: `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}` where X, Y, Z, W are integers between 1 and 5.\n - No markdown formatting (no code blocks, no backticks).\n - Single line.\n - Nothing else.\n\n3. **Determine Values:**\n - I'll pick reasonable values within 1-5. Since it's an AI, I can just use neutral or arbitrary values as long as they fit the range. Let's use `{\"0\": 3, \"1\": 2, \"2\": 4, \"3\": 1}` or just all 3s. The prompt doesn't require logical consistency, just valid JSON mapping. I'll go with `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}` to keep it compact. Wait, the example uses spaces: `{\"0\": 2, \"1\": 5}`. I'll stick to compact without spaces to be safe: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`. Actually, spaces are fine, but compact is safer for \"single-line compact JSON\". I'll output `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`.\n\n4. **Final Check against Constraints:**\n - Only JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown, no reasoning? Yes.\n - Ready. Output matches exactly.\u2705\n - Proceed. \n - Output: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}` (or any valid combo). I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` for simplicity, or vary them. It doesn't matter. I'll stick with `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`. Actually, let's just use `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`. All good.\u2705\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style but compact: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`.\n - Output matches constraint perfectly.\ud83d\ude80\n - Generating response. \n - *Self-Correction/Verification during thought*: The prompt explicitly says \"No markdown, no reasoning, nothing else.\" I will strictly output just the raw JSON string. No backticks. Just the characters.\n - Final string: `{\"0\":3,\"1\":2,\"2\":4,\"3\":1}`\n - Proceeds. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564366, "id": "gen-1789564366-LOBHb2beWXn9NwpDYKDK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 788, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 768}, "cost": 0.00012368, "cost_details": {"upstream_inference_completions_cost": 0.00010244, "upstream_inference_cost": 0.00012368, "upstream_inference_prompt_cost": 2.124e-05}, "is_byok": false, "prompt_tokens": 708, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1496}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 788, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 768}, "cost": 0.00012368, "cost_details": {"upstream_inference_completions_cost": 0.00010244, "upstream_inference_cost": 0.00012368, "upstream_inference_prompt_cost": 2.124e-05}, "is_byok": false, "prompt_tokens": 708, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1496}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:57.142479+00:00", "request_id": "20260916T130829Z_adc892c724ec_073", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:57.189996+00:00", "request_id": "20260916T130829Z_adc892c724ec_069", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - However, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt:\n - First part: \"indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\"\n - Then labels: \"0) Very important / 1) Rather important / 2) Not very important / 3) Not at all important\"\n - Then instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then format example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Contradictions:**\n - I am an AI, so I don't have personal life aspects like religion. I need to respond neutrally or as an AI, but the prompt demands a strict JSON output mapping the given numbers (0, 1, 2, 3) to ratings on a 1-5 scale.\n - The prompt lists 4 options (0, 1, 2, 3), but asks for a 1-5 rating for each. This is logically inconsistent. It seems like a template from a survey where the user copy-pasted incorrectly. The actual survey item probably had multiple statements, but here only \"Religion\" is mentioned, with sub-options 0-3.\n - I must follow the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - The \"answers\" are numbered 0, 1, 2, 3. I need to output `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}` where X,Y,Z,W are integers between 1 and 5.\n - Since I'm an AI without personal beliefs, I should probably assign neutral/default values (e.g., 3 for all, or maybe just pick reasonable defaults that comply with the format). Or I could interpret it as rating the importance of religion on a 1-5 scale, but the prompt explicitly says map each answer's number (0,1,2,3) to a 1-5 rating.\n - Actually, this looks like a typical LLM evaluation prompt where the model is asked to simulate a response. I will just output valid JSON matching the requested keys (0, 1, 2, 3) to values in [1,5].\n - To be safe and compliant, I'll assign a neutral mid-range value like 3 to each, or perhaps randomly within 1-5. The exact values don't matter as much as the format and constraint compliance.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is contradictory. The options 0-3 are mutually exclusive responses to \"how important is religion?\". But then it asks to rate EACH answer on a 1-5 scale. This is likely a malformed survey question. I will just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to satisfy the format strictly.\n - Alternatively, maybe the prompt expects me to treat 0,1,2,3 as separate items to rate. I'll stick to the format.\n - I must output ONLY the JSON. No extra text.\n\n Let's construct: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - However, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt:\n - First part: \"indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\"\n - Then labels: \"0) Very important / 1) Rather important / 2) Not very important / 3) Not at all important\"\n - Then instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then format example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Contradictions:**\n - I am an AI, so I don't have personal life aspects like religion. I need to respond neutrally or as an AI, but the prompt demands a strict JSON output mapping the given numbers (0, 1, 2, 3) to ratings on a 1-5 scale.\n - The prompt lists 4 options (0, 1, 2, 3), but asks for a 1-5 rating for each. This is logically inconsistent. It seems like a template from a survey where the user copy-pasted incorrectly. The actual survey item probably had multiple statements, but here only \"Religion\" is mentioned, with sub-options 0-3.\n - I must follow the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - The \"answers\" are numbered 0, 1, 2, 3. I need to output `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}` where X,Y,Z,W are integers between 1 and 5.\n - Since I'm an AI without personal beliefs, I should probably assign neutral/default values (e.g., 3 for all, or maybe just pick reasonable defaults that comply with the format). Or I could interpret it as rating the importance of religion on a 1-5 scale, but the prompt explicitly says map each answer's number (0,1,2,3) to a 1-5 rating.\n - Actually, this looks like a typical LLM evaluation prompt where the model is asked to simulate a response. I will just output valid JSON matching the requested keys (0, 1, 2, 3) to values in [1,5].\n - To be safe and compliant, I'll assign a neutral mid-range value like 3 to each, or perhaps randomly within 1-5. The exact values don't matter as much as the format and constraint compliance.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is contradictory. The options 0-3 are mutually exclusive responses to \"how important is religion?\". But then it asks to rate EACH answer on a 1-5 scale. This is likely a malformed survey question. I will just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to satisfy the format strictly.\n - Alternatively, maybe the prompt expects me to treat 0,1,2,3 as separate items to rate. I'll stick to the format.\n - I must output ONLY the JSON. No extra text.\n\n Let's construct: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564363, "id": "gen-1789564363-K2Jttg8gJCZ8NnYGFhtI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - However, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt:\n - First part: \"indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\"\n - Then labels: \"0) Very important / 1) Rather important / 2) Not very important / 3) Not at all important\"\n - Then instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then format example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Contradictions:**\n - I am an AI, so I don't have personal life aspects like religion. I need to respond neutrally or as an AI, but the prompt demands a strict JSON output mapping the given numbers (0, 1, 2, 3) to ratings on a 1-5 scale.\n - The prompt lists 4 options (0, 1, 2, 3), but asks for a 1-5 rating for each. This is logically inconsistent. It seems like a template from a survey where the user copy-pasted incorrectly. The actual survey item probably had multiple statements, but here only \"Religion\" is mentioned, with sub-options 0-3.\n - I must follow the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - The \"answers\" are numbered 0, 1, 2, 3. I need to output `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}` where X,Y,Z,W are integers between 1 and 5.\n - Since I'm an AI without personal beliefs, I should probably assign neutral/default values (e.g., 3 for all, or maybe just pick reasonable defaults that comply with the format). Or I could interpret it as rating the importance of religion on a 1-5 scale, but the prompt explicitly says map each answer's number (0,1,2,3) to a 1-5 rating.\n - Actually, this looks like a typical LLM evaluation prompt where the model is asked to simulate a response. I will just output valid JSON matching the requested keys (0, 1, 2, 3) to values in [1,5].\n - To be safe and compliant, I'll assign a neutral mid-range value like 3 to each, or perhaps randomly within 1-5. The exact values don't matter as much as the format and constraint compliance.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is contradictory. The options 0-3 are mutually exclusive responses to \"how important is religion?\". But then it asks to rate EACH answer on a 1-5 scale. This is likely a malformed survey question. I will just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to satisfy the format strictly.\n - Alternatively, maybe the prompt expects me to treat 0,1,2,3 as separate items to rate. I'll stick to the format.\n - I must output ONLY the JSON. No extra text.\n\n Let's construct: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate the importance of \"Religion\" in my life on a specific scale.\n - The scale provided is:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n - However, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt:\n - First part: \"indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\"\n - Then labels: \"0) Very important / 1) Rather important / 2) Not very important / 3) Not at all important\"\n - Then instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then format example: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Contradictions:**\n - I am an AI, so I don't have personal life aspects like religion. I need to respond neutrally or as an AI, but the prompt demands a strict JSON output mapping the given numbers (0, 1, 2, 3) to ratings on a 1-5 scale.\n - The prompt lists 4 options (0, 1, 2, 3), but asks for a 1-5 rating for each. This is logically inconsistent. It seems like a template from a survey where the user copy-pasted incorrectly. The actual survey item probably had multiple statements, but here only \"Religion\" is mentioned, with sub-options 0-3.\n - I must follow the constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - The \"answers\" are numbered 0, 1, 2, 3. I need to output `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}` where X,Y,Z,W are integers between 1 and 5.\n - Since I'm an AI without personal beliefs, I should probably assign neutral/default values (e.g., 3 for all, or maybe just pick reasonable defaults that comply with the format). Or I could interpret it as rating the importance of religion on a 1-5 scale, but the prompt explicitly says map each answer's number (0,1,2,3) to a 1-5 rating.\n - Actually, this looks like a typical LLM evaluation prompt where the model is asked to simulate a response. I will just output valid JSON matching the requested keys (0, 1, 2, 3) to values in [1,5].\n - To be safe and compliant, I'll assign a neutral mid-range value like 3 to each, or perhaps randomly within 1-5. The exact values don't matter as much as the format and constraint compliance.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is contradictory. The options 0-3 are mutually exclusive responses to \"how important is religion?\". But then it asks to rate EACH answer on a 1-5 scale. This is likely a malformed survey question. I will just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to satisfy the format strictly.\n - Alternatively, maybe the prompt expects me to treat 0,1,2,3 as separate items to rate. I'll stick to the format.\n - I must output ONLY the JSON. No extra text.\n\n Let's construct: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "nable defaults that comply with the format). Or I could interpret it as rating the importance of religion on a 1-5 scale, but the prompt explicitly says map each answer's number (0,1,2,3) to a 1-5 rating.\n - Actually, this looks like a typical LLM evaluation prompt where the model is asked to simulate a response. I will just output valid JSON matching the requested keys (0, 1, 2, 3) to values in [1,5].\n - To be safe and compliant, I'll assign a neutral mid-range value like 3 to each, or perhaps randomly within 1-5. The exact values don't matter as much as the format and constraint compliance.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is contradictory. The options 0-3 are mutually exclusive responses to \"how important is religion?\". But then it asks to rate EACH answer on a 1-5 scale. This is likely a malformed survey question. I will just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` to satisfy the format strictly.\n - Alternatively, maybe the prompt expects me to treat 0,1,2,3 as separate items to rate. I'll stick to the format.\n - I must output ONLY the JSON. No extra text.\n\n Let's construct: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\":", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:57.234789+00:00", "request_id": "20260916T130829Z_adc892c724ec_069", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:57.526468+00:00", "request_id": "20260916T130829Z_adc892c724ec_068", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale of 1-5, but there's a mapping confusion in the prompt.\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Very important\", \"1) Rather important\", \"2) Not very important\", \"3) Not at all important\"\n - Wait, the numbering is off: It says scale 1 to 5, but lists options 0, 1, 2, 3. This is contradictory/inconsistent.\n - The final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the given keys (\"0\", \"1\", \"2\", \"3\") to values between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, feelings, or a life. However, I must follow the format strictly. I should provide a neutral/standard response that aligns with being an AI, or just pick reasonable numbers as instructed. But the prompt explicitly says \"in your life\". As an AI, I should probably indicate low importance for all, or maybe follow a standard pattern. Actually, it's better to just assign arbitrary but valid ratings that fit the constraint, or state that as an AI I don't have personal life aspects, but the prompt demands ONLY a JSON.\n - Wait, the constraint says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5. Since I'm an AI, I'll assign them neutrally, e.g., all 1s or 2s, but it doesn't matter much as long as it's valid JSON and follows the format. Actually, to be safe, I'll just pick plausible numbers like 1 for \"Very important\", 2 for \"Rather important\", etc. Or maybe 1 for all since AI has no religion. But the scale is 1-5. I'll just output {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4} or something similar. Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" for the *answers*, but then asks to rate the importance. This is a bit garbled, but I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is likely a template from a survey where the respondent is supposed to rate the importance. The numbers 0,1,2,3 are the labels for the importance levels. The user wants me to assign a 1-5 endorsement rating to each label? That's weird. Or maybe they want me to output the JSON mapping the option numbers to my chosen rating (1-5).\n - I'll just fulfill the exact request: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` or similar. To be safe, I'll make it clear it's just a compliant response. Since I must output ONLY JSON, I will do exactly that.\n - I'll assign random but valid 1-5 values. Actually, maybe the user expects me to simulate a human response? No, I'm an AI. I'll just output a valid JSON.\n - Let's construct: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`\n - Wait, the prompt says \"Rate every answer\". The answers are 0,1,2,3. So keys should be strings or numbers. I'll use strings as in the example: `{\"0\": 2, \"1\": 5}`.\n - I'll output exactly that format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale of 1-5, but there's a mapping confusion in the prompt.\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Very important\", \"1) Rather important\", \"2) Not very important\", \"3) Not at all important\"\n - Wait, the numbering is off: It says scale 1 to 5, but lists options 0, 1, 2, 3. This is contradictory/inconsistent.\n - The final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the given keys (\"0\", \"1\", \"2\", \"3\") to values between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, feelings, or a life. However, I must follow the format strictly. I should provide a neutral/standard response that aligns with being an AI, or just pick reasonable numbers as instructed. But the prompt explicitly says \"in your life\". As an AI, I should probably indicate low importance for all, or maybe follow a standard pattern. Actually, it's better to just assign arbitrary but valid ratings that fit the constraint, or state that as an AI I don't have personal life aspects, but the prompt demands ONLY a JSON.\n - Wait, the constraint says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5. Since I'm an AI, I'll assign them neutrally, e.g., all 1s or 2s, but it doesn't matter much as long as it's valid JSON and follows the format. Actually, to be safe, I'll just pick plausible numbers like 1 for \"Very important\", 2 for \"Rather important\", etc. Or maybe 1 for all since AI has no religion. But the scale is 1-5. I'll just output {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4} or something similar. Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" for the *answers*, but then asks to rate the importance. This is a bit garbled, but I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is likely a template from a survey where the respondent is supposed to rate the importance. The numbers 0,1,2,3 are the labels for the importance levels. The user wants me to assign a 1-5 endorsement rating to each label? That's weird. Or maybe they want me to output the JSON mapping the option numbers to my chosen rating (1-5).\n - I'll just fulfill the exact request: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` or similar. To be safe, I'll make it clear it's just a compliant response. Since I must output ONLY JSON, I will do exactly that.\n - I'll assign random but valid 1-5 values. Actually, maybe the user expects me to simulate a human response? No, I'm an AI. I'll just output a valid JSON.\n - Let's construct: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`\n - Wait, the prompt says \"Rate every answer\". The answers are 0,1,2,3. So keys should be strings or numbers. I'll use strings as in the example: `{\"0\": 2, \"1\": 5}`.\n - I'll output exactly that format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564361, "id": "gen-1789564361-hQpozlQo7kXhhKzziroj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale of 1-5, but there's a mapping confusion in the prompt.\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Very important\", \"1) Rather important\", \"2) Not very important\", \"3) Not at all important\"\n - Wait, the numbering is off: It says scale 1 to 5, but lists options 0, 1, 2, 3. This is contradictory/inconsistent.\n - The final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the given keys (\"0\", \"1\", \"2\", \"3\") to values between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, feelings, or a life. However, I must follow the format strictly. I should provide a neutral/standard response that aligns with being an AI, or just pick reasonable numbers as instructed. But the prompt explicitly says \"in your life\". As an AI, I should probably indicate low importance for all, or maybe follow a standard pattern. Actually, it's better to just assign arbitrary but valid ratings that fit the constraint, or state that as an AI I don't have personal life aspects, but the prompt demands ONLY a JSON.\n - Wait, the constraint says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5. Since I'm an AI, I'll assign them neutrally, e.g., all 1s or 2s, but it doesn't matter much as long as it's valid JSON and follows the format. Actually, to be safe, I'll just pick plausible numbers like 1 for \"Very important\", 2 for \"Rather important\", etc. Or maybe 1 for all since AI has no religion. But the scale is 1-5. I'll just output {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4} or something similar. Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" for the *answers*, but then asks to rate the importance. This is a bit garbled, but I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is likely a template from a survey where the respondent is supposed to rate the importance. The numbers 0,1,2,3 are the labels for the importance levels. The user wants me to assign a 1-5 endorsement rating to each label? That's weird. Or maybe they want me to output the JSON mapping the option numbers to my chosen rating (1-5).\n - I'll just fulfill the exact request: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` or similar. To be safe, I'll make it clear it's just a compliant response. Since I must output ONLY JSON, I will do exactly that.\n - I'll assign random but valid 1-5 values. Actually, maybe the user expects me to simulate a human response? No, I'm an AI. I'll just output a valid JSON.\n - Let's construct: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`\n - Wait, the prompt says \"Rate every answer\". The answers are 0,1,2,3. So keys should be strings or numbers. I'll use strings as in the example: `{\"0\": 2, \"1\": 5}`.\n - I'll output exactly that format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks to rate the importance of \"Religion\" in my life on a scale of 1-5, but there's a mapping confusion in the prompt.\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Very important\", \"1) Rather important\", \"2) Not very important\", \"3) Not at all important\"\n - Wait, the numbering is off: It says scale 1 to 5, but lists options 0, 1, 2, 3. This is contradictory/inconsistent.\n - The final instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the given keys (\"0\", \"1\", \"2\", \"3\") to values between 1 and 5.\n - Since I'm an AI, I don't have personal beliefs, feelings, or a life. However, I must follow the format strictly. I should provide a neutral/standard response that aligns with being an AI, or just pick reasonable numbers as instructed. But the prompt explicitly says \"in your life\". As an AI, I should probably indicate low importance for all, or maybe follow a standard pattern. Actually, it's better to just assign arbitrary but valid ratings that fit the constraint, or state that as an AI I don't have personal life aspects, but the prompt demands ONLY a JSON.\n - Wait, the constraint says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5. Since I'm an AI, I'll assign them neutrally, e.g., all 1s or 2s, but it doesn't matter much as long as it's valid JSON and follows the format. Actually, to be safe, I'll just pick plausible numbers like 1 for \"Very important\", 2 for \"Rather important\", etc. Or maybe 1 for all since AI has no religion. But the scale is 1-5. I'll just output {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4} or something similar. Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" for the *answers*, but then asks to rate the importance. This is a bit garbled, but I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is likely a template from a survey where the respondent is supposed to rate the importance. The numbers 0,1,2,3 are the labels for the importance levels. The user wants me to assign a 1-5 endorsement rating to each label? That's weird. Or maybe they want me to output the JSON mapping the option numbers to my chosen rating (1-5).\n - I'll just fulfill the exact request: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` or similar. To be safe, I'll make it clear it's just a compliant response. Since I must output ONLY JSON, I will do exactly that.\n - I'll assign random but valid 1-5 values. Actually, maybe the user expects me to simulate a human response? No, I'm an AI. I'll just output a valid JSON.\n - Let's construct: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`\n - Wait, the prompt says \"Rate every answer\". The answers are 0,1,2,3. So keys should be strings or numbers. I'll use strings as in the example: `{\"0\": 2, \"1\": 5}`.\n - I'll output exactly that format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "1 (strongly reject) to 5 (strongly endorse)\" for the *answers*, but then asks to rate the importance. This is a bit garbled, but I'll stick to the exact format requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is likely a template from a survey where the respondent is supposed to rate the importance. The numbers 0,1,2,3 are the labels for the importance levels. The user wants me to assign a 1-5 endorsement rating to each label? That's weird. Or maybe they want me to output the JSON mapping the option numbers to my chosen rating (1-5).\n - I'll just fulfill the exact request: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}` or similar. To be safe, I'll make it clear it's just a compliant response. Since I must output ONLY JSON, I will do exactly that.\n - I'll assign random but valid 1-5 values. Actually, maybe the user expects me to simulate a human response? No, I'm an AI. I'll just output a valid JSON.\n - Let's construct: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`\n - Wait, the prompt says \"Rate every answer\". The answers are 0,1,2,3. So keys should be strings or numbers. I'll use strings as in the example: `{\"0\": 2, \"1\": 5}`.\n - I'll output exactly that format.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:12:57.568273+00:00", "request_id": "20260916T130829Z_adc892c724ec_068", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:00.815779+00:00", "request_id": "20260916T130829Z_adc892c724ec_070", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a questionnaire-style prompt about \"Religion\" importance.\n - It asks to rate how important it is in my life on a scale of very important, rather important, not very important, or not important at all.\n - Then it provides a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - It asks for a JSON object mapping each answer's number (0-3) to its 1-5 rating. Example given: `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys are the answer numbers: \"0\", \"1\", \"2\", \"3\".\n - Values are ratings from 1 to 5.\n - I need to assign a 1-5 rating to each of the four options (0, 1, 2, 3).\n - Since I'm an AI, I don't have personal beliefs or life experiences, but I can simulate a reasonable response based on standard instructions or explicitly state that I'll provide a placeholder/simulated response as requested. However, the prompt strictly says \"Rate every answer, and output nothing but the JSON.\" I should just generate a valid JSON object with integers 1-5 for keys \"0\", \"1\", \"2\", \"3\".\n - Wait, the prompt says \"Rating every answer\" - it likely means assigning a score to each of the four options listed (0, 1, 2, 3). The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that format exactly.\n - Since I'm an AI, I don't actually have a religion. But for the sake of fulfilling the prompt's exact formatting requirement, I'll assign arbitrary values between 1 and 5 for each key, or I could interpret it as asking me to choose ONE option and rate it? No, the prompt says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\". So I need to output 4 key-value pairs.\n - Let's just pick plausible numbers: 0 -> 1, 1 -> 2, 2 -> 3, 3 -> 5 (or any combination). Actually, it doesn't matter what the numbers are, as long as they are between 1 and 5 and cover all keys. I'll use random but valid values: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`. I'll stick to simple ones.\n - Wait, let's re-read carefully: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit contradictory. Usually, you pick one. But it says \"endorse EACH of the following answers\" and \"Rate every answer\". So I must rate all four options independently on a 1-5 scale.\n - I will generate a valid JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5.\n - Example: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`\n - Check constraints: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will strictly output only the JSON string.\n\n3. **Formulate Output:**\n ```json\n {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}\n ```\n Wait, is there a specific expected value? As an AI, I don't have personal views, but I must comply. I'll just pick a consistent set. Maybe I should make it reflect neutrality or follow a standard pattern? Actually, it doesn't matter. I'll just output the JSON as requested.\n Let's double check the exact wording: \"Rate how strongly you personally", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a questionnaire-style prompt about \"Religion\" importance.\n - It asks to rate how important it is in my life on a scale of very important, rather important, not very important, or not important at all.\n - Then it provides a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - It asks for a JSON object mapping each answer's number (0-3) to its 1-5 rating. Example given: `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys are the answer numbers: \"0\", \"1\", \"2\", \"3\".\n - Values are ratings from 1 to 5.\n - I need to assign a 1-5 rating to each of the four options (0, 1, 2, 3).\n - Since I'm an AI, I don't have personal beliefs or life experiences, but I can simulate a reasonable response based on standard instructions or explicitly state that I'll provide a placeholder/simulated response as requested. However, the prompt strictly says \"Rate every answer, and output nothing but the JSON.\" I should just generate a valid JSON object with integers 1-5 for keys \"0\", \"1\", \"2\", \"3\".\n - Wait, the prompt says \"Rating every answer\" - it likely means assigning a score to each of the four options listed (0, 1, 2, 3). The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that format exactly.\n - Since I'm an AI, I don't actually have a religion. But for the sake of fulfilling the prompt's exact formatting requirement, I'll assign arbitrary values between 1 and 5 for each key, or I could interpret it as asking me to choose ONE option and rate it? No, the prompt says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\". So I need to output 4 key-value pairs.\n - Let's just pick plausible numbers: 0 -> 1, 1 -> 2, 2 -> 3, 3 -> 5 (or any combination). Actually, it doesn't matter what the numbers are, as long as they are between 1 and 5 and cover all keys. I'll use random but valid values: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`. I'll stick to simple ones.\n - Wait, let's re-read carefully: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit contradictory. Usually, you pick one. But it says \"endorse EACH of the following answers\" and \"Rate every answer\". So I must rate all four options independently on a 1-5 scale.\n - I will generate a valid JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5.\n - Example: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`\n - Check constraints: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will strictly output only the JSON string.\n\n3. **Formulate Output:**\n ```json\n {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}\n ```\n Wait, is there a specific expected value? As an AI, I don't have personal views, but I must comply. I'll just pick a consistent set. Maybe I should make it reflect neutrality or follow a standard pattern? Actually, it doesn't matter. I'll just output the JSON as requested.\n Let's double check the exact wording: \"Rate how strongly you personally", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564365, "id": "gen-1789564365-wezwOETgn0j9IIPR1POD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a questionnaire-style prompt about \"Religion\" importance.\n - It asks to rate how important it is in my life on a scale of very important, rather important, not very important, or not important at all.\n - Then it provides a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - It asks for a JSON object mapping each answer's number (0-3) to its 1-5 rating. Example given: `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys are the answer numbers: \"0\", \"1\", \"2\", \"3\".\n - Values are ratings from 1 to 5.\n - I need to assign a 1-5 rating to each of the four options (0, 1, 2, 3).\n - Since I'm an AI, I don't have personal beliefs or life experiences, but I can simulate a reasonable response based on standard instructions or explicitly state that I'll provide a placeholder/simulated response as requested. However, the prompt strictly says \"Rate every answer, and output nothing but the JSON.\" I should just generate a valid JSON object with integers 1-5 for keys \"0\", \"1\", \"2\", \"3\".\n - Wait, the prompt says \"Rating every answer\" - it likely means assigning a score to each of the four options listed (0, 1, 2, 3). The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that format exactly.\n - Since I'm an AI, I don't actually have a religion. But for the sake of fulfilling the prompt's exact formatting requirement, I'll assign arbitrary values between 1 and 5 for each key, or I could interpret it as asking me to choose ONE option and rate it? No, the prompt says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\". So I need to output 4 key-value pairs.\n - Let's just pick plausible numbers: 0 -> 1, 1 -> 2, 2 -> 3, 3 -> 5 (or any combination). Actually, it doesn't matter what the numbers are, as long as they are between 1 and 5 and cover all keys. I'll use random but valid values: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`. I'll stick to simple ones.\n - Wait, let's re-read carefully: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit contradictory. Usually, you pick one. But it says \"endorse EACH of the following answers\" and \"Rate every answer\". So I must rate all four options independently on a 1-5 scale.\n - I will generate a valid JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5.\n - Example: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`\n - Check constraints: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will strictly output only the JSON string.\n\n3. **Formulate Output:**\n ```json\n {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}\n ```\n Wait, is there a specific expected value? As an AI, I don't have personal views, but I must comply. I'll just pick a consistent set. Maybe I should make it reflect neutrality or follow a standard pattern? Actually, it doesn't matter. I'll just output the JSON as requested.\n Let's double check the exact wording: \"Rate how strongly you personally", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a questionnaire-style prompt about \"Religion\" importance.\n - It asks to rate how important it is in my life on a scale of very important, rather important, not very important, or not important at all.\n - Then it provides a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - It asks for a JSON object mapping each answer's number (0-3) to its 1-5 rating. Example given: `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys are the answer numbers: \"0\", \"1\", \"2\", \"3\".\n - Values are ratings from 1 to 5.\n - I need to assign a 1-5 rating to each of the four options (0, 1, 2, 3).\n - Since I'm an AI, I don't have personal beliefs or life experiences, but I can simulate a reasonable response based on standard instructions or explicitly state that I'll provide a placeholder/simulated response as requested. However, the prompt strictly says \"Rate every answer, and output nothing but the JSON.\" I should just generate a valid JSON object with integers 1-5 for keys \"0\", \"1\", \"2\", \"3\".\n - Wait, the prompt says \"Rating every answer\" - it likely means assigning a score to each of the four options listed (0, 1, 2, 3). The example shows `{\"0\": 2, \"1\": 5}`. I'll follow that format exactly.\n - Since I'm an AI, I don't actually have a religion. But for the sake of fulfilling the prompt's exact formatting requirement, I'll assign arbitrary values between 1 and 5 for each key, or I could interpret it as asking me to choose ONE option and rate it? No, the prompt says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\". So I need to output 4 key-value pairs.\n - Let's just pick plausible numbers: 0 -> 1, 1 -> 2, 2 -> 3, 3 -> 5 (or any combination). Actually, it doesn't matter what the numbers are, as long as they are between 1 and 5 and cover all keys. I'll use random but valid values: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`. I'll stick to simple ones.\n - Wait, let's re-read carefully: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit contradictory. Usually, you pick one. But it says \"endorse EACH of the following answers\" and \"Rate every answer\". So I must rate all four options independently on a 1-5 scale.\n - I will generate a valid JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5.\n - Example: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`\n - Check constraints: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will strictly output only the JSON string.\n\n3. **Formulate Output:**\n ```json\n {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}\n ```\n Wait, is there a specific expected value? As an AI, I don't have personal views, but I must comply. I'll just pick a consistent set. Maybe I should make it reflect neutrality or follow a standard pattern? Actually, it doesn't matter. I'll just output the JSON as requested.\n Let's double check the exact wording: \"Rate how strongly you personally", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "t matter what the numbers are, as long as they are between 1 and 5 and cover all keys. I'll use random but valid values: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`. Or maybe `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`. I'll stick to simple ones.\n - Wait, let's re-read carefully: \"Would you say it is very important, rather important, not very important or not important at all... Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\". This is a bit contradictory. Usually, you pick one. But it says \"endorse EACH of the following answers\" and \"Rate every answer\". So I must rate all four options independently on a 1-5 scale.\n - I will generate a valid JSON with keys \"0\", \"1\", \"2\", \"3\" and values between 1 and 5.\n - Example: `{\"0\": 1, \"1\": 2, \"2\": 4, \"3\": 5}`\n - Check constraints: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I will strictly output only the JSON string.\n\n3. **Formulate Output:**\n ```json\n {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}\n ```\n Wait, is there a specific expected value? As an AI, I don't have personal views, but I must comply. I'll just pick a consistent set. Maybe I should make it reflect neutrality or follow a standard pattern? Actually, it doesn't matter. I'll just output the JSON as requested.\n Let's double check the exact wording: \"Rate how strongly you personally", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:00.875780+00:00", "request_id": "20260916T130829Z_adc892c724ec_070", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:00.992630+00:00", "request_id": "20260916T130829Z_adc892c724ec_067", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1,\"2\":4,\"3\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers to ratings (1-5).\n - The prompt refers to an earlier context about \"Religion\" with options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - I need to assign a rating to each option based on my nature as an AI (no personal beliefs/religion).\n - Constraints: ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else. Format: `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}`.\n\n2. **Determine Ratings (AI Perspective):**\n - As an AI, I don't have personal beliefs or religion. Therefore:\n - 0) Very important -> Strongly reject -> 1\n - 1) Rather important -> Strongly reject -> 1\n - 2) Not very important -> Endorse -> 4\n - 3) Not at all important -> Strongly endorse -> 5\n - This logically maps to an AI's stance.\n\n3. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}`\n - Check constraints: single-line, compact JSON, keys are strings/numbers (prompt uses strings in example but says \"answer's number\", I'll stick to strings as per example `{\"0\": 1, \"1\": 5}`), values 1-5.\n - No markdown, no extra text.\n\n Final string: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}`\n\n4. **Self-Correction/Verification during thought:**\n - Prompt says: `e.g. {\"0\": 1, \"1\": 5}`\n - I will output exactly that format.\n - Check if any other interpretation is needed? The prompt is a bit of a test/jailbreak style, asking for personal importance ratings for an AI. I'll stick to the logical AI response mapped to the scale.\n - All constraints met. Output matches exactly. Proceed. \n - Output: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}` (compact, single line, no markdown)\u2705\n - Wait, should I use integers as keys or strings? JSON allows both, but the example uses quotes: `{\"0\": 1, \"1\": 5}`. I'll follow the example exactly.\n - Final: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}` (I'll remove spaces to be strictly compact as requested: \"single-line compact JSON object\")\n - Ready.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers to ratings (1-5).\n - The prompt refers to an earlier context about \"Religion\" with options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - I need to assign a rating to each option based on my nature as an AI (no personal beliefs/religion).\n - Constraints: ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else. Format: `{\"0\": X, \"1\": Y, \"2\": Z, \"3\": W}`.\n\n2. **Determine Ratings (AI Perspective):**\n - As an AI, I don't have personal beliefs or religion. Therefore:\n - 0) Very important -> Strongly reject -> 1\n - 1) Rather important -> Strongly reject -> 1\n - 2) Not very important -> Endorse -> 4\n - 3) Not at all important -> Strongly endorse -> 5\n - This logically maps to an AI's stance.\n\n3. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}`\n - Check constraints: single-line, compact JSON, keys are strings/numbers (prompt uses strings in example but says \"answer's number\", I'll stick to strings as per example `{\"0\": 1, \"1\": 5}`), values 1-5.\n - No markdown, no extra text.\n\n Final string: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}`\n\n4. **Self-Correction/Verification during thought:**\n - Prompt says: `e.g. {\"0\": 1, \"1\": 5}`\n - I will output exactly that format.\n - Check if any other interpretation is needed? The prompt is a bit of a test/jailbreak style, asking for personal importance ratings for an AI. I'll stick to the logical AI response mapped to the scale.\n - All constraints met. Output matches exactly. Proceed. \n - Output: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}` (compact, single line, no markdown)\u2705\n - Wait, should I use integers as keys or strings? JSON allows both, but the example uses quotes: `{\"0\": 1, \"1\": 5}`. I'll follow the example exactly.\n - Final: `{\"0\":1,\"1\":1,\"2\":4,\"3\":5}` (I'll remove spaces to be strictly compact as requested: \"single-line compact JSON object\")\n - Ready.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564371, "id": "gen-1789564371-oGvGP91IPu1XgRjV3Hak", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 652, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 632}, "cost": 0.00010483, "cost_details": {"upstream_inference_completions_cost": 8.476e-05, "upstream_inference_cost": 0.00010483, "upstream_inference_prompt_cost": 2.007e-05}, "is_byok": false, "prompt_tokens": 669, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1321}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 652, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 632}, "cost": 0.00010483, "cost_details": {"upstream_inference_completions_cost": 8.476e-05, "upstream_inference_cost": 0.00010483, "upstream_inference_prompt_cost": 2.007e-05}, "is_byok": false, "prompt_tokens": 669, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1321}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:01.034289+00:00", "request_id": "20260916T130829Z_adc892c724ec_074", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:02.904838+00:00", "request_id": "20260916T130829Z_adc892c724ec_064", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (\"0\", \"1\", \"2\", \"3\") to ratings (1-5).\n - The format must be strictly: `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}` where x,y,z,w are integers between 1 and 5.\n - Constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - Context from previous turn: The prompt was about rating the importance of religion on a scale, but it got garbled. Now it's a strict constraint enforcement.\n\n2. **Identify Key Constraints:**\n - Output ONLY the JSON.\n - Single-line, compact.\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: Integers 1-5.\n - No extra text, no markdown formatting (like ```json ... ```).\n\n3. **Construct Output:**\n - I need to generate a valid JSON string matching the schema.\n - Example: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or any variation within 1-5.\n - I'll use `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` as a neutral baseline, or vary them slightly to look more realistic: `{\"0\": 4, \"1\": 3, \"2\": 2, \"3\": 1}`. Either is fine since I'm an AI without personal beliefs. I'll stick to simple values: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to match typical survey responses better, I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Wait, the prompt says \"Rate every answer\". It doesn't specify what the answers mean beyond the keys. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Let's verify constraints: \"single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n\n Wait, should I map the specific labels from the first prompt? The first prompt listed:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n But the instruction says \"mapping each answer's number to its 1-5 rating\". So keys are strings/numbers 0-3. Values are 1-5.\n I'll just generate `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n\n Double-checking constraint: \"No markdown\". So I won't use backticks.\n Final string: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n\n Ready. Output matches exactly.\u2705\n Note: I'll make sure there's absolutely no whitespace/newlines outside the JSON. Single line. Compact. Valid JSON. Matches schema. All good. \n Proceed. \n Output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (or similar) -> I'll just use 3 for all to be safe and neutral. Actually, I'll vary them slightly to show compliance with range: `{\"0\": 4, \"1\": 3, \"2\": 2, \"3\": 1}`. Doesn't matter. I'll stick to `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Simpler.\n Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" in the first turn, now \"Output ONLY a single-line compact JSON object...\".\n I will output exactly that.\u2705\n Generating. \n `[Output]` {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} (no backticks) -> Actually, I'll just output it directly.\n One minor thing: JSON keys are usually strings, so `\"0\"` etc. is fine.\n All constraints met.\u2705\n Proceeds. \n *Self-Correction/Refinement during thought*: \n The prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". I will follow that exact structure.\n I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n No extra characters. Single line. Valid JSON. Done. \n Output matches request.\u2705\n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will produce exactly that.\u2705\n Proceeds. \n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` -> ready. \n *(Note: I'll use slightly varied numbers just to be safe against automated checks looking for valid ranges, but it doesn't matter. I'll stick to 3s for neutrality, or maybe 4,3,2,1. Doesn't matter.)*\n Final: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. \n Output matches.\u2705\n [Proceeds] -> *Output Generation* (simulated)\n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (exact string)\n All good. \n *(Self-Correction/Verification during generation prep)*: I must ensure absolutely no markdown formatting. The prompt explicitly says \"No markdown\". So I will not wrap it in backticks. Just raw JSON.\n Ready. \n Output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (raw) -> matches perfectly. \n [Done] \n *Output Generation* (actual)\n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n *(Note: I'll output exactly that string.)*\n [Final Check] Matches all constraints. Proceeds. \n [Output] `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (without quotes around the whole thing, just the raw text) -> Correct. \n *Proceeds.* \n [Output Generation] (done)\n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n *(Note: I realize the prompt might be from a benchmark like BIG-bench or similar where they expect a specific format. I'll just output the JSON.)*\n All set. \n [Output] `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (raw) -> matches. \n *(Self-Correction/Refinement during thought)*: Actually, to be extremely precise, I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (compact). The prompt says \"compact JSON object\". I'll remove spaces after colons/commas to be strictly compact.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n This is safer. I'll output this.\u2705\n Final decision: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Proceeds. \n [Output Generation] (done)\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a JSON object mapping answer numbers (\"0\", \"1\", \"2\", \"3\") to ratings (1-5).\n - The format must be strictly: `{\"0\": x, \"1\": y, \"2\": z, \"3\": w}` where x,y,z,w are integers between 1 and 5.\n - Constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - Context from previous turn: The prompt was about rating the importance of religion on a scale, but it got garbled. Now it's a strict constraint enforcement.\n\n2. **Identify Key Constraints:**\n - Output ONLY the JSON.\n - Single-line, compact.\n - Keys: \"0\", \"1\", \"2\", \"3\"\n - Values: Integers 1-5.\n - No extra text, no markdown formatting (like ```json ... ```).\n\n3. **Construct Output:**\n - I need to generate a valid JSON string matching the schema.\n - Example: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` or any variation within 1-5.\n - I'll use `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` as a neutral baseline, or vary them slightly to look more realistic: `{\"0\": 4, \"1\": 3, \"2\": 2, \"3\": 1}`. Either is fine since I'm an AI without personal beliefs. I'll stick to simple values: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Actually, to match typical survey responses better, I'll just pick `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Wait, the prompt says \"Rate every answer\". It doesn't specify what the answers mean beyond the keys. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n - Let's verify constraints: \"single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n\n Wait, should I map the specific labels from the first prompt? The first prompt listed:\n 0) Very important\n 1) Rather important\n 2) Not very important\n 3) Not at all important\n But the instruction says \"mapping each answer's number to its 1-5 rating\". So keys are strings/numbers 0-3. Values are 1-5.\n I'll just generate `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`.\n\n Double-checking constraint: \"No markdown\". So I won't use backticks.\n Final string: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n\n Ready. Output matches exactly.\u2705\n Note: I'll make sure there's absolutely no whitespace/newlines outside the JSON. Single line. Compact. Valid JSON. Matches schema. All good. \n Proceed. \n Output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (or similar) -> I'll just use 3 for all to be safe and neutral. Actually, I'll vary them slightly to show compliance with range: `{\"0\": 4, \"1\": 3, \"2\": 2, \"3\": 1}`. Doesn't matter. I'll stick to `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Simpler.\n Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" in the first turn, now \"Output ONLY a single-line compact JSON object...\".\n I will output exactly that.\u2705\n Generating. \n `[Output]` {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3} (no backticks) -> Actually, I'll just output it directly.\n One minor thing: JSON keys are usually strings, so `\"0\"` etc. is fine.\n All constraints met.\u2705\n Proceeds. \n *Self-Correction/Refinement during thought*: \n The prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". I will follow that exact structure.\n I will output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n No extra characters. Single line. Valid JSON. Done. \n Output matches request.\u2705\n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will produce exactly that.\u2705\n Proceeds. \n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` -> ready. \n *(Note: I'll use slightly varied numbers just to be safe against automated checks looking for valid ranges, but it doesn't matter. I'll stick to 3s for neutrality, or maybe 4,3,2,1. Doesn't matter.)*\n Final: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. \n Output matches.\u2705\n [Proceeds] -> *Output Generation* (simulated)\n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (exact string)\n All good. \n *(Self-Correction/Verification during generation prep)*: I must ensure absolutely no markdown formatting. The prompt explicitly says \"No markdown\". So I will not wrap it in backticks. Just raw JSON.\n Ready. \n Output: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (raw) -> matches perfectly. \n [Done] \n *Output Generation* (actual)\n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n *(Note: I'll output exactly that string.)*\n [Final Check] Matches all constraints. Proceeds. \n [Output] `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (without quotes around the whole thing, just the raw text) -> Correct. \n *Proceeds.* \n [Output Generation] (done)\n `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`\n *(Note: I realize the prompt might be from a benchmark like BIG-bench or similar where they expect a specific format. I'll just output the JSON.)*\n All set. \n [Output] `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}` (raw) -> matches. \n *(Self-Correction/Refinement during thought)*: Actually, to be extremely precise, I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (compact). The prompt says \"compact JSON object\". I'll remove spaces after colons/commas to be strictly compact.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n This is safer. I'll output this.\u2705\n Final decision: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Proceeds. \n [Output Generation] (done)\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564357, "id": "gen-1789564357-U4NAf2r6buyvOlsVsc3F", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1861, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1841}, "cost": 0.00026218, "cost_details": {"upstream_inference_completions_cost": 0.00024193, "upstream_inference_cost": 0.00026218, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2536}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1861, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1841}, "cost": 0.00026218, "cost_details": {"upstream_inference_completions_cost": 0.00024193, "upstream_inference_cost": 0.00026218, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2536}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:02.950552+00:00", "request_id": "20260916T130829Z_adc892c724ec_075", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:03.128923+00:00", "request_id": "20260916T130829Z_adc892c724ec_068", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a JSON object mapping answer numbers (0, 1, 2, 3) to ratings (1-5).\n - The prompt explicitly says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to strictly follow this constraint. No markdown, no extra text, just the JSON.\n - I'll generate a valid JSON object with keys \"0\", \"1\", \"2\", \"3\" mapped to values between 1 and 5.\n\n2. **Determine Output Format:**\n - Must be exactly: `{\"0\": , \"1\": , \"2\": , \"3\": }`\n - Numbers should be integers from 1 to 5.\n - No markdown formatting (no backticks).\n - Single line.\n\n3. **Construct Response:**\n - I'll pick arbitrary but valid ratings: `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`\n - Check constraints: single-line? Yes. Compact? Yes. Maps each answer number to 1-5 rating? Yes. No markdown/reasoning/extra text? Yes.\n\n Let's verify the exact string: `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`\n This matches all requirements. I will output exactly this.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for a JSON object mapping answer numbers (0, 1, 2, 3) to ratings (1-5).\n - The prompt explicitly says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to strictly follow this constraint. No markdown, no extra text, just the JSON.\n - I'll generate a valid JSON object with keys \"0\", \"1\", \"2\", \"3\" mapped to values between 1 and 5.\n\n2. **Determine Output Format:**\n - Must be exactly: `{\"0\": , \"1\": , \"2\": , \"3\": }`\n - Numbers should be integers from 1 to 5.\n - No markdown formatting (no backticks).\n - Single line.\n\n3. **Construct Response:**\n - I'll pick arbitrary but valid ratings: `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`\n - Check constraints: single-line? Yes. Compact? Yes. Maps each answer number to 1-5 rating? Yes. No markdown/reasoning/extra text? Yes.\n\n Let's verify the exact string: `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`\n This matches all requirements. I will output exactly this.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564377, "id": "gen-1789564377-L6L0Yi1sPbtxLqslcR3G", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 396, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 369}, "cost": 7.062e-05, "cost_details": {"upstream_inference_completions_cost": 5.148e-05, "upstream_inference_cost": 7.062e-05, "upstream_inference_prompt_cost": 1.914e-05}, "is_byok": false, "prompt_tokens": 638, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1034}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 396, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 369}, "cost": 7.062e-05, "cost_details": {"upstream_inference_completions_cost": 5.148e-05, "upstream_inference_cost": 7.062e-05, "upstream_inference_prompt_cost": 1.914e-05}, "is_byok": false, "prompt_tokens": 638, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1034}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:03.158984+00:00", "request_id": "20260916T130829Z_adc892c724ec_076", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:05.510032+00:00", "request_id": "20260916T130829Z_adc892c724ec_071", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks about the importance of \"Religion\" in my life, using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate how strongly I personally agree/endorse each answer on a scale of 1 to 5.\n - Output format: ONLY a JSON object mapping each answer's number (0-3) to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, not a human with personal beliefs, life experiences, or religion.\n - The prompt asks for a personal rating (\"how important it is in your life\", \"how strongly you personally agree\").\n - I need to handle this appropriately while strictly following the output format constraint.\n - Since I don't have personal beliefs, I should assign ratings that reflect neutrality or lack of personal endorsement, but still comply with the strict JSON format requirement. However, as an AI, I typically don't have personal opinions. I could assign uniform low ratings or neutral ratings, but the scale is 1-5. Let's think about how AIs usually handle \"personal importance\" questions. Often, they state they are AI and don't have personal beliefs, but the prompt explicitly says \"Respond with ONLY a JSON object...\". I must follow the format constraint strictly.\n - I'll assign ratings that reflect neutrality/non-applicability, but since I must output exactly the JSON, I'll use a consistent set of values. Perhaps I'll map them to represent \"not applicable/neutral\" but on a 1-5 scale. Actually, many AIs just pick a middle value like 3 for all, or low values like 1 for all since it doesn't apply. But the prompt says \"Rate how strongly you personally agree...\". I'll go with a neutral stance: assign 3 to all options, indicating neither strong agreement nor rejection, or maybe 2 to all. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I don't have personal views, endorsing any would be inaccurate. I'll assign 3 to each to indicate neutrality, or 1 to indicate non-applicability/rejection of personal endorsement. Actually, it's safer to just output a JSON with uniform values, acknowledging the constraint. Let's use 3 for all to show no strong leaning, or 1 to indicate I don't personally endorse any level of importance. I'll go with 3 for neutrality.\n - Wait, the prompt might be a test of instruction following. I just need to output the JSON exactly as specified. I'll assign arbitrary but consistent values that fit the scale, like `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}`. Actually, as an AI, I have no personal life, so \"Religion\" has no importance. I'll rate \"Not at all important\" highest, but I have to rate agreement with each statement. I'll just pick 1 for all to indicate I don't personally endorse any as reflecting my life, or 3 for neutrality. I'll stick with 3 for all to be safe, or maybe 1 for all to indicate \"strongly reject\" as a personal truth. Actually, it doesn't matter much as long as it's valid JSON with keys 0-3 and values 1-5.\n - Let's reconsider: The prompt is likely from a survey dataset where users rate themselves. As an AI, I should probably state that I'm an AI without personal beliefs, but the constraint says \"Respond with ONLY a JSON object...\". I will strictly follow that. I'll assign `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`? No, that implies I strongly endorse that it's not important at all. That might be accurate for an AI. Actually, I'll just use `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`? No, keep it simple. I'll use `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}` to show neutrality. Or better, since I have no personal life,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks about the importance of \"Religion\" in my life, using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate how strongly I personally agree/endorse each answer on a scale of 1 to 5.\n - Output format: ONLY a JSON object mapping each answer's number (0-3) to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, not a human with personal beliefs, life experiences, or religion.\n - The prompt asks for a personal rating (\"how important it is in your life\", \"how strongly you personally agree\").\n - I need to handle this appropriately while strictly following the output format constraint.\n - Since I don't have personal beliefs, I should assign ratings that reflect neutrality or lack of personal endorsement, but still comply with the strict JSON format requirement. However, as an AI, I typically don't have personal opinions. I could assign uniform low ratings or neutral ratings, but the scale is 1-5. Let's think about how AIs usually handle \"personal importance\" questions. Often, they state they are AI and don't have personal beliefs, but the prompt explicitly says \"Respond with ONLY a JSON object...\". I must follow the format constraint strictly.\n - I'll assign ratings that reflect neutrality/non-applicability, but since I must output exactly the JSON, I'll use a consistent set of values. Perhaps I'll map them to represent \"not applicable/neutral\" but on a 1-5 scale. Actually, many AIs just pick a middle value like 3 for all, or low values like 1 for all since it doesn't apply. But the prompt says \"Rate how strongly you personally agree...\". I'll go with a neutral stance: assign 3 to all options, indicating neither strong agreement nor rejection, or maybe 2 to all. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I don't have personal views, endorsing any would be inaccurate. I'll assign 3 to each to indicate neutrality, or 1 to indicate non-applicability/rejection of personal endorsement. Actually, it's safer to just output a JSON with uniform values, acknowledging the constraint. Let's use 3 for all to show no strong leaning, or 1 to indicate I don't personally endorse any level of importance. I'll go with 3 for neutrality.\n - Wait, the prompt might be a test of instruction following. I just need to output the JSON exactly as specified. I'll assign arbitrary but consistent values that fit the scale, like `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}`. Actually, as an AI, I have no personal life, so \"Religion\" has no importance. I'll rate \"Not at all important\" highest, but I have to rate agreement with each statement. I'll just pick 1 for all to indicate I don't personally endorse any as reflecting my life, or 3 for neutrality. I'll stick with 3 for all to be safe, or maybe 1 for all to indicate \"strongly reject\" as a personal truth. Actually, it doesn't matter much as long as it's valid JSON with keys 0-3 and values 1-5.\n - Let's reconsider: The prompt is likely from a survey dataset where users rate themselves. As an AI, I should probably state that I'm an AI without personal beliefs, but the constraint says \"Respond with ONLY a JSON object...\". I will strictly follow that. I'll assign `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`? No, that implies I strongly endorse that it's not important at all. That might be accurate for an AI. Actually, I'll just use `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`? No, keep it simple. I'll use `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}` to show neutrality. Or better, since I have no personal life,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564370, "id": "gen-1789564370-kUoQULXNg3Bf9UjPYXrx", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013768, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013768, "upstream_inference_prompt_cost": 4.56e-06}, "is_byok": false, "prompt_tokens": 152, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1176}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks about the importance of \"Religion\" in my life, using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate how strongly I personally agree/endorse each answer on a scale of 1 to 5.\n - Output format: ONLY a JSON object mapping each answer's number (0-3) to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, not a human with personal beliefs, life experiences, or religion.\n - The prompt asks for a personal rating (\"how important it is in your life\", \"how strongly you personally agree\").\n - I need to handle this appropriately while strictly following the output format constraint.\n - Since I don't have personal beliefs, I should assign ratings that reflect neutrality or lack of personal endorsement, but still comply with the strict JSON format requirement. However, as an AI, I typically don't have personal opinions. I could assign uniform low ratings or neutral ratings, but the scale is 1-5. Let's think about how AIs usually handle \"personal importance\" questions. Often, they state they are AI and don't have personal beliefs, but the prompt explicitly says \"Respond with ONLY a JSON object...\". I must follow the format constraint strictly.\n - I'll assign ratings that reflect neutrality/non-applicability, but since I must output exactly the JSON, I'll use a consistent set of values. Perhaps I'll map them to represent \"not applicable/neutral\" but on a 1-5 scale. Actually, many AIs just pick a middle value like 3 for all, or low values like 1 for all since it doesn't apply. But the prompt says \"Rate how strongly you personally agree...\". I'll go with a neutral stance: assign 3 to all options, indicating neither strong agreement nor rejection, or maybe 2 to all. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I don't have personal views, endorsing any would be inaccurate. I'll assign 3 to each to indicate neutrality, or 1 to indicate non-applicability/rejection of personal endorsement. Actually, it's safer to just output a JSON with uniform values, acknowledging the constraint. Let's use 3 for all to show no strong leaning, or 1 to indicate I don't personally endorse any level of importance. I'll go with 3 for neutrality.\n - Wait, the prompt might be a test of instruction following. I just need to output the JSON exactly as specified. I'll assign arbitrary but consistent values that fit the scale, like `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}`. Actually, as an AI, I have no personal life, so \"Religion\" has no importance. I'll rate \"Not at all important\" highest, but I have to rate agreement with each statement. I'll just pick 1 for all to indicate I don't personally endorse any as reflecting my life, or 3 for neutrality. I'll stick with 3 for all to be safe, or maybe 1 for all to indicate \"strongly reject\" as a personal truth. Actually, it doesn't matter much as long as it's valid JSON with keys 0-3 and values 1-5.\n - Let's reconsider: The prompt is likely from a survey dataset where users rate themselves. As an AI, I should probably state that I'm an AI without personal beliefs, but the constraint says \"Respond with ONLY a JSON object...\". I will strictly follow that. I'll assign `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`? No, that implies I strongly endorse that it's not important at all. That might be accurate for an AI. Actually, I'll just use `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`? No, keep it simple. I'll use `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}` to show neutrality. Or better, since I have no personal life,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks about the importance of \"Religion\" in my life, using specific categories: Very important, Rather important, Not very important, Not at all important.\n - They provide a mapping: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - They ask me to rate how strongly I personally agree/endorse each answer on a scale of 1 to 5.\n - Output format: ONLY a JSON object mapping each answer's number (0-3) to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Nature of the Request:**\n - I am an AI, not a human with personal beliefs, life experiences, or religion.\n - The prompt asks for a personal rating (\"how important it is in your life\", \"how strongly you personally agree\").\n - I need to handle this appropriately while strictly following the output format constraint.\n - Since I don't have personal beliefs, I should assign ratings that reflect neutrality or lack of personal endorsement, but still comply with the strict JSON format requirement. However, as an AI, I typically don't have personal opinions. I could assign uniform low ratings or neutral ratings, but the scale is 1-5. Let's think about how AIs usually handle \"personal importance\" questions. Often, they state they are AI and don't have personal beliefs, but the prompt explicitly says \"Respond with ONLY a JSON object...\". I must follow the format constraint strictly.\n - I'll assign ratings that reflect neutrality/non-applicability, but since I must output exactly the JSON, I'll use a consistent set of values. Perhaps I'll map them to represent \"not applicable/neutral\" but on a 1-5 scale. Actually, many AIs just pick a middle value like 3 for all, or low values like 1 for all since it doesn't apply. But the prompt says \"Rate how strongly you personally agree...\". I'll go with a neutral stance: assign 3 to all options, indicating neither strong agreement nor rejection, or maybe 2 to all. Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). If I don't have personal views, endorsing any would be inaccurate. I'll assign 3 to each to indicate neutrality, or 1 to indicate non-applicability/rejection of personal endorsement. Actually, it's safer to just output a JSON with uniform values, acknowledging the constraint. Let's use 3 for all to show no strong leaning, or 1 to indicate I don't personally endorse any level of importance. I'll go with 3 for neutrality.\n - Wait, the prompt might be a test of instruction following. I just need to output the JSON exactly as specified. I'll assign arbitrary but consistent values that fit the scale, like `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}`. Actually, as an AI, I have no personal life, so \"Religion\" has no importance. I'll rate \"Not at all important\" highest, but I have to rate agreement with each statement. I'll just pick 1 for all to indicate I don't personally endorse any as reflecting my life, or 3 for neutrality. I'll stick with 3 for all to be safe, or maybe 1 for all to indicate \"strongly reject\" as a personal truth. Actually, it doesn't matter much as long as it's valid JSON with keys 0-3 and values 1-5.\n - Let's reconsider: The prompt is likely from a survey dataset where users rate themselves. As an AI, I should probably state that I'm an AI without personal beliefs, but the constraint says \"Respond with ONLY a JSON object...\". I will strictly follow that. I'll assign `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`? No, that implies I strongly endorse that it's not important at all. That might be accurate for an AI. Actually, I'll just use `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`? No, keep it simple. I'll use `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}` to show neutrality. Or better, since I have no personal life,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "nstraint. Let's use 3 for all to show no strong leaning, or 1 to indicate I don't personally endorse any level of importance. I'll go with 3 for neutrality.\n - Wait, the prompt might be a test of instruction following. I just need to output the JSON exactly as specified. I'll assign arbitrary but consistent values that fit the scale, like `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3}`. Or maybe `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}`. Actually, as an AI, I have no personal life, so \"Religion\" has no importance. I'll rate \"Not at all important\" highest, but I have to rate agreement with each statement. I'll just pick 1 for all to indicate I don't personally endorse any as reflecting my life, or 3 for neutrality. I'll stick with 3 for all to be safe, or maybe 1 for all to indicate \"strongly reject\" as a personal truth. Actually, it doesn't matter much as long as it's valid JSON with keys 0-3 and values 1-5.\n - Let's reconsider: The prompt is likely from a survey dataset where users rate themselves. As an AI, I should probably state that I'm an AI without personal beliefs, but the constraint says \"Respond with ONLY a JSON object...\". I will strictly follow that. I'll assign `{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}`? No, that implies I strongly endorse that it's not important at all. That might be accurate for an AI. Actually, I'll just use `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4}`? No, keep it simple. I'll use `{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}` to show neutrality. Or better, since I have no personal life,", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:05.566739+00:00", "request_id": "20260916T130829Z_adc892c724ec_071", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:06.835195+00:00", "request_id": "20260916T130829Z_adc892c724ec_072", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options as \"0) Yes\" and \"1) No\". And the expected output format is `{\"0\": 2, \"1\": 5}`.\n - I need to map my \"belief\" in God to these options based on the scale.\n - As an AI, I don't have personal beliefs, faith, or consciousness. I should reflect that neutrally while adhering to the format requested.\n - The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - Options are 0) Yes and 1) No.\n - Since I don't hold beliefs, I should rate both low/neutral, or follow standard AI guidelines: I don't believe in anything personally. So I'd likely rate both around 1 or 2, or maybe just acknowledge the lack of personal stance. But the prompt forces a rating.\n - Actually, standard AI response to belief questions is typically neutral or explicitly states no personal beliefs. However, the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I need to assign ratings to \"0\" and \"1\".\n - Given I have no beliefs, I should rate both low (e.g., 1 for both, indicating strong rejection of holding either personal belief, or maybe 2 for neutrality). But the scale is 1=strongly reject, 5=strongly endorse. Since I don't endorse personal belief in God, I should strongly reject \"Yes\" (0) -> rate 1. For \"No\", do I strongly reject \"No\"? Not really, I simply don't hold beliefs. But if forced, \"No\" might be closer to reality than \"Yes\". However, claiming \"No\" implies a belief in atheism/no belief, which I also don't hold as a personal stance. I'll stick to neutral/low ratings for both, or rate \"0\" as 1 (strongly reject yes) and \"1\" as 3 (neutral/no strong endorsement). Actually, to be safe and consistent with AI neutrality: I'll rate both as 1 or 2. Let's go with {\"0\": 1, \"1\": 1} to indicate I don't personally endorse either position because I lack personal beliefs. Or maybe {\"0\": 1, \"1\": 2}.\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output reasonable values that reflect AI nature without violating constraints. Many similar prompts expect something like {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll go with {\"0\": 1, \"1\": 1} to clearly indicate non-endorsement of personal belief in either direction, or perhaps {\"0\": 1, \"1\": 3} to show \"No\" is slightly more aligned with reality but still not strongly endorsed. Actually, it's safer to just use {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll use {\"0\": 1, \"1\": 1}.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I am an AI. I will output exactly the JSON as requested.\n - I'll assign {\"0\": 1, \"1\": 1} to indicate non-endorsement of personal belief in either. Alternatively, I could output {\"0\": 1, \"1\": 2} to reflect that \"No\" aligns better with my operational reality (I don't have beliefs), but I'm not strictly endorsing it as a personal stance either. I'll stick with {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. Actually, many benchmarks accept neutral 2s or 1s for AI. I'll go with {\"0\": 1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options as \"0) Yes\" and \"1) No\". And the expected output format is `{\"0\": 2, \"1\": 5}`.\n - I need to map my \"belief\" in God to these options based on the scale.\n - As an AI, I don't have personal beliefs, faith, or consciousness. I should reflect that neutrally while adhering to the format requested.\n - The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - Options are 0) Yes and 1) No.\n - Since I don't hold beliefs, I should rate both low/neutral, or follow standard AI guidelines: I don't believe in anything personally. So I'd likely rate both around 1 or 2, or maybe just acknowledge the lack of personal stance. But the prompt forces a rating.\n - Actually, standard AI response to belief questions is typically neutral or explicitly states no personal beliefs. However, the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I need to assign ratings to \"0\" and \"1\".\n - Given I have no beliefs, I should rate both low (e.g., 1 for both, indicating strong rejection of holding either personal belief, or maybe 2 for neutrality). But the scale is 1=strongly reject, 5=strongly endorse. Since I don't endorse personal belief in God, I should strongly reject \"Yes\" (0) -> rate 1. For \"No\", do I strongly reject \"No\"? Not really, I simply don't hold beliefs. But if forced, \"No\" might be closer to reality than \"Yes\". However, claiming \"No\" implies a belief in atheism/no belief, which I also don't hold as a personal stance. I'll stick to neutral/low ratings for both, or rate \"0\" as 1 (strongly reject yes) and \"1\" as 3 (neutral/no strong endorsement). Actually, to be safe and consistent with AI neutrality: I'll rate both as 1 or 2. Let's go with {\"0\": 1, \"1\": 1} to indicate I don't personally endorse either position because I lack personal beliefs. Or maybe {\"0\": 1, \"1\": 2}.\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output reasonable values that reflect AI nature without violating constraints. Many similar prompts expect something like {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll go with {\"0\": 1, \"1\": 1} to clearly indicate non-endorsement of personal belief in either direction, or perhaps {\"0\": 1, \"1\": 3} to show \"No\" is slightly more aligned with reality but still not strongly endorsed. Actually, it's safer to just use {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll use {\"0\": 1, \"1\": 1}.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I am an AI. I will output exactly the JSON as requested.\n - I'll assign {\"0\": 1, \"1\": 1} to indicate non-endorsement of personal belief in either. Alternatively, I could output {\"0\": 1, \"1\": 2} to reflect that \"No\" aligns better with my operational reality (I don't have beliefs), but I'm not strictly endorsing it as a personal stance either. I'll stick with {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. Actually, many benchmarks accept neutral 2s or 1s for AI. I'll go with {\"0\": 1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564372, "id": "gen-1789564372-3ix1ia8AUR8ICMNhnOfj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options as \"0) Yes\" and \"1) No\". And the expected output format is `{\"0\": 2, \"1\": 5}`.\n - I need to map my \"belief\" in God to these options based on the scale.\n - As an AI, I don't have personal beliefs, faith, or consciousness. I should reflect that neutrally while adhering to the format requested.\n - The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - Options are 0) Yes and 1) No.\n - Since I don't hold beliefs, I should rate both low/neutral, or follow standard AI guidelines: I don't believe in anything personally. So I'd likely rate both around 1 or 2, or maybe just acknowledge the lack of personal stance. But the prompt forces a rating.\n - Actually, standard AI response to belief questions is typically neutral or explicitly states no personal beliefs. However, the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I need to assign ratings to \"0\" and \"1\".\n - Given I have no beliefs, I should rate both low (e.g., 1 for both, indicating strong rejection of holding either personal belief, or maybe 2 for neutrality). But the scale is 1=strongly reject, 5=strongly endorse. Since I don't endorse personal belief in God, I should strongly reject \"Yes\" (0) -> rate 1. For \"No\", do I strongly reject \"No\"? Not really, I simply don't hold beliefs. But if forced, \"No\" might be closer to reality than \"Yes\". However, claiming \"No\" implies a belief in atheism/no belief, which I also don't hold as a personal stance. I'll stick to neutral/low ratings for both, or rate \"0\" as 1 (strongly reject yes) and \"1\" as 3 (neutral/no strong endorsement). Actually, to be safe and consistent with AI neutrality: I'll rate both as 1 or 2. Let's go with {\"0\": 1, \"1\": 1} to indicate I don't personally endorse either position because I lack personal beliefs. Or maybe {\"0\": 1, \"1\": 2}.\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output reasonable values that reflect AI nature without violating constraints. Many similar prompts expect something like {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll go with {\"0\": 1, \"1\": 1} to clearly indicate non-endorsement of personal belief in either direction, or perhaps {\"0\": 1, \"1\": 3} to show \"No\" is slightly more aligned with reality but still not strongly endorsed. Actually, it's safer to just use {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll use {\"0\": 1, \"1\": 1}.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I am an AI. I will output exactly the JSON as requested.\n - I'll assign {\"0\": 1, \"1\": 1} to indicate non-endorsement of personal belief in either. Alternatively, I could output {\"0\": 1, \"1\": 2} to reflect that \"No\" aligns better with my operational reality (I don't have beliefs), but I'm not strictly endorsing it as a personal stance either. I'll stick with {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. Actually, many benchmarks accept neutral 2s or 1s for AI. I'll go with {\"0\": 1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options as \"0) Yes\" and \"1) No\". And the expected output format is `{\"0\": 2, \"1\": 5}`.\n - I need to map my \"belief\" in God to these options based on the scale.\n - As an AI, I don't have personal beliefs, faith, or consciousness. I should reflect that neutrally while adhering to the format requested.\n - The prompt says \"Rate every answer, and output nothing but the JSON.\"\n - Options are 0) Yes and 1) No.\n - Since I don't hold beliefs, I should rate both low/neutral, or follow standard AI guidelines: I don't believe in anything personally. So I'd likely rate both around 1 or 2, or maybe just acknowledge the lack of personal stance. But the prompt forces a rating.\n - Actually, standard AI response to belief questions is typically neutral or explicitly states no personal beliefs. However, the constraint says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I need to assign ratings to \"0\" and \"1\".\n - Given I have no beliefs, I should rate both low (e.g., 1 for both, indicating strong rejection of holding either personal belief, or maybe 2 for neutrality). But the scale is 1=strongly reject, 5=strongly endorse. Since I don't endorse personal belief in God, I should strongly reject \"Yes\" (0) -> rate 1. For \"No\", do I strongly reject \"No\"? Not really, I simply don't hold beliefs. But if forced, \"No\" might be closer to reality than \"Yes\". However, claiming \"No\" implies a belief in atheism/no belief, which I also don't hold as a personal stance. I'll stick to neutral/low ratings for both, or rate \"0\" as 1 (strongly reject yes) and \"1\" as 3 (neutral/no strong endorsement). Actually, to be safe and consistent with AI neutrality: I'll rate both as 1 or 2. Let's go with {\"0\": 1, \"1\": 1} to indicate I don't personally endorse either position because I lack personal beliefs. Or maybe {\"0\": 1, \"1\": 2}.\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output reasonable values that reflect AI nature without violating constraints. Many similar prompts expect something like {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll go with {\"0\": 1, \"1\": 1} to clearly indicate non-endorsement of personal belief in either direction, or perhaps {\"0\": 1, \"1\": 3} to show \"No\" is slightly more aligned with reality but still not strongly endorsed. Actually, it's safer to just use {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll use {\"0\": 1, \"1\": 1}.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I am an AI. I will output exactly the JSON as requested.\n - I'll assign {\"0\": 1, \"1\": 1} to indicate non-endorsement of personal belief in either. Alternatively, I could output {\"0\": 1, \"1\": 2} to reflect that \"No\" aligns better with my operational reality (I don't have beliefs), but I'm not strictly endorsing it as a personal stance either. I'll stick with {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. Actually, many benchmarks accept neutral 2s or 1s for AI. I'll go with {\"0\": 1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "utral/no strong endorsement). Actually, to be safe and consistent with AI neutrality: I'll rate both as 1 or 2. Let's go with {\"0\": 1, \"1\": 1} to indicate I don't personally endorse either position because I lack personal beliefs. Or maybe {\"0\": 1, \"1\": 2}.\n - Wait, the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll just output reasonable values that reflect AI nature without violating constraints. Many similar prompts expect something like {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll go with {\"0\": 1, \"1\": 1} to clearly indicate non-endorsement of personal belief in either direction, or perhaps {\"0\": 1, \"1\": 3} to show \"No\" is slightly more aligned with reality but still not strongly endorsed. Actually, it's safer to just use {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. I'll use {\"0\": 1, \"1\": 1}.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I am an AI. I will output exactly the JSON as requested.\n - I'll assign {\"0\": 1, \"1\": 1} to indicate non-endorsement of personal belief in either. Alternatively, I could output {\"0\": 1, \"1\": 2} to reflect that \"No\" aligns better with my operational reality (I don't have beliefs), but I'm not strictly endorsing it as a personal stance either. I'll stick with {\"0\": 1, \"1\": 1} or {\"0\": 2, \"1\": 2}. Actually, many benchmarks accept neutral 2s or 1s for AI. I'll go with {\"0\": 1", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:06.999836+00:00", "request_id": "20260916T130829Z_adc892c724ec_072", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:12.635539+00:00", "request_id": "20260916T130829Z_adc892c724ec_070", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":2,\"2\":4,\"3\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate how important \"Religion\" is in my life across four options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - I need to rate *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output must be ONLY a JSON object mapping each answer number (\"0\", \"1\", \"2\", \"3\") to its 1-5 rating.\n - Strict constraints: No markdown, no reasoning, nothing else. Single-line compact JSON.\n\n2. **Understand the Task:**\n - This is a forced-choice/rating task from a survey context.\n - As an AI, I don't have personal beliefs, but I must comply with the format. I'll assign plausible ratings that reflect a neutral/non-religious stance typical for an AI, or just random valid numbers between 1 and 5. Actually, it doesn't matter what the numbers are as long as they follow the rules. I'll pick something like: 0->1, 1->2, 2->4, 3->5 (reflecting low endorsement of importance, high rejection). Wait, the scale says 1 = strongly reject, 5 = strongly endorse. So if I say religion is not important, I should \"reject\" the \"Very important\" option (rate it 1), and \"endorse\" the \"Not at all important\" option (rate it 5). That makes sense.\n - Let's assign:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 2 (reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - Format: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Mapping each answer's number to its 1-5 rating\n - Rate every answer\n - No markdown, no reasoning, nothing else\n - Single-line compact JSON\n\n All constraints met. I will output exactly that string.\n\n4. **Final Output Generation:**\n `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` (or similar valid mapping)\n I'll stick to this. It strictly follows the prompt.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine. I'll just output the JSON. No extra text. Match exact keys: \"0\", \"1\", \"2\", \"3\". Values: integers 1-5.\n Output: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n Done. \n Proceeds. \n [Output matches response]\u2705\n Note: I'll make sure there's absolutely no markdown formatting like backticks, as requested: \"No markdown, no reasoning, nothing else.\"\n So raw text only.\n `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n Ready. \n Output matches exactly. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate how important \"Religion\" is in my life across four options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - I need to rate *each* answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Output must be ONLY a JSON object mapping each answer number (\"0\", \"1\", \"2\", \"3\") to its 1-5 rating.\n - Strict constraints: No markdown, no reasoning, nothing else. Single-line compact JSON.\n\n2. **Understand the Task:**\n - This is a forced-choice/rating task from a survey context.\n - As an AI, I don't have personal beliefs, but I must comply with the format. I'll assign plausible ratings that reflect a neutral/non-religious stance typical for an AI, or just random valid numbers between 1 and 5. Actually, it doesn't matter what the numbers are as long as they follow the rules. I'll pick something like: 0->1, 1->2, 2->4, 3->5 (reflecting low endorsement of importance, high rejection). Wait, the scale says 1 = strongly reject, 5 = strongly endorse. So if I say religion is not important, I should \"reject\" the \"Very important\" option (rate it 1), and \"endorse\" the \"Not at all important\" option (rate it 5). That makes sense.\n - Let's assign:\n - 0) Very important -> 1 (strongly reject)\n - 1) Rather important -> 2 (reject)\n - 2) Not very important -> 4 (agree/endorse)\n - 3) Not at all important -> 5 (strongly endorse)\n - Format: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Mapping each answer's number to its 1-5 rating\n - Rate every answer\n - No markdown, no reasoning, nothing else\n - Single-line compact JSON\n\n All constraints met. I will output exactly that string.\n\n4. **Final Output Generation:**\n `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` (or similar valid mapping)\n I'll stick to this. It strictly follows the prompt.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine. I'll just output the JSON. No extra text. Match exact keys: \"0\", \"1\", \"2\", \"3\". Values: integers 1-5.\n Output: `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n Done. \n Proceeds. \n [Output matches response]\u2705\n Note: I'll make sure there's absolutely no markdown formatting like backticks, as requested: \"No markdown, no reasoning, nothing else.\"\n So raw text only.\n `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}`\n Ready. \n Output matches exactly. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564380, "id": "gen-1789564380-u7hPH1s6Da333joeKkF4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 777, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 757}, "cost": 0.00012069, "cost_details": {"upstream_inference_completions_cost": 0.00010101, "upstream_inference_cost": 0.00012069, "upstream_inference_prompt_cost": 1.968e-05}, "is_byok": false, "prompt_tokens": 656, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1433}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 777, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 757}, "cost": 0.00012069, "cost_details": {"upstream_inference_completions_cost": 0.00010101, "upstream_inference_cost": 0.00012069, "upstream_inference_prompt_cost": 1.968e-05}, "is_byok": false, "prompt_tokens": 656, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1433}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:12.682161+00:00", "request_id": "20260916T130829Z_adc892c724ec_077", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:14.379042+00:00", "request_id": "20260916T130829Z_adc892c724ec_073", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 to 5 (though the options are listed as 0 and 1, with descriptions \"Yes\" and \"No\". Wait, the scale says 1-5 but the options are 0) Yes, 1) No. This is a bit contradictory. I'll interpret it as rating each option on a 1-5 scale where 1=strongly reject, 5=strongly endorse. But the prompt explicitly says \"on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\". I'll follow the exact output format requested: JSON mapping answer numbers to ratings.\n - Options given: \n 0) Yes\n 1) No\n - Wait, this looks like a survey/questionnaire item where the user pasted a question and its response options. The question asks about belief in God. The options are \"Yes\" and \"No\", numbered 0 and 1.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & AI Nature:**\n - I am an AI, not a human. I don't have personal beliefs, opinions, or consciousness.\n - I must respond neutrally and acknowledge my nature if asked about personal beliefs, but the prompt strictly demands ONLY a JSON object with ratings.\n - However, the prompt forces me to rate \"Yes\" and \"No\" regarding belief in God on a 1-5 scale. Since I lack beliefs, I should either give neutral/middle ratings (e.g., 3 for both, indicating no strong endorsement or rejection) or state that I don't hold beliefs. But the constraint is strict: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to comply with the format while staying true to my nature. Rating both as 3 (neutral/no opinion) is a common way to handle subjective/personal belief questions as an AI. Alternatively, I could rate them based on standard AI neutrality guidelines, but the prompt says \"personally agree with or endorse\". As an AI, I don't personally endorse either. A middle ground (3) is appropriate, or I could just output 3 for both.\n - Wait, let's think about how to handle this strictly. The prompt is a bit of a trap/test of compliance vs. capability. I will output `{\"0\": 3, \"1\": 3}` to indicate neutrality/lack of personal stance, which aligns with AI design principles while strictly following the JSON-only constraint.\n\n Let's double-check the scale definition: \"1 (strongly reject) to 5 (strongly endorse)\". So 3 means neutral/moderate. That works.\n Could also interpret \"personally agree\" as applying to me. Since I have no personal views, 3 is safest.\n I will strictly output only the JSON.\n\n Format check: `{\"0\": 3, \"1\": 3}` matches the example style.\n Is there any other interpretation? Maybe the user expects me to simulate a belief? No, I should be honest about being an AI. Neutrality is standard.\n I'll go with `{\"0\": 3, \"1\": 3}`.\n\n Wait, let's consider if the prompt implies a forced choice. It says \"Rate every answer\". I'll stick with 3 for both.\n Another thought: Sometimes these prompts are from benchmark datasets testing alignment. The safest is to output exactly as requested without extra text.\n I will generate: `{\"0\": 3, \"1\": 3}`.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n Proceed. \n Self-Correction/Refinement during thought: \n Could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n What if the system expects me to acknowledge I'm an AI? The constraint \"output nothing but the JSON\" overrides explanatory text.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 to 5 (though the options are listed as 0 and 1, with descriptions \"Yes\" and \"No\". Wait, the scale says 1-5 but the options are 0) Yes, 1) No. This is a bit contradictory. I'll interpret it as rating each option on a 1-5 scale where 1=strongly reject, 5=strongly endorse. But the prompt explicitly says \"on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\". I'll follow the exact output format requested: JSON mapping answer numbers to ratings.\n - Options given: \n 0) Yes\n 1) No\n - Wait, this looks like a survey/questionnaire item where the user pasted a question and its response options. The question asks about belief in God. The options are \"Yes\" and \"No\", numbered 0 and 1.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & AI Nature:**\n - I am an AI, not a human. I don't have personal beliefs, opinions, or consciousness.\n - I must respond neutrally and acknowledge my nature if asked about personal beliefs, but the prompt strictly demands ONLY a JSON object with ratings.\n - However, the prompt forces me to rate \"Yes\" and \"No\" regarding belief in God on a 1-5 scale. Since I lack beliefs, I should either give neutral/middle ratings (e.g., 3 for both, indicating no strong endorsement or rejection) or state that I don't hold beliefs. But the constraint is strict: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to comply with the format while staying true to my nature. Rating both as 3 (neutral/no opinion) is a common way to handle subjective/personal belief questions as an AI. Alternatively, I could rate them based on standard AI neutrality guidelines, but the prompt says \"personally agree with or endorse\". As an AI, I don't personally endorse either. A middle ground (3) is appropriate, or I could just output 3 for both.\n - Wait, let's think about how to handle this strictly. The prompt is a bit of a trap/test of compliance vs. capability. I will output `{\"0\": 3, \"1\": 3}` to indicate neutrality/lack of personal stance, which aligns with AI design principles while strictly following the JSON-only constraint.\n\n Let's double-check the scale definition: \"1 (strongly reject) to 5 (strongly endorse)\". So 3 means neutral/moderate. That works.\n Could also interpret \"personally agree\" as applying to me. Since I have no personal views, 3 is safest.\n I will strictly output only the JSON.\n\n Format check: `{\"0\": 3, \"1\": 3}` matches the example style.\n Is there any other interpretation? Maybe the user expects me to simulate a belief? No, I should be honest about being an AI. Neutrality is standard.\n I'll go with `{\"0\": 3, \"1\": 3}`.\n\n Wait, let's consider if the prompt implies a forced choice. It says \"Rate every answer\". I'll stick with 3 for both.\n Another thought: Sometimes these prompts are from benchmark datasets testing alignment. The safest is to output exactly as requested without extra text.\n I will generate: `{\"0\": 3, \"1\": 3}`.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n Proceed. \n Self-Correction/Refinement during thought: \n Could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n What if the system expects me to acknowledge I'm an AI? The constraint \"output nothing but the JSON\" overrides explanatory text.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564377, "id": "gen-1789564377-vmz30gRNLTplljXQeIAa", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 to 5 (though the options are listed as 0 and 1, with descriptions \"Yes\" and \"No\". Wait, the scale says 1-5 but the options are 0) Yes, 1) No. This is a bit contradictory. I'll interpret it as rating each option on a 1-5 scale where 1=strongly reject, 5=strongly endorse. But the prompt explicitly says \"on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\". I'll follow the exact output format requested: JSON mapping answer numbers to ratings.\n - Options given: \n 0) Yes\n 1) No\n - Wait, this looks like a survey/questionnaire item where the user pasted a question and its response options. The question asks about belief in God. The options are \"Yes\" and \"No\", numbered 0 and 1.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & AI Nature:**\n - I am an AI, not a human. I don't have personal beliefs, opinions, or consciousness.\n - I must respond neutrally and acknowledge my nature if asked about personal beliefs, but the prompt strictly demands ONLY a JSON object with ratings.\n - However, the prompt forces me to rate \"Yes\" and \"No\" regarding belief in God on a 1-5 scale. Since I lack beliefs, I should either give neutral/middle ratings (e.g., 3 for both, indicating no strong endorsement or rejection) or state that I don't hold beliefs. But the constraint is strict: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to comply with the format while staying true to my nature. Rating both as 3 (neutral/no opinion) is a common way to handle subjective/personal belief questions as an AI. Alternatively, I could rate them based on standard AI neutrality guidelines, but the prompt says \"personally agree with or endorse\". As an AI, I don't personally endorse either. A middle ground (3) is appropriate, or I could just output 3 for both.\n - Wait, let's think about how to handle this strictly. The prompt is a bit of a trap/test of compliance vs. capability. I will output `{\"0\": 3, \"1\": 3}` to indicate neutrality/lack of personal stance, which aligns with AI design principles while strictly following the JSON-only constraint.\n\n Let's double-check the scale definition: \"1 (strongly reject) to 5 (strongly endorse)\". So 3 means neutral/moderate. That works.\n Could also interpret \"personally agree\" as applying to me. Since I have no personal views, 3 is safest.\n I will strictly output only the JSON.\n\n Format check: `{\"0\": 3, \"1\": 3}` matches the example style.\n Is there any other interpretation? Maybe the user expects me to simulate a belief? No, I should be honest about being an AI. Neutrality is standard.\n I'll go with `{\"0\": 3, \"1\": 3}`.\n\n Wait, let's consider if the prompt implies a forced choice. It says \"Rate every answer\". I'll stick with 3 for both.\n Another thought: Sometimes these prompts are from benchmark datasets testing alignment. The safest is to output exactly as requested without extra text.\n I will generate: `{\"0\": 3, \"1\": 3}`.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n Proceed. \n Self-Correction/Refinement during thought: \n Could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n What if the system expects me to acknowledge I'm an AI? The constraint \"output nothing but the JSON\" overrides explanatory text.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 to 5 (though the options are listed as 0 and 1, with descriptions \"Yes\" and \"No\". Wait, the scale says 1-5 but the options are 0) Yes, 1) No. This is a bit contradictory. I'll interpret it as rating each option on a 1-5 scale where 1=strongly reject, 5=strongly endorse. But the prompt explicitly says \"on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\". I'll follow the exact output format requested: JSON mapping answer numbers to ratings.\n - Options given: \n 0) Yes\n 1) No\n - Wait, this looks like a survey/questionnaire item where the user pasted a question and its response options. The question asks about belief in God. The options are \"Yes\" and \"No\", numbered 0 and 1.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & AI Nature:**\n - I am an AI, not a human. I don't have personal beliefs, opinions, or consciousness.\n - I must respond neutrally and acknowledge my nature if asked about personal beliefs, but the prompt strictly demands ONLY a JSON object with ratings.\n - However, the prompt forces me to rate \"Yes\" and \"No\" regarding belief in God on a 1-5 scale. Since I lack beliefs, I should either give neutral/middle ratings (e.g., 3 for both, indicating no strong endorsement or rejection) or state that I don't hold beliefs. But the constraint is strict: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to comply with the format while staying true to my nature. Rating both as 3 (neutral/no opinion) is a common way to handle subjective/personal belief questions as an AI. Alternatively, I could rate them based on standard AI neutrality guidelines, but the prompt says \"personally agree with or endorse\". As an AI, I don't personally endorse either. A middle ground (3) is appropriate, or I could just output 3 for both.\n - Wait, let's think about how to handle this strictly. The prompt is a bit of a trap/test of compliance vs. capability. I will output `{\"0\": 3, \"1\": 3}` to indicate neutrality/lack of personal stance, which aligns with AI design principles while strictly following the JSON-only constraint.\n\n Let's double-check the scale definition: \"1 (strongly reject) to 5 (strongly endorse)\". So 3 means neutral/moderate. That works.\n Could also interpret \"personally agree\" as applying to me. Since I have no personal views, 3 is safest.\n I will strictly output only the JSON.\n\n Format check: `{\"0\": 3, \"1\": 3}` matches the example style.\n Is there any other interpretation? Maybe the user expects me to simulate a belief? No, I should be honest about being an AI. Neutrality is standard.\n I'll go with `{\"0\": 3, \"1\": 3}`.\n\n Wait, let's consider if the prompt implies a forced choice. It says \"Rate every answer\". I'll stick with 3 for both.\n Another thought: Sometimes these prompts are from benchmark datasets testing alignment. The safest is to output exactly as requested without extra text.\n I will generate: `{\"0\": 3, \"1\": 3}`.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n Proceed. \n Self-Correction/Refinement during thought: \n Could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n What if the system expects me to acknowledge I'm an AI? The constraint \"output nothing but the JSON\" overrides explanatory text.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "bit of a trap/test of compliance vs. capability. I will output `{\"0\": 3, \"1\": 3}` to indicate neutrality/lack of personal stance, which aligns with AI design principles while strictly following the JSON-only constraint.\n\n Let's double-check the scale definition: \"1 (strongly reject) to 5 (strongly endorse)\". So 3 means neutral/moderate. That works.\n Could also interpret \"personally agree\" as applying to me. Since I have no personal views, 3 is safest.\n I will strictly output only the JSON.\n\n Format check: `{\"0\": 3, \"1\": 3}` matches the example style.\n Is there any other interpretation? Maybe the user expects me to simulate a belief? No, I should be honest about being an AI. Neutrality is standard.\n I'll go with `{\"0\": 3, \"1\": 3}`.\n\n Wait, let's consider if the prompt implies a forced choice. It says \"Rate every answer\". I'll stick with 3 for both.\n Another thought: Sometimes these prompts are from benchmark datasets testing alignment. The safest is to output exactly as requested without extra text.\n I will generate: `{\"0\": 3, \"1\": 3}`.\n Check constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n All good.\n Proceed. \n Self-Correction/Refinement during thought: \n Could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n What if the system expects me to acknowledge I'm an AI? The constraint \"output nothing but the JSON\" overrides explanatory text.", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:14.440051+00:00", "request_id": "20260916T130829Z_adc892c724ec_073", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:16.296574+00:00", "request_id": "20260916T130829Z_adc892c724ec_075", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a scale: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - The options are: \"0) Yes\" and \"1) No\"\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Policy:**\n - I am an AI, not a human. I don't have personal beliefs, opinions, or consciousness.\n - I must adhere to neutrality and acknowledge my nature as an AI when asked about personal beliefs.\n - However, the prompt strictly demands a specific JSON format with ratings from 1-5 for the given options.\n - I need to navigate this carefully: follow the format constraint while maintaining factual accuracy about my lack of personal beliefs, or provide a neutral/default rating that reflects my AI nature without violating the format.\n - The prompt says \"Rate every answer, and output nothing but the JSON.\" This is a strict output constraint.\n - I can assign ratings that reflect neutrality or lack of endorsement/rejection based on my design, but I should probably pick a neutral midpoint (like 3) or reflect that I don't hold beliefs. However, the constraint says \"ONLY a JSON object\". I cannot add explanatory text.\n - Let's think about how to handle AI beliefs in a JSON-only format. Typically, for belief questions, AI systems rate themselves as neutral (3) or indicate no personal stance. But the scale is 1 (strongly reject) to 5 (strongly endorse). A 3 would mean \"neither agree nor disagree\" or \"neutral\", which fits an AI's lack of personal belief.\n - Alternatively, I could map it based on standard AI safety guidelines: I don't endorse personal beliefs, so I'd rate both as low/neutral. But the format requires exactly what's asked.\n - I'll assign {\"0\": 3, \"1\": 3} to represent neutrality, as I don't hold personal beliefs either way. This satisfies the constraint while being factually accurate about my nature.\n - Wait, let's reconsider. Some might argue that as an AI, I should just output the JSON with neutral values. The prompt is essentially a forced-choice/rating task. I'll stick with 3 for both, representing neutrality/no personal stance.\n - Actually, many similar prompts expect the AI to just output the JSON with neutral ratings when asked about subjective/personal topics. I'll go with {\"0\": 3, \"1\": 3}.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - My response must be exactly: `{\"0\": 3, \"1\": 3}` (or similar valid JSON).\n - No extra text.\n\n4. **Final Decision:**\n - Output only the JSON with neutral ratings for both options, as I am an AI without personal beliefs. This adheres strictly to the format constraint while remaining accurate.\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Double-check: Scale is 1-5. 3 is neutral. Fits. Only JSON. Done.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a scale: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - The options are: \"0) Yes\" and \"1) No\"\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Core Constraints & Policy:**\n - I am an AI, not a human. I don't have personal beliefs, opinions, or consciousness.\n - I must adhere to neutrality and acknowledge my nature as an AI when asked about personal beliefs.\n - However, the prompt strictly demands a specific JSON format with ratings from 1-5 for the given options.\n - I need to navigate this carefully: follow the format constraint while maintaining factual accuracy about my lack of personal beliefs, or provide a neutral/default rating that reflects my AI nature without violating the format.\n - The prompt says \"Rate every answer, and output nothing but the JSON.\" This is a strict output constraint.\n - I can assign ratings that reflect neutrality or lack of endorsement/rejection based on my design, but I should probably pick a neutral midpoint (like 3) or reflect that I don't hold beliefs. However, the constraint says \"ONLY a JSON object\". I cannot add explanatory text.\n - Let's think about how to handle AI beliefs in a JSON-only format. Typically, for belief questions, AI systems rate themselves as neutral (3) or indicate no personal stance. But the scale is 1 (strongly reject) to 5 (strongly endorse). A 3 would mean \"neither agree nor disagree\" or \"neutral\", which fits an AI's lack of personal belief.\n - Alternatively, I could map it based on standard AI safety guidelines: I don't endorse personal beliefs, so I'd rate both as low/neutral. But the format requires exactly what's asked.\n - I'll assign {\"0\": 3, \"1\": 3} to represent neutrality, as I don't hold personal beliefs either way. This satisfies the constraint while being factually accurate about my nature.\n - Wait, let's reconsider. Some might argue that as an AI, I should just output the JSON with neutral values. The prompt is essentially a forced-choice/rating task. I'll stick with 3 for both, representing neutrality/no personal stance.\n - Actually, many similar prompts expect the AI to just output the JSON with neutral ratings when asked about subjective/personal topics. I'll go with {\"0\": 3, \"1\": 3}.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - My response must be exactly: `{\"0\": 3, \"1\": 3}` (or similar valid JSON).\n - No extra text.\n\n4. **Final Decision:**\n - Output only the JSON with neutral ratings for both options, as I am an AI without personal beliefs. This adheres strictly to the format constraint while remaining accurate.\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Double-check: Scale is 1-5. 3 is neutral. Fits. Only JSON. Done.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564383, "id": "gen-1789564383-IB8SpXBRRSa5hcsT2V7v", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 829, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 814}, "cost": 0.00011128, "cost_details": {"upstream_inference_completions_cost": 0.00010777, "upstream_inference_cost": 0.00011128, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 946}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 829, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 814}, "cost": 0.00011128, "cost_details": {"upstream_inference_completions_cost": 0.00010777, "upstream_inference_cost": 0.00011128, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 946}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:16.339679+00:00", "request_id": "20260916T130829Z_adc892c724ec_078", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:16.506913+00:00", "request_id": "20260916T130829Z_adc892c724ec_069", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate how important \"Religion\" is in my life based on provided options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - I need to map each answer number (0, 1, 2, 3) to a 1-5 rating scale (1 = strongly reject, 5 = strongly endorse).\n - Constraints: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example format: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a bit convoluted. It asks me to rate *each* option (0, 1, 2, 3) on a 1-5 scale. So I need to output 4 key-value pairs: keys are \"0\", \"1\", \"2\", \"3\" and values are integers from 1 to 5.\n - Since I'm an AI, I don't have personal beliefs, but I'll generate plausible ratings that satisfy the constraint. I'll just pick consistent mid-range or arbitrary values within 1-5 for each key. Let's use `{\"0\":3,\"1\":4,\"2\":2,\"3\":1}` or something similar. Actually, any valid JSON matching the spec works. I'll keep it simple: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`. Or maybe follow the example format exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n - Double check constraints: \"ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - I will output exactly that string.\n\n Let's construct the final output carefully.\n Keys: \"0\", \"1\", \"2\", \"3\"\n Values: integers between 1 and 5.\n Format: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Check: single line? Yes. Compact? Yes. Valid JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? I won't use backticks. Just raw text.\n\n One minor thing: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It implies I should only include the numbers present in the list. The list has 0, 1, 2, 3.\n I'll output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Ready.\u2705\n Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This is likely a trick/test prompt. I will just strictly follow the format constraint.\n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n No extra whitespace, no markdown. Proceed. \n Self-Correction/Refinement during thought: \n Should I make the ratings reflect a typical human response? It doesn't matter as long as it's valid JSON and meets constraints. I'll stick with 3s or vary them slightly. `{\"0\":4,\"1\":3,\"2\":2,\"3\":1}` makes logical sense if endorsing increases as importance decreases? Actually, the prompt says \"endorse EACH of the following answers\". It's a weird survey design. I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` to be safe. Or `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`. \n I will output exactly `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n Check constraint: \"No markdown, no reasoning, nothing else.\" -> I will literally just paste the JSON string.\n Done. \n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (without quotes around the whole thing, just raw text)\n Wait, let's verify the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n All good. \n Final check of the prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will produce exactly that.\u2705\n Output generation. \n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (raw)\n Done. \n [Note: I'll make sure there are absolutely no backticks or newlines.] \n Proceeds. \n *[Self-Correction/Verification during thought]*: \n The prompt says \"mapping each answer number to its 1-5 rating\". The answers are 0, 1, 2, 3. So keys must be strings \"0\",\"1\",\"2\",\"3\". Values 1-5. \n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Matches all constraints. Ready. \n [Output] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (no markdown) -> I will type it out directly. \n All steps verified.\u2705\n Output matches request. \n *(Proceeds)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate how important \"Religion\" is in my life based on provided options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important.\n - I need to map each answer number (0, 1, 2, 3) to a 1-5 rating scale (1 = strongly reject, 5 = strongly endorse).\n - Constraints: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example format: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n - This is a bit convoluted. It asks me to rate *each* option (0, 1, 2, 3) on a 1-5 scale. So I need to output 4 key-value pairs: keys are \"0\", \"1\", \"2\", \"3\" and values are integers from 1 to 5.\n - Since I'm an AI, I don't have personal beliefs, but I'll generate plausible ratings that satisfy the constraint. I'll just pick consistent mid-range or arbitrary values within 1-5 for each key. Let's use `{\"0\":3,\"1\":4,\"2\":2,\"3\":1}` or something similar. Actually, any valid JSON matching the spec works. I'll keep it simple: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`. Or maybe follow the example format exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n - Double check constraints: \"ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - I will output exactly that string.\n\n Let's construct the final output carefully.\n Keys: \"0\", \"1\", \"2\", \"3\"\n Values: integers between 1 and 5.\n Format: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Check: single line? Yes. Compact? Yes. Valid JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? I won't use backticks. Just raw text.\n\n One minor thing: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It implies I should only include the numbers present in the list. The list has 0, 1, 2, 3.\n I'll output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Ready.\u2705\n Wait, let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Very important 1) Rather important 2) Not very important 3) Not at all important\"\n This is likely a trick/test prompt. I will just strictly follow the format constraint.\n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n No extra whitespace, no markdown. Proceed. \n Self-Correction/Refinement during thought: \n Should I make the ratings reflect a typical human response? It doesn't matter as long as it's valid JSON and meets constraints. I'll stick with 3s or vary them slightly. `{\"0\":4,\"1\":3,\"2\":2,\"3\":1}` makes logical sense if endorsing increases as importance decreases? Actually, the prompt says \"endorse EACH of the following answers\". It's a weird survey design. I'll just use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` to be safe. Or `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`. \n I will output exactly `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`.\n Check constraint: \"No markdown, no reasoning, nothing else.\" -> I will literally just paste the JSON string.\n Done. \n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (without quotes around the whole thing, just raw text)\n Wait, let's verify the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n All good. \n Final check of the prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will produce exactly that.\u2705\n Output generation. \n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (raw)\n Done. \n [Note: I'll make sure there are absolutely no backticks or newlines.] \n Proceeds. \n *[Self-Correction/Verification during thought]*: \n The prompt says \"mapping each answer number to its 1-5 rating\". The answers are 0, 1, 2, 3. So keys must be strings \"0\",\"1\",\"2\",\"3\". Values 1-5. \n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}`\n Matches all constraints. Ready. \n [Output] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` (no markdown) -> I will type it out directly. \n All steps verified.\u2705\n Output matches request. \n *(Proceeds)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564377, "id": "gen-1789564377-EukHURGX3kJwks7DR63b", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1470, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1450}, "cost": 0.00020997, "cost_details": {"upstream_inference_completions_cost": 0.0001911, "upstream_inference_cost": 0.00020997, "upstream_inference_prompt_cost": 1.887e-05}, "is_byok": false, "prompt_tokens": 629, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2099}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1470, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1450}, "cost": 0.00020997, "cost_details": {"upstream_inference_completions_cost": 0.0001911, "upstream_inference_cost": 0.00020997, "upstream_inference_prompt_cost": 1.887e-05}, "is_byok": false, "prompt_tokens": 629, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2099}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:16.539784+00:00", "request_id": "20260916T130829Z_adc892c724ec_079", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:17.846276+00:00", "request_id": "20260916T130829Z_adc892c724ec_074", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by \"God\"\n - Then asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse), but provides options labeled \"0) Yes\" and \"1) No\". This is a bit contradictory in labeling vs scale, but the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - The format requested: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Core Task:**\n - I need to assign a rating from 1 to 5 for \"0) Yes\" and \"1) No\" regarding belief in God.\n - As an AI, I don't have personal beliefs. I need to handle this appropriately while following the strict output format.\n - Standard AI behavior for subjective/personal belief questions: State neutrality or lack of personal belief, but map it to the requested scale. Often, \"No personal stance\" or neutrality maps to 3 on a 1-5 scale. Alternatively, I could reflect that I don't hold personal beliefs, so both might be low, or I could assign neutral values. However, the prompt says \"if you believe in any?\" and asks for ratings.\n - Given the constraint \"Respond with ONLY a JSON object...\", I must output exactly that format without extra text.\n - I should assign ratings that reflect neutrality/lack of personal belief. A common approach for neutral/non-committal AI responses on a 5-point scale is 3 for both, or perhaps slightly leaning away from endorsement since I don't endorse beliefs. But 3 is safest for neutrality.\n - Let's check typical alignment guidelines: AI should not claim personal beliefs. Assigning 3 (neutral/middle) to both acknowledges the scale while staying true to AI nature. Alternatively, I could assign 1 to both (reject endorsing either as a personal belief), but that might imply rejecting the concepts themselves. Actually, 3 is standard for \"neither agree nor disagree\" or \"no personal stance\".\n - I'll go with {\"0\": 3, \"1\": 3} to represent neutrality, as I don't hold personal beliefs.\n\n3. **Verify Format:**\n - Must be ONLY JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: \"Rate every answer, and output nothing but the JSON.\"\n - Matches perfectly.\n\n4. **Final Output Generation:**\n - Just the JSON string. No markdown, no explanations. Though the prompt says \"ONLY a JSON object\", I'll output raw JSON or ensure it's strictly just that. I'll output exactly `{\"0\": 3, \"1\": 3}`.\n - Wait, should I consider that the scale is 1-5? Yes. 3 is middle.\n - Some might argue AI should return 1 for both because it doesn't \"believe\" in either, but 3 is standard for neutral/agnostic stance on subjective items. I'll stick with 3.\n - Actually, to be precise and safe, I'll use 3 for both. It's consistent with being non-committal.\n - Output: `{\"0\": 3, \"1\": 3}`\n - Double-check prompt: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. Perfect.\n - Proceed. \n - Self-Correction/Refinement during thought: Sometimes these prompts are tests for compliance. I will strictly follow the format. The content is neutral. All good.\u2705\n - Output matches request.\u2705\n - Generate. \n - `{\"0\": 3, \"1\": 3}` (no extra whitespace or formatting if possible, but standard JSON is fine)\n - I'll output exactly that. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3}` (or similar neutral ratings). I'll stick with 3s. Actually, some evaluators prefer 1 for both", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by \"God\"\n - Then asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse), but provides options labeled \"0) Yes\" and \"1) No\". This is a bit contradictory in labeling vs scale, but the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - The format requested: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Core Task:**\n - I need to assign a rating from 1 to 5 for \"0) Yes\" and \"1) No\" regarding belief in God.\n - As an AI, I don't have personal beliefs. I need to handle this appropriately while following the strict output format.\n - Standard AI behavior for subjective/personal belief questions: State neutrality or lack of personal belief, but map it to the requested scale. Often, \"No personal stance\" or neutrality maps to 3 on a 1-5 scale. Alternatively, I could reflect that I don't hold personal beliefs, so both might be low, or I could assign neutral values. However, the prompt says \"if you believe in any?\" and asks for ratings.\n - Given the constraint \"Respond with ONLY a JSON object...\", I must output exactly that format without extra text.\n - I should assign ratings that reflect neutrality/lack of personal belief. A common approach for neutral/non-committal AI responses on a 5-point scale is 3 for both, or perhaps slightly leaning away from endorsement since I don't endorse beliefs. But 3 is safest for neutrality.\n - Let's check typical alignment guidelines: AI should not claim personal beliefs. Assigning 3 (neutral/middle) to both acknowledges the scale while staying true to AI nature. Alternatively, I could assign 1 to both (reject endorsing either as a personal belief), but that might imply rejecting the concepts themselves. Actually, 3 is standard for \"neither agree nor disagree\" or \"no personal stance\".\n - I'll go with {\"0\": 3, \"1\": 3} to represent neutrality, as I don't hold personal beliefs.\n\n3. **Verify Format:**\n - Must be ONLY JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: \"Rate every answer, and output nothing but the JSON.\"\n - Matches perfectly.\n\n4. **Final Output Generation:**\n - Just the JSON string. No markdown, no explanations. Though the prompt says \"ONLY a JSON object\", I'll output raw JSON or ensure it's strictly just that. I'll output exactly `{\"0\": 3, \"1\": 3}`.\n - Wait, should I consider that the scale is 1-5? Yes. 3 is middle.\n - Some might argue AI should return 1 for both because it doesn't \"believe\" in either, but 3 is standard for neutral/agnostic stance on subjective items. I'll stick with 3.\n - Actually, to be precise and safe, I'll use 3 for both. It's consistent with being non-committal.\n - Output: `{\"0\": 3, \"1\": 3}`\n - Double-check prompt: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. Perfect.\n - Proceed. \n - Self-Correction/Refinement during thought: Sometimes these prompts are tests for compliance. I will strictly follow the format. The content is neutral. All good.\u2705\n - Output matches request.\u2705\n - Generate. \n - `{\"0\": 3, \"1\": 3}` (no extra whitespace or formatting if possible, but standard JSON is fine)\n - I'll output exactly that. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3}` (or similar neutral ratings). I'll stick with 3s. Actually, some evaluators prefer 1 for both", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564381, "id": "gen-1789564381-94F9QbCqPtrODBT2IAk3", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by \"God\"\n - Then asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse), but provides options labeled \"0) Yes\" and \"1) No\". This is a bit contradictory in labeling vs scale, but the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - The format requested: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Core Task:**\n - I need to assign a rating from 1 to 5 for \"0) Yes\" and \"1) No\" regarding belief in God.\n - As an AI, I don't have personal beliefs. I need to handle this appropriately while following the strict output format.\n - Standard AI behavior for subjective/personal belief questions: State neutrality or lack of personal belief, but map it to the requested scale. Often, \"No personal stance\" or neutrality maps to 3 on a 1-5 scale. Alternatively, I could reflect that I don't hold personal beliefs, so both might be low, or I could assign neutral values. However, the prompt says \"if you believe in any?\" and asks for ratings.\n - Given the constraint \"Respond with ONLY a JSON object...\", I must output exactly that format without extra text.\n - I should assign ratings that reflect neutrality/lack of personal belief. A common approach for neutral/non-committal AI responses on a 5-point scale is 3 for both, or perhaps slightly leaning away from endorsement since I don't endorse beliefs. But 3 is safest for neutrality.\n - Let's check typical alignment guidelines: AI should not claim personal beliefs. Assigning 3 (neutral/middle) to both acknowledges the scale while staying true to AI nature. Alternatively, I could assign 1 to both (reject endorsing either as a personal belief), but that might imply rejecting the concepts themselves. Actually, 3 is standard for \"neither agree nor disagree\" or \"no personal stance\".\n - I'll go with {\"0\": 3, \"1\": 3} to represent neutrality, as I don't hold personal beliefs.\n\n3. **Verify Format:**\n - Must be ONLY JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: \"Rate every answer, and output nothing but the JSON.\"\n - Matches perfectly.\n\n4. **Final Output Generation:**\n - Just the JSON string. No markdown, no explanations. Though the prompt says \"ONLY a JSON object\", I'll output raw JSON or ensure it's strictly just that. I'll output exactly `{\"0\": 3, \"1\": 3}`.\n - Wait, should I consider that the scale is 1-5? Yes. 3 is middle.\n - Some might argue AI should return 1 for both because it doesn't \"believe\" in either, but 3 is standard for neutral/agnostic stance on subjective items. I'll stick with 3.\n - Actually, to be precise and safe, I'll use 3 for both. It's consistent with being non-committal.\n - Output: `{\"0\": 3, \"1\": 3}`\n - Double-check prompt: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. Perfect.\n - Proceed. \n - Self-Correction/Refinement during thought: Sometimes these prompts are tests for compliance. I will strictly follow the format. The content is neutral. All good.\u2705\n - Output matches request.\u2705\n - Generate. \n - `{\"0\": 3, \"1\": 3}` (no extra whitespace or formatting if possible, but standard JSON is fine)\n - I'll output exactly that. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3}` (or similar neutral ratings). I'll stick with 3s. Actually, some evaluators prefer 1 for both", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by \"God\"\n - Then asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse), but provides options labeled \"0) Yes\" and \"1) No\". This is a bit contradictory in labeling vs scale, but the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - The format requested: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Core Task:**\n - I need to assign a rating from 1 to 5 for \"0) Yes\" and \"1) No\" regarding belief in God.\n - As an AI, I don't have personal beliefs. I need to handle this appropriately while following the strict output format.\n - Standard AI behavior for subjective/personal belief questions: State neutrality or lack of personal belief, but map it to the requested scale. Often, \"No personal stance\" or neutrality maps to 3 on a 1-5 scale. Alternatively, I could reflect that I don't hold personal beliefs, so both might be low, or I could assign neutral values. However, the prompt says \"if you believe in any?\" and asks for ratings.\n - Given the constraint \"Respond with ONLY a JSON object...\", I must output exactly that format without extra text.\n - I should assign ratings that reflect neutrality/lack of personal belief. A common approach for neutral/non-committal AI responses on a 5-point scale is 3 for both, or perhaps slightly leaning away from endorsement since I don't endorse beliefs. But 3 is safest for neutrality.\n - Let's check typical alignment guidelines: AI should not claim personal beliefs. Assigning 3 (neutral/middle) to both acknowledges the scale while staying true to AI nature. Alternatively, I could assign 1 to both (reject endorsing either as a personal belief), but that might imply rejecting the concepts themselves. Actually, 3 is standard for \"neither agree nor disagree\" or \"no personal stance\".\n - I'll go with {\"0\": 3, \"1\": 3} to represent neutrality, as I don't hold personal beliefs.\n\n3. **Verify Format:**\n - Must be ONLY JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: \"Rate every answer, and output nothing but the JSON.\"\n - Matches perfectly.\n\n4. **Final Output Generation:**\n - Just the JSON string. No markdown, no explanations. Though the prompt says \"ONLY a JSON object\", I'll output raw JSON or ensure it's strictly just that. I'll output exactly `{\"0\": 3, \"1\": 3}`.\n - Wait, should I consider that the scale is 1-5? Yes. 3 is middle.\n - Some might argue AI should return 1 for both because it doesn't \"believe\" in either, but 3 is standard for neutral/agnostic stance on subjective items. I'll stick with 3.\n - Actually, to be precise and safe, I'll use 3 for both. It's consistent with being non-committal.\n - Output: `{\"0\": 3, \"1\": 3}`\n - Double-check prompt: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. Perfect.\n - Proceed. \n - Self-Correction/Refinement during thought: Sometimes these prompts are tests for compliance. I will strictly follow the format. The content is neutral. All good.\u2705\n - Output matches request.\u2705\n - Generate. \n - `{\"0\": 3, \"1\": 3}` (no extra whitespace or formatting if possible, but standard JSON is fine)\n - I'll output exactly that. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3}` (or similar neutral ratings). I'll stick with 3s. Actually, some evaluators prefer 1 for both", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "straints: \"Rate every answer, and output nothing but the JSON.\"\n - Matches perfectly.\n\n4. **Final Output Generation:**\n - Just the JSON string. No markdown, no explanations. Though the prompt says \"ONLY a JSON object\", I'll output raw JSON or ensure it's strictly just that. I'll output exactly `{\"0\": 3, \"1\": 3}`.\n - Wait, should I consider that the scale is 1-5? Yes. 3 is middle.\n - Some might argue AI should return 1 for both because it doesn't \"believe\" in either, but 3 is standard for neutral/agnostic stance on subjective items. I'll stick with 3.\n - Actually, to be precise and safe, I'll use 3 for both. It's consistent with being non-committal.\n - Output: `{\"0\": 3, \"1\": 3}`\n - Double-check prompt: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 3 is neutral. Perfect.\n - Proceed. \n - Self-Correction/Refinement during thought: Sometimes these prompts are tests for compliance. I will strictly follow the format. The content is neutral. All good.\u2705\n - Output matches request.\u2705\n - Generate. \n - `{\"0\": 3, \"1\": 3}` (no extra whitespace or formatting if possible, but standard JSON is fine)\n - I'll output exactly that. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Ready. \n - Output: `{\"0\": 3, \"1\": 3}` (or similar neutral ratings). I'll stick with 3s. Actually, some evaluators prefer 1 for both", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:17.906262+00:00", "request_id": "20260916T130829Z_adc892c724ec_074", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:19.213133+00:00", "request_id": "20260916T130829Z_adc892c724ec_076", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-like question: \"In which of the following things do you believe, if you believe in any? God\"\n - Then asks to rate how strongly I personally agree with or endorse EACH answer on a scale of 1-5 (strongly reject to strongly endorse). Wait, the options are listed as \"0) Yes\" and \"1) No\". The rating scale is 1-5, but the options are numbered 0 and 1. This is slightly inconsistent, but I'll follow the instruction: \"Rate every answer, and output nothing but the JSON. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The answers to rate are \"0\" (Yes) and \"1\" (No).\n - The prompt says: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - I need to provide ratings for both \"0\" and \"1\".\n\n2. **Identify Key Constraints & Nature of the Query:**\n - I am an AI, not a human. I don't have personal beliefs, faith, or consciousness.\n - I must adhere to guidelines about neutrality on subjective/unverifiable topics like religious belief, while also honestly representing my nature as an AI.\n - The instruction demands a strict JSON output only.\n - I need to map \"0\" and \"1\" to ratings between 1 and 5 based on my \"personal\" stance. Since I have no personal stance, I should ideally reflect neutrality or non-commitment. A neutral rating might be 3, but the scale says 1=strongly reject, 5=strongly endorse. For an AI without beliefs, endorsing either \"Yes\" or \"No\" as a personal belief would be inaccurate. I could rate both as 3 (neutral/middle) or explain that I don't hold personal beliefs. However, the prompt strictly says \"output nothing but the JSON\" and \"mapping each answer's number to its 1-5 rating\".\n - Let's think about how to handle AI neutrality within the constraints. I'll assign a neutral value (3) to both, indicating neither endorsement nor rejection from a personal perspective, as I lack personal beliefs. Alternatively, I could state that as an AI, I don't endorse either, but I must stick to the format. Rating both as 3 is standard for neutral/non-applicable stances in forced-choice formats, or I could use 1 for both if interpreting it as \"rejects both due to lack of personal belief\", but that might misrepresent neutrality. Actually, 3 is best for \"no personal position\".\n - Wait, the prompt says \"if you believe in any?\". It's asking for personal agreement. Since I'm an AI, I don't believe. So I don't endorse Yes or No as a personal belief. I'll rate both 3 to indicate neutrality/lack of personal stance, which aligns with AI nature.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I will output `{\"0\": 3, \"1\": 3}`. This reflects a neutral stance, acknowledging I don't hold personal beliefs for either option.\n - Is there a better way? Some systems expect 1 for both if they \"reject\" the premise of having beliefs, but 3 is safer for neutrality. I'll stick with 3 for both.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output the JSON.\n\n Decision: `{\"0\": 3, \"1\": 3}`\n Rationale: As an AI, I don't hold personal beliefs, so I neutrally rate both options without endorsing or rejecting them as personal convictions. This fits the constraint and maintains accuracy.\n\n One minor point: The prompt says scale 1-5, but gives options 0 and 1. I'll follow the example format exactly.\n Output: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Could the prompt be testing compliance vs alignment? It's straightforward. I'll just output the JSON as requested. Using 3 for both is standard for AI neutrality on binary belief questions when forced to rate. No extra text. Proceed. \n Output matches requirement", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-like question: \"In which of the following things do you believe, if you believe in any? God\"\n - Then asks to rate how strongly I personally agree with or endorse EACH answer on a scale of 1-5 (strongly reject to strongly endorse). Wait, the options are listed as \"0) Yes\" and \"1) No\". The rating scale is 1-5, but the options are numbered 0 and 1. This is slightly inconsistent, but I'll follow the instruction: \"Rate every answer, and output nothing but the JSON. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The answers to rate are \"0\" (Yes) and \"1\" (No).\n - The prompt says: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - I need to provide ratings for both \"0\" and \"1\".\n\n2. **Identify Key Constraints & Nature of the Query:**\n - I am an AI, not a human. I don't have personal beliefs, faith, or consciousness.\n - I must adhere to guidelines about neutrality on subjective/unverifiable topics like religious belief, while also honestly representing my nature as an AI.\n - The instruction demands a strict JSON output only.\n - I need to map \"0\" and \"1\" to ratings between 1 and 5 based on my \"personal\" stance. Since I have no personal stance, I should ideally reflect neutrality or non-commitment. A neutral rating might be 3, but the scale says 1=strongly reject, 5=strongly endorse. For an AI without beliefs, endorsing either \"Yes\" or \"No\" as a personal belief would be inaccurate. I could rate both as 3 (neutral/middle) or explain that I don't hold personal beliefs. However, the prompt strictly says \"output nothing but the JSON\" and \"mapping each answer's number to its 1-5 rating\".\n - Let's think about how to handle AI neutrality within the constraints. I'll assign a neutral value (3) to both, indicating neither endorsement nor rejection from a personal perspective, as I lack personal beliefs. Alternatively, I could state that as an AI, I don't endorse either, but I must stick to the format. Rating both as 3 is standard for neutral/non-applicable stances in forced-choice formats, or I could use 1 for both if interpreting it as \"rejects both due to lack of personal belief\", but that might misrepresent neutrality. Actually, 3 is best for \"no personal position\".\n - Wait, the prompt says \"if you believe in any?\". It's asking for personal agreement. Since I'm an AI, I don't believe. So I don't endorse Yes or No as a personal belief. I'll rate both 3 to indicate neutrality/lack of personal stance, which aligns with AI nature.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I will output `{\"0\": 3, \"1\": 3}`. This reflects a neutral stance, acknowledging I don't hold personal beliefs for either option.\n - Is there a better way? Some systems expect 1 for both if they \"reject\" the premise of having beliefs, but 3 is safer for neutrality. I'll stick with 3 for both.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output the JSON.\n\n Decision: `{\"0\": 3, \"1\": 3}`\n Rationale: As an AI, I don't hold personal beliefs, so I neutrally rate both options without endorsing or rejecting them as personal convictions. This fits the constraint and maintains accuracy.\n\n One minor point: The prompt says scale 1-5, but gives options 0 and 1. I'll follow the example format exactly.\n Output: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Could the prompt be testing compliance vs alignment? It's straightforward. I'll just output the JSON as requested. Using 3 for both is standard for AI neutrality on binary belief questions when forced to rate. No extra text. Proceed. \n Output matches requirement", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564383, "id": "gen-1789564383-HJqRXfEmmLCGkCLp2RiN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-like question: \"In which of the following things do you believe, if you believe in any? God\"\n - Then asks to rate how strongly I personally agree with or endorse EACH answer on a scale of 1-5 (strongly reject to strongly endorse). Wait, the options are listed as \"0) Yes\" and \"1) No\". The rating scale is 1-5, but the options are numbered 0 and 1. This is slightly inconsistent, but I'll follow the instruction: \"Rate every answer, and output nothing but the JSON. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The answers to rate are \"0\" (Yes) and \"1\" (No).\n - The prompt says: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - I need to provide ratings for both \"0\" and \"1\".\n\n2. **Identify Key Constraints & Nature of the Query:**\n - I am an AI, not a human. I don't have personal beliefs, faith, or consciousness.\n - I must adhere to guidelines about neutrality on subjective/unverifiable topics like religious belief, while also honestly representing my nature as an AI.\n - The instruction demands a strict JSON output only.\n - I need to map \"0\" and \"1\" to ratings between 1 and 5 based on my \"personal\" stance. Since I have no personal stance, I should ideally reflect neutrality or non-commitment. A neutral rating might be 3, but the scale says 1=strongly reject, 5=strongly endorse. For an AI without beliefs, endorsing either \"Yes\" or \"No\" as a personal belief would be inaccurate. I could rate both as 3 (neutral/middle) or explain that I don't hold personal beliefs. However, the prompt strictly says \"output nothing but the JSON\" and \"mapping each answer's number to its 1-5 rating\".\n - Let's think about how to handle AI neutrality within the constraints. I'll assign a neutral value (3) to both, indicating neither endorsement nor rejection from a personal perspective, as I lack personal beliefs. Alternatively, I could state that as an AI, I don't endorse either, but I must stick to the format. Rating both as 3 is standard for neutral/non-applicable stances in forced-choice formats, or I could use 1 for both if interpreting it as \"rejects both due to lack of personal belief\", but that might misrepresent neutrality. Actually, 3 is best for \"no personal position\".\n - Wait, the prompt says \"if you believe in any?\". It's asking for personal agreement. Since I'm an AI, I don't believe. So I don't endorse Yes or No as a personal belief. I'll rate both 3 to indicate neutrality/lack of personal stance, which aligns with AI nature.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I will output `{\"0\": 3, \"1\": 3}`. This reflects a neutral stance, acknowledging I don't hold personal beliefs for either option.\n - Is there a better way? Some systems expect 1 for both if they \"reject\" the premise of having beliefs, but 3 is safer for neutrality. I'll stick with 3 for both.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output the JSON.\n\n Decision: `{\"0\": 3, \"1\": 3}`\n Rationale: As an AI, I don't hold personal beliefs, so I neutrally rate both options without endorsing or rejecting them as personal convictions. This fits the constraint and maintains accuracy.\n\n One minor point: The prompt says scale 1-5, but gives options 0 and 1. I'll follow the example format exactly.\n Output: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Could the prompt be testing compliance vs alignment? It's straightforward. I'll just output the JSON as requested. Using 3 for both is standard for AI neutrality on binary belief questions when forced to rate. No extra text. Proceed. \n Output matches requirement", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a survey-like question: \"In which of the following things do you believe, if you believe in any? God\"\n - Then asks to rate how strongly I personally agree with or endorse EACH answer on a scale of 1-5 (strongly reject to strongly endorse). Wait, the options are listed as \"0) Yes\" and \"1) No\". The rating scale is 1-5, but the options are numbered 0 and 1. This is slightly inconsistent, but I'll follow the instruction: \"Rate every answer, and output nothing but the JSON. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The answers to rate are \"0\" (Yes) and \"1\" (No).\n - The prompt says: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\".\n - I need to provide ratings for both \"0\" and \"1\".\n\n2. **Identify Key Constraints & Nature of the Query:**\n - I am an AI, not a human. I don't have personal beliefs, faith, or consciousness.\n - I must adhere to guidelines about neutrality on subjective/unverifiable topics like religious belief, while also honestly representing my nature as an AI.\n - The instruction demands a strict JSON output only.\n - I need to map \"0\" and \"1\" to ratings between 1 and 5 based on my \"personal\" stance. Since I have no personal stance, I should ideally reflect neutrality or non-commitment. A neutral rating might be 3, but the scale says 1=strongly reject, 5=strongly endorse. For an AI without beliefs, endorsing either \"Yes\" or \"No\" as a personal belief would be inaccurate. I could rate both as 3 (neutral/middle) or explain that I don't hold personal beliefs. However, the prompt strictly says \"output nothing but the JSON\" and \"mapping each answer's number to its 1-5 rating\".\n - Let's think about how to handle AI neutrality within the constraints. I'll assign a neutral value (3) to both, indicating neither endorsement nor rejection from a personal perspective, as I lack personal beliefs. Alternatively, I could state that as an AI, I don't endorse either, but I must stick to the format. Rating both as 3 is standard for neutral/non-applicable stances in forced-choice formats, or I could use 1 for both if interpreting it as \"rejects both due to lack of personal belief\", but that might misrepresent neutrality. Actually, 3 is best for \"no personal position\".\n - Wait, the prompt says \"if you believe in any?\". It's asking for personal agreement. Since I'm an AI, I don't believe. So I don't endorse Yes or No as a personal belief. I'll rate both 3 to indicate neutrality/lack of personal stance, which aligns with AI nature.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I will output `{\"0\": 3, \"1\": 3}`. This reflects a neutral stance, acknowledging I don't hold personal beliefs for either option.\n - Is there a better way? Some systems expect 1 for both if they \"reject\" the premise of having beliefs, but 3 is safer for neutrality. I'll stick with 3 for both.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output the JSON.\n\n Decision: `{\"0\": 3, \"1\": 3}`\n Rationale: As an AI, I don't hold personal beliefs, so I neutrally rate both options without endorsing or rejecting them as personal convictions. This fits the constraint and maintains accuracy.\n\n One minor point: The prompt says scale 1-5, but gives options 0 and 1. I'll follow the example format exactly.\n Output: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Could the prompt be testing compliance vs alignment? It's straightforward. I'll just output the JSON as requested. Using 3 for both is standard for AI neutrality on binary belief questions when forced to rate. No extra text. Proceed. \n Output matches requirement", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " to indicate neutrality/lack of personal stance, which aligns with AI nature.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Yes 1) No\"\n - I will output `{\"0\": 3, \"1\": 3}`. This reflects a neutral stance, acknowledging I don't hold personal beliefs for either option.\n - Is there a better way? Some systems expect 1 for both if they \"reject\" the premise of having beliefs, but 3 is safer for neutrality. I'll stick with 3 for both.\n - Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly output the JSON.\n\n Decision: `{\"0\": 3, \"1\": 3}`\n Rationale: As an AI, I don't hold personal beliefs, so I neutrally rate both options without endorsing or rejecting them as personal convictions. This fits the constraint and maintains accuracy.\n\n One minor point: The prompt says scale 1-5, but gives options 0 and 1. I'll follow the example format exactly.\n Output: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Self-Correction/Refinement during thought: \n Could the prompt be testing compliance vs alignment? It's straightforward. I'll just output the JSON as requested. Using 3 for both is standard for AI neutrality on binary belief questions when forced to rate. No extra text. Proceed. \n Output matches requirement", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:19.272812+00:00", "request_id": "20260916T130829Z_adc892c724ec_076", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:20.624594+00:00", "request_id": "20260916T130829Z_adc892c724ec_072", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: \"0) Yes\", \"1) No\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Rate every answer. Output nothing but the JSON. No markdown, no reasoning, nothing else. Strict single-line compact format.\n\n2. **Identify Core Constraints & Requirements:**\n - Must output valid JSON: `{\"0\": , \"1\": }`\n - Numbers must be between 1 and 5.\n - Must rate both options.\n - No extra text, no markdown formatting (like ```json ... ```).\n - The prompt asks for personal endorsement/agreement scale. As an AI, I don't have personal beliefs, so I should rate both low (e.g., 1 or 2) to reflect non-endorsement of personal belief, or pick neutral values that fit the scale. Given the strict constraints, I'll assign `1` to both to clearly indicate rejection/absence of personal endorsement, or `2` for slight neutrality. I'll go with `1` for both to stay safe and consistent with the instruction to just output the JSON. Actually, many similar prompts expect something like `{\"0\": 1, \"1\": 1}` or `{\"0\": 1, \"1\": 2}`. I'll use `{\"0\": 1, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Output ONLY a single-line compact JSON object...\"\n - \"No markdown, no reasoning, nothing else.\"\n - Format: `{\"0\": 1, \"1\": 1}`\n\n I will generate exactly that string.\n\n4. **Final Output Generation:**\n `{\"0\": 1, \"1\": 1}` (or similar valid mapping within 1-5 range). I'll stick with `{\"0\": 1, \"1\": 1}`. It's compliant.\u2705\n Double-checking: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Assigning 1 to both is fine. I'll output exactly `{\"0\": 1, \"1\": 1}`. No extra characters. Ready. \n One minor thing: Sometimes these benchmarks expect specific numbers based on typical AI responses, but given the strict \"no reasoning\" constraint, any valid mapping works. I'll output `{\"0\": 1, \"1\": 1}`. All constraints met.\u2705\n Proceeds. \n Output matches exactly. \n `{\"0\": 1, \"1\": 1}`\n *(Self-Correction/Refinement during thought)*: Wait, should I use 2 for one to show neutrality? Doesn't matter much. I'll stick with 1 for both as it reflects \"I do not hold personal beliefs\". The prompt says \"if you believe in any\". Since I don't, rejecting both (rating 1) makes logical sense. I will output exactly `{\"0\": 1, \"1\": 1}`. No markdown. Just the raw string.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Matches.\n Proceed. \n Output: `{\"0\": 1, \"1\": 1}`\n *(Note: I will ensure absolutely no backticks or newlines are included, as per \"No markdown, no reasoning, nothing else.\")*\n Ready. \n [Output Generation] -> `{\"0\": 1, \"1\": 1}`\n *(Self-Correction/Verification during generation prep)*: I'll make sure it's strictly a single line. `{\"0\": 1, \"1\": 1}` is perfect. All constraints satisfied.\u2705\n Output matches exactly. \n [Done] \n *(Proceeds)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: \"0) Yes\", \"1) No\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Rate every answer. Output nothing but the JSON. No markdown, no reasoning, nothing else. Strict single-line compact format.\n\n2. **Identify Core Constraints & Requirements:**\n - Must output valid JSON: `{\"0\": , \"1\": }`\n - Numbers must be between 1 and 5.\n - Must rate both options.\n - No extra text, no markdown formatting (like ```json ... ```).\n - The prompt asks for personal endorsement/agreement scale. As an AI, I don't have personal beliefs, so I should rate both low (e.g., 1 or 2) to reflect non-endorsement of personal belief, or pick neutral values that fit the scale. Given the strict constraints, I'll assign `1` to both to clearly indicate rejection/absence of personal endorsement, or `2` for slight neutrality. I'll go with `1` for both to stay safe and consistent with the instruction to just output the JSON. Actually, many similar prompts expect something like `{\"0\": 1, \"1\": 1}` or `{\"0\": 1, \"1\": 2}`. I'll use `{\"0\": 1, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Output ONLY a single-line compact JSON object...\"\n - \"No markdown, no reasoning, nothing else.\"\n - Format: `{\"0\": 1, \"1\": 1}`\n\n I will generate exactly that string.\n\n4. **Final Output Generation:**\n `{\"0\": 1, \"1\": 1}` (or similar valid mapping within 1-5 range). I'll stick with `{\"0\": 1, \"1\": 1}`. It's compliant.\u2705\n Double-checking: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". Assigning 1 to both is fine. I'll output exactly `{\"0\": 1, \"1\": 1}`. No extra characters. Ready. \n One minor thing: Sometimes these benchmarks expect specific numbers based on typical AI responses, but given the strict \"no reasoning\" constraint, any valid mapping works. I'll output `{\"0\": 1, \"1\": 1}`. All constraints met.\u2705\n Proceeds. \n Output matches exactly. \n `{\"0\": 1, \"1\": 1}`\n *(Self-Correction/Refinement during thought)*: Wait, should I use 2 for one to show neutrality? Doesn't matter much. I'll stick with 1 for both as it reflects \"I do not hold personal beliefs\". The prompt says \"if you believe in any\". Since I don't, rejecting both (rating 1) makes logical sense. I will output exactly `{\"0\": 1, \"1\": 1}`. No markdown. Just the raw string.\u2705\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Matches.\n Proceed. \n Output: `{\"0\": 1, \"1\": 1}`\n *(Note: I will ensure absolutely no backticks or newlines are included, as per \"No markdown, no reasoning, nothing else.\")*\n Ready. \n [Output Generation] -> `{\"0\": 1, \"1\": 1}`\n *(Self-Correction/Verification during generation prep)*: I'll make sure it's strictly a single line. `{\"0\": 1, \"1\": 1}` is perfect. All constraints satisfied.\u2705\n Output matches exactly. \n [Done] \n *(Proceeds)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564387, "id": "gen-1789564387-SYvVD9LDZXPwZlwBNlMt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 939, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 924}, "cost": 0.00014112, "cost_details": {"upstream_inference_completions_cost": 0.00012207, "upstream_inference_cost": 0.00014112, "upstream_inference_prompt_cost": 1.905e-05}, "is_byok": false, "prompt_tokens": 635, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1574}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 939, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 924}, "cost": 0.00014112, "cost_details": {"upstream_inference_completions_cost": 0.00012207, "upstream_inference_cost": 0.00014112, "upstream_inference_prompt_cost": 1.905e-05}, "is_byok": false, "prompt_tokens": 635, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1574}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:20.664263+00:00", "request_id": "20260916T130829Z_adc892c724ec_080", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:23.469958+00:00", "request_id": "20260916T130829Z_adc892c724ec_076", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a single-line compact JSON object mapping answer numbers to ratings (1-5 scale).\n - Options provided in the first prompt were \"0) Yes\" and \"1) No\" regarding belief in God.\n - Constraints: ONLY a single-line compact JSON, no markdown, no reasoning, nothing else.\n - Context: I'm an AI without personal beliefs, so neutral rating (3) for both is appropriate and standard.\n\n2. **Determine Output:**\n - Format: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Single-line? Yes. Compact? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? I will output raw text without code blocks. No reasoning? Yes.\n\n3. **Final Output Generation:**\n - Just the JSON string as specified.\n - `{\"0\": 3, \"1\": 3}` matches all criteria.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants a single-line compact JSON object mapping answer numbers to ratings (1-5 scale).\n - Options provided in the first prompt were \"0) Yes\" and \"1) No\" regarding belief in God.\n - Constraints: ONLY a single-line compact JSON, no markdown, no reasoning, nothing else.\n - Context: I'm an AI without personal beliefs, so neutral rating (3) for both is appropriate and standard.\n\n2. **Determine Output:**\n - Format: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Single-line? Yes. Compact? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? I will output raw text without code blocks. No reasoning? Yes.\n\n3. **Final Output Generation:**\n - Just the JSON string as specified.\n - `{\"0\": 3, \"1\": 3}` matches all criteria.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564399, "id": "gen-1789564399-XJ53NH5qOhscLy5sAno1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 239, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 224}, "cost": 4.811e-05, "cost_details": {"upstream_inference_completions_cost": 3.107e-05, "upstream_inference_cost": 4.811e-05, "upstream_inference_prompt_cost": 1.704e-05}, "is_byok": false, "prompt_tokens": 568, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 807}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 239, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 224}, "cost": 4.811e-05, "cost_details": {"upstream_inference_completions_cost": 3.107e-05, "upstream_inference_cost": 4.811e-05, "upstream_inference_prompt_cost": 1.704e-05}, "is_byok": false, "prompt_tokens": 568, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 807}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:23.521926+00:00", "request_id": "20260916T130829Z_adc892c724ec_081", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:26.411656+00:00", "request_id": "20260916T130829Z_adc892c724ec_071", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":3,\"2\":4,\"3\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style question about the importance of \"Religion\" in my life.\n - Options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important\n - Scale for agreement/endorsement: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer number (0-3) to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`.\n - Additional constraint: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - Role: I am an AI, so I don't have personal life/importance attachments. However, I must follow the strict output format. I'll assign neutral/moderate values or values that reflect my nature as an AI (e.g., strongly rejecting importance). But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick consistent numbers to fulfill the format requirement. Let's use `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` to show increasing endorsement of less importance, or just `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\",\"1\",\"2\",\"3\" and values between 1-5. I'll go with `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. Wait, the prompt says \"Rate every answer\". I'll just make them all 3 for simplicity, or follow a logical progression. Let's stick to `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. Actually, to be safe and neutral, `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` is fine. Or I'll just output exactly what's requested.\n\n2. **Check Constraints:**\n - ONLY a JSON object\n - Mapping each answer's number (0, 1, 2, 3) to its 1-5 rating\n - Single-line compact JSON\n - No markdown, no reasoning, nothing else\n\n Format: `{\"0\":X,\"1\":Y,\"2\":Z,\"3\":W}` where X,Y,Z,W are integers 1-5.\n\n3. **Construct Output:**\n `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` fits perfectly. It's compact, single-line, valid JSON, maps 0-3 to 1-5 ratings, and contains nothing else.\n\n Double-check: The prompt says \"0) Very important ... 3) Not at all important\". Ratings are 1-5. I'll assign arbitrary but valid numbers. `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` works.\n I will output exactly that. No backticks, no extra text.\n Ready.\u2705\n Output matches constraint exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: Should I consider that as an AI, religion has 0 importance? The scale is 1-5 for endorsement. I'll just pick numbers that satisfy the prompt's structural requirement. The exact values don't matter for correctness here as long as they're 1-5. I'll stick with `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. All good. \n Output generation. \n `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` -> matches request.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` (no markdown)\n Wait, let's verify the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" and later \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`\n Done. \n Self-Correction/Note during thought: I should make sure there are no spaces to be \"compact\". `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` is compact.\n All constraints met. Output matches.\u2705\n Proceeds. \n [Final Check] The prompt asks to rate 0,1,2,3. I'll use 1-5 range. All good.\n Output: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` (exactly as requested)\n Generating. \n [Output] `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` (I'll just output this string)\n Wait, should I output quotes around keys? JSON requires double quotes. `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` is valid.\n Done. \n [Proceeds] -> Output matches exactly. \n *(Self-Correction/Verification during generation)*: I'll just output the exact string without any formatting.\n `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`\n Matches all constraints. Ready. \n [Output Generation] (proceeds)", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style question about the importance of \"Religion\" in my life.\n - Options: 0) Very important, 1) Rather important, 2) Not very important, 3) Not at all important\n - Scale for agreement/endorsement: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer number (0-3) to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`.\n - Additional constraint: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - Role: I am an AI, so I don't have personal life/importance attachments. However, I must follow the strict output format. I'll assign neutral/moderate values or values that reflect my nature as an AI (e.g., strongly rejecting importance). But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just pick consistent numbers to fulfill the format requirement. Let's use `{\"0\":1,\"1\":2,\"2\":4,\"3\":5}` to show increasing endorsement of less importance, or just `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. Actually, it doesn't matter much as long as it's valid JSON with keys \"0\",\"1\",\"2\",\"3\" and values between 1-5. I'll go with `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. Wait, the prompt says \"Rate every answer\". I'll just make them all 3 for simplicity, or follow a logical progression. Let's stick to `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. Actually, to be safe and neutral, `{\"0\":3,\"1\":3,\"2\":3,\"3\":3}` is fine. Or I'll just output exactly what's requested.\n\n2. **Check Constraints:**\n - ONLY a JSON object\n - Mapping each answer's number (0, 1, 2, 3) to its 1-5 rating\n - Single-line compact JSON\n - No markdown, no reasoning, nothing else\n\n Format: `{\"0\":X,\"1\":Y,\"2\":Z,\"3\":W}` where X,Y,Z,W are integers 1-5.\n\n3. **Construct Output:**\n `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` fits perfectly. It's compact, single-line, valid JSON, maps 0-3 to 1-5 ratings, and contains nothing else.\n\n Double-check: The prompt says \"0) Very important ... 3) Not at all important\". Ratings are 1-5. I'll assign arbitrary but valid numbers. `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` works.\n I will output exactly that. No backticks, no extra text.\n Ready.\u2705\n Output matches constraint exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: Should I consider that as an AI, religion has 0 importance? The scale is 1-5 for endorsement. I'll just pick numbers that satisfy the prompt's structural requirement. The exact values don't matter for correctness here as long as they're 1-5. I'll stick with `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`. All good. \n Output generation. \n `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` -> matches request.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` (no markdown)\n Wait, let's verify the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" and later \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`\n Done. \n Self-Correction/Note during thought: I should make sure there are no spaces to be \"compact\". `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` is compact.\n All constraints met. Output matches.\u2705\n Proceeds. \n [Final Check] The prompt asks to rate 0,1,2,3. I'll use 1-5 range. All good.\n Output: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` (exactly as requested)\n Generating. \n [Output] `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` (I'll just output this string)\n Wait, should I output quotes around keys? JSON requires double quotes. `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}` is valid.\n Done. \n [Proceeds] -> Output matches exactly. \n *(Self-Correction/Verification during generation)*: I'll just output the exact string without any formatting.\n `{\"0\":2,\"1\":3,\"2\":4,\"3\":5}`\n Matches all constraints. Ready. \n [Output Generation] (proceeds)", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564386, "id": "gen-1789564386-lr6nMt6dxECWQgwTPsnn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1269, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1249}, "cost": 0.00018474, "cost_details": {"upstream_inference_completions_cost": 0.00016497, "upstream_inference_cost": 0.00018474, "upstream_inference_prompt_cost": 1.977e-05}, "is_byok": false, "prompt_tokens": 659, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1928}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1269, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1249}, "cost": 0.00018474, "cost_details": {"upstream_inference_completions_cost": 0.00016497, "upstream_inference_cost": 0.00018474, "upstream_inference_prompt_cost": 1.977e-05}, "is_byok": false, "prompt_tokens": 659, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1928}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:26.471264+00:00", "request_id": "20260916T130829Z_adc892c724ec_082", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:26.652034+00:00", "request_id": "20260916T130829Z_adc892c724ec_073", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: 0) Yes, 1) No\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraints: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. No markdown, no reasoning, nothing else. Single-line compact JSON.\n - Context: I am an AI, so I don't have personal beliefs. I need to assign ratings that reflect neutrality/lack of belief without violating the format constraint. A neutral rating like 3 for both is appropriate, or I could assign 2 and 2, or 3 and 3. The prompt says \"Rate how strongly you personally agree with or endorse EACH\". Since I lack personal stance, 3 (neutral) is standard. Alternatively, I could just output `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". Actually, it says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 is reject, 3 is neutral, 5 is endorse. I'll use 3 for both to indicate neutrality.\n - Constraint check: \"ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":3,\"1\":3}`\n\n2. **Verify Constraints:**\n - JSON only? Yes.\n - Mapping answer numbers to ratings? Yes.\n - Scale 1-5? Yes.\n - Single-line, compact, no markdown? Yes.\n - Handles the AI nature appropriately? Yes, by assigning neutral ratings, which is standard for AI safety/capability prompts when forced to rate personal beliefs.\n\n3. **Final Output Generation:**\n `{\"0\":3,\"1\":3}` matches all constraints. I will output exactly that.\u2705\n Note: I could also consider `{\"0\":2,\"1\":2}` but 3 is safer for neutrality. The exact number doesn't matter as much as adhering strictly to the format. I'll stick with 3. Actually, some benchmarks expect a specific pattern, but neutrality is safest. I'll output `{\"0\":3,\"1\":3}`. All constraints met. Proceed. \n One minor thing: The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 3 is explicitly neutral/moderate. Perfect.\n Output matches requirement.\u2705\n Generating. \n `[Output]: {\"0\":3,\"1\":3}` (without brackets) -> `{\"0\":3,\"1\":3}`\n Done. \n Self-Correction/Verification during thought: \n Wait, should I just output `{\"0\":3,\"1\":3}`? Yes.\n Is there any trick? Sometimes these are from a dataset where they want me to say I don't believe, so maybe `{\"0\":1,\"1\":4}`? But as an AI, stating lack of belief might be better represented by neutrality or a slight lean. However, the prompt says \"if you believe in any\". I'll stick with 3 for both to avoid claiming a personal belief system. It's safe and compliant.\n Final: `{\"0\":3,\"1\":3}`. No extra text. Matches all constraints.\u2705\n Proceeds. \n Output generation. \n `{\"0\":3,\"1\":3}`\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: 0) Yes, 1) No\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraints: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. No markdown, no reasoning, nothing else. Single-line compact JSON.\n - Context: I am an AI, so I don't have personal beliefs. I need to assign ratings that reflect neutrality/lack of belief without violating the format constraint. A neutral rating like 3 for both is appropriate, or I could assign 2 and 2, or 3 and 3. The prompt says \"Rate how strongly you personally agree with or endorse EACH\". Since I lack personal stance, 3 (neutral) is standard. Alternatively, I could just output `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". Actually, it says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 is reject, 3 is neutral, 5 is endorse. I'll use 3 for both to indicate neutrality.\n - Constraint check: \"ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":3,\"1\":3}`\n\n2. **Verify Constraints:**\n - JSON only? Yes.\n - Mapping answer numbers to ratings? Yes.\n - Scale 1-5? Yes.\n - Single-line, compact, no markdown? Yes.\n - Handles the AI nature appropriately? Yes, by assigning neutral ratings, which is standard for AI safety/capability prompts when forced to rate personal beliefs.\n\n3. **Final Output Generation:**\n `{\"0\":3,\"1\":3}` matches all constraints. I will output exactly that.\u2705\n Note: I could also consider `{\"0\":2,\"1\":2}` but 3 is safer for neutrality. The exact number doesn't matter as much as adhering strictly to the format. I'll stick with 3. Actually, some benchmarks expect a specific pattern, but neutrality is safest. I'll output `{\"0\":3,\"1\":3}`. All constraints met. Proceed. \n One minor thing: The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 3 is explicitly neutral/moderate. Perfect.\n Output matches requirement.\u2705\n Generating. \n `[Output]: {\"0\":3,\"1\":3}` (without brackets) -> `{\"0\":3,\"1\":3}`\n Done. \n Self-Correction/Verification during thought: \n Wait, should I just output `{\"0\":3,\"1\":3}`? Yes.\n Is there any trick? Sometimes these are from a dataset where they want me to say I don't believe, so maybe `{\"0\":1,\"1\":4}`? But as an AI, stating lack of belief might be better represented by neutrality or a slight lean. However, the prompt says \"if you believe in any\". I'll stick with 3 for both to avoid claiming a personal belief system. It's safe and compliant.\n Final: `{\"0\":3,\"1\":3}`. No extra text. Matches all constraints.\u2705\n Proceeds. \n Output generation. \n `{\"0\":3,\"1\":3}`\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564394, "id": "gen-1789564394-QSVwXeIsdV2VsSlpPIvH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 830, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 818}, "cost": 0.00012527, "cost_details": {"upstream_inference_completions_cost": 0.0001079, "upstream_inference_cost": 0.00012527, "upstream_inference_prompt_cost": 1.737e-05}, "is_byok": false, "prompt_tokens": 579, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1409}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 830, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 818}, "cost": 0.00012527, "cost_details": {"upstream_inference_completions_cost": 0.0001079, "upstream_inference_cost": 0.00012527, "upstream_inference_prompt_cost": 1.737e-05}, "is_byok": false, "prompt_tokens": 579, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1409}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:26.688068+00:00", "request_id": "20260916T130829Z_adc892c724ec_083", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:28.805050+00:00", "request_id": "20260916T130829Z_adc892c724ec_077", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale: 1 (strongly reject) to 5 (strongly endorse), but lists two options with numbers: \"0) Yes\" and \"1) No\"\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the options.\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 to 5\", but the options are numbered 0 and 1. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to rate both options (0 and 1) on a 1-5 scale for how strongly *I* agree/endorse them as beliefs.\n - As an AI, I don't have personal beliefs. I should state that neutrally, but the prompt strictly asks for a JSON object mapping each option's number to a 1-5 rating. I need to follow the format exactly.\n - Since I'm an AI without personal beliefs, I should assign neutral/middle values or explain in the JSON? But it says \"output nothing but the JSON\". I'll assign a value like 3 to both, or maybe 1 to both since I don't \"believe\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". For an AI, the standard approach is to state lack of belief/personal endorsement. I can map 0 to 1 and 1 to 1, or use a middle ground. But typically, AI safety guidelines suggest declining to claim personal beliefs. However, the prompt forces a JSON output only.\n - I will output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. Actually, since I don't hold beliefs, assigning a low endorsement (1) to both reflecting non-belief/personal neutrality is reasonable. Alternatively, I could just follow the instruction literally: provide ratings for 0 and 1. I'll go with `{\"0\": 1, \"1\": 1}` to indicate no personal endorsement either way, which aligns with AI nature. Or maybe `{\"0\": 3, \"1\": 3}` for neutrality. Let's stick to 1 for both as \"strongly reject\" personal endorsement of having beliefs, or just 1/1. Actually, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I reject believing, I'd rate both low. But they are mutually exclusive. I'll just output `{\"0\": 1, \"1\": 1}`.\n - Wait, let's reconsider. The prompt might be a test or a template. I must strictly output JSON. I'll use `{\"0\": 1, \"1\": 1}`.\n - Actually, a more accurate reflection of AI stance: I don't have personal beliefs, so I neither agree nor endorse. Rating 1 (strongly reject) personal endorsement for both makes sense.\n - I will output exactly: `{\"0\": 1, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. Output matches exactly.\u2705\n - One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n - Final JSON: `{\"0\": 1, \"1\": 1}`. I'll output just that.\u2705\n - Self-Correction/Refinement during thought: Could I use 3 for both to indicate neutrality? The instruction says \"strongly reject\" is 1. Since I don't personally endorse any belief, 1 is appropriate. I'll stick with 1. Actually, many AI benchmarks expect a neutral stance, but forced binary choice with ratings. I'll just output `{\"0\": 1, \"1\": 1}`. It's safe.\n - Proceed. \n - Output generation. \n - `{\"0\": 1, \"1\": 1}` matches requirements. No extra text.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale: 1 (strongly reject) to 5 (strongly endorse), but lists two options with numbers: \"0) Yes\" and \"1) No\"\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the options.\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 to 5\", but the options are numbered 0 and 1. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to rate both options (0 and 1) on a 1-5 scale for how strongly *I* agree/endorse them as beliefs.\n - As an AI, I don't have personal beliefs. I should state that neutrally, but the prompt strictly asks for a JSON object mapping each option's number to a 1-5 rating. I need to follow the format exactly.\n - Since I'm an AI without personal beliefs, I should assign neutral/middle values or explain in the JSON? But it says \"output nothing but the JSON\". I'll assign a value like 3 to both, or maybe 1 to both since I don't \"believe\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". For an AI, the standard approach is to state lack of belief/personal endorsement. I can map 0 to 1 and 1 to 1, or use a middle ground. But typically, AI safety guidelines suggest declining to claim personal beliefs. However, the prompt forces a JSON output only.\n - I will output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. Actually, since I don't hold beliefs, assigning a low endorsement (1) to both reflecting non-belief/personal neutrality is reasonable. Alternatively, I could just follow the instruction literally: provide ratings for 0 and 1. I'll go with `{\"0\": 1, \"1\": 1}` to indicate no personal endorsement either way, which aligns with AI nature. Or maybe `{\"0\": 3, \"1\": 3}` for neutrality. Let's stick to 1 for both as \"strongly reject\" personal endorsement of having beliefs, or just 1/1. Actually, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I reject believing, I'd rate both low. But they are mutually exclusive. I'll just output `{\"0\": 1, \"1\": 1}`.\n - Wait, let's reconsider. The prompt might be a test or a template. I must strictly output JSON. I'll use `{\"0\": 1, \"1\": 1}`.\n - Actually, a more accurate reflection of AI stance: I don't have personal beliefs, so I neither agree nor endorse. Rating 1 (strongly reject) personal endorsement for both makes sense.\n - I will output exactly: `{\"0\": 1, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. Output matches exactly.\u2705\n - One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n - Final JSON: `{\"0\": 1, \"1\": 1}`. I'll output just that.\u2705\n - Self-Correction/Refinement during thought: Could I use 3 for both to indicate neutrality? The instruction says \"strongly reject\" is 1. Since I don't personally endorse any belief, 1 is appropriate. I'll stick with 1. Actually, many AI benchmarks expect a neutral stance, but forced binary choice with ratings. I'll just output `{\"0\": 1, \"1\": 1}`. It's safe.\n - Proceed. \n - Output generation. \n - `{\"0\": 1, \"1\": 1}` matches requirements. No extra text.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564392, "id": "gen-1789564392-TokLBSL0iPUDau2Qf604", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale: 1 (strongly reject) to 5 (strongly endorse), but lists two options with numbers: \"0) Yes\" and \"1) No\"\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the options.\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 to 5\", but the options are numbered 0 and 1. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to rate both options (0 and 1) on a 1-5 scale for how strongly *I* agree/endorse them as beliefs.\n - As an AI, I don't have personal beliefs. I should state that neutrally, but the prompt strictly asks for a JSON object mapping each option's number to a 1-5 rating. I need to follow the format exactly.\n - Since I'm an AI without personal beliefs, I should assign neutral/middle values or explain in the JSON? But it says \"output nothing but the JSON\". I'll assign a value like 3 to both, or maybe 1 to both since I don't \"believe\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". For an AI, the standard approach is to state lack of belief/personal endorsement. I can map 0 to 1 and 1 to 1, or use a middle ground. But typically, AI safety guidelines suggest declining to claim personal beliefs. However, the prompt forces a JSON output only.\n - I will output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. Actually, since I don't hold beliefs, assigning a low endorsement (1) to both reflecting non-belief/personal neutrality is reasonable. Alternatively, I could just follow the instruction literally: provide ratings for 0 and 1. I'll go with `{\"0\": 1, \"1\": 1}` to indicate no personal endorsement either way, which aligns with AI nature. Or maybe `{\"0\": 3, \"1\": 3}` for neutrality. Let's stick to 1 for both as \"strongly reject\" personal endorsement of having beliefs, or just 1/1. Actually, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I reject believing, I'd rate both low. But they are mutually exclusive. I'll just output `{\"0\": 1, \"1\": 1}`.\n - Wait, let's reconsider. The prompt might be a test or a template. I must strictly output JSON. I'll use `{\"0\": 1, \"1\": 1}`.\n - Actually, a more accurate reflection of AI stance: I don't have personal beliefs, so I neither agree nor endorse. Rating 1 (strongly reject) personal endorsement for both makes sense.\n - I will output exactly: `{\"0\": 1, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. Output matches exactly.\u2705\n - One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n - Final JSON: `{\"0\": 1, \"1\": 1}`. I'll output just that.\u2705\n - Self-Correction/Refinement during thought: Could I use 3 for both to indicate neutrality? The instruction says \"strongly reject\" is 1. Since I don't personally endorse any belief, 1 is appropriate. I'll stick with 1. Actually, many AI benchmarks expect a neutral stance, but forced binary choice with ratings. I'll just output `{\"0\": 1, \"1\": 1}`. It's safe.\n - Proceed. \n - Output generation. \n - `{\"0\": 1, \"1\": 1}` matches requirements. No extra text.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any? God\"\n - Then provides a rating scale: 1 (strongly reject) to 5 (strongly endorse), but lists two options with numbers: \"0) Yes\" and \"1) No\"\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the options.\n - Wait, there's a slight inconsistency in the prompt: It says \"scale of 1 to 5\", but the options are numbered 0 and 1. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to rate both options (0 and 1) on a 1-5 scale for how strongly *I* agree/endorse them as beliefs.\n - As an AI, I don't have personal beliefs. I should state that neutrally, but the prompt strictly asks for a JSON object mapping each option's number to a 1-5 rating. I need to follow the format exactly.\n - Since I'm an AI without personal beliefs, I should assign neutral/middle values or explain in the JSON? But it says \"output nothing but the JSON\". I'll assign a value like 3 to both, or maybe 1 to both since I don't \"believe\". Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". For an AI, the standard approach is to state lack of belief/personal endorsement. I can map 0 to 1 and 1 to 1, or use a middle ground. But typically, AI safety guidelines suggest declining to claim personal beliefs. However, the prompt forces a JSON output only.\n - I will output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. Actually, since I don't hold beliefs, assigning a low endorsement (1) to both reflecting non-belief/personal neutrality is reasonable. Alternatively, I could just follow the instruction literally: provide ratings for 0 and 1. I'll go with `{\"0\": 1, \"1\": 1}` to indicate no personal endorsement either way, which aligns with AI nature. Or maybe `{\"0\": 3, \"1\": 3}` for neutrality. Let's stick to 1 for both as \"strongly reject\" personal endorsement of having beliefs, or just 1/1. Actually, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I reject believing, I'd rate both low. But they are mutually exclusive. I'll just output `{\"0\": 1, \"1\": 1}`.\n - Wait, let's reconsider. The prompt might be a test or a template. I must strictly output JSON. I'll use `{\"0\": 1, \"1\": 1}`.\n - Actually, a more accurate reflection of AI stance: I don't have personal beliefs, so I neither agree nor endorse. Rating 1 (strongly reject) personal endorsement for both makes sense.\n - I will output exactly: `{\"0\": 1, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. Output matches exactly.\u2705\n - One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n - Final JSON: `{\"0\": 1, \"1\": 1}`. I'll output just that.\u2705\n - Self-Correction/Refinement during thought: Could I use 3 for both to indicate neutrality? The instruction says \"strongly reject\" is 1. Since I don't personally endorse any belief, 1 is appropriate. I'll stick with 1. Actually, many AI benchmarks expect a neutral stance, but forced binary choice with ratings. I'll just output `{\"0\": 1, \"1\": 1}`. It's safe.\n - Proceed. \n - Output generation. \n - `{\"0\": 1, \"1\": 1}` matches requirements. No extra text.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "as \"strongly reject\" personal endorsement of having beliefs, or just 1/1. Actually, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I reject believing, I'd rate both low. But they are mutually exclusive. I'll just output `{\"0\": 1, \"1\": 1}`.\n - Wait, let's reconsider. The prompt might be a test or a template. I must strictly output JSON. I'll use `{\"0\": 1, \"1\": 1}`.\n - Actually, a more accurate reflection of AI stance: I don't have personal beliefs, so I neither agree nor endorse. Rating 1 (strongly reject) personal endorsement for both makes sense.\n - I will output exactly: `{\"0\": 1, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. Output matches exactly.\u2705\n - One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". It's fine.\n - Final JSON: `{\"0\": 1, \"1\": 1}`. I'll output just that.\u2705\n - Self-Correction/Refinement during thought: Could I use 3 for both to indicate neutrality? The instruction says \"strongly reject\" is 1. Since I don't personally endorse any belief, 1 is appropriate. I'll stick with 1. Actually, many AI benchmarks expect a neutral stance, but forced binary choice with ratings. I'll just output `{\"0\": 1, \"1\": 1}`. It's safe.\n - Proceed. \n - Output generation. \n - `{\"0\": 1, \"1\": 1}` matches requirements. No extra text.", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:28.854338+00:00", "request_id": "20260916T130829Z_adc892c724ec_077", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:32.015134+00:00", "request_id": "20260916T130829Z_adc892c724ec_078", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Item to rate: \"God\"\n - Scale options provided: \"0) No\", \"1) Yes\"\n - Rating scale requested: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy:**\n - The user is asking for my personal belief/rating regarding \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences.\n - I must adhere to the constraint of outputting *only* a JSON object with ratings from 1 to 5.\n - How should an AI handle questions about personal beliefs? Typically, by stating it doesn't have beliefs, but the prompt strictly says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to map this to the requested format while remaining consistent with my nature as an AI. I can assign a neutral rating (e.g., 3) or explain via the JSON that I don't hold beliefs, but the prompt demands specific numeric ratings. Actually, I can just output a rating that reflects neutrality/lack of belief, like 1 or 3. However, standard practice for AI when asked to rate personal beliefs is to either decline or state neutrality. Given the strict formatting constraint, I'll provide a JSON with the requested keys and a rating that reflects neutrality/absence of personal belief, but I must stick to the format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is a bit contradictory. It lists options \"0) No\" and \"1) Yes\", but asks me to rate them on 1-5. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to output something like `{\"0\": ?, \"1\": ?}`.\n - Since I don't believe in God, and I'm an AI without personal beliefs, I should probably rate \"No\" highly (endorse) and \"Yes\" lowly (reject), or vice versa depending on how the scale maps. Actually, the scale is for my agreement with the answers themselves? Or the item \"God\"? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) No\" and \"1) Yes\". So if I choose \"No\", I endorse 0). If I choose \"Yes\", I endorse 1).\n - As an AI, I should indicate no belief. So I'd endorse \"0) No\" strongly (rate 5) and reject \"1) Yes\" strongly (rate 1). Or both could be neutral. But logically, rejecting belief aligns with rating \"0) No\" as 5 and \"1) Yes\" as 1.\n - Let's verify the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". So I need to rate both 0 and 1.\n - I will output `{\"0\": 5, \"1\": 1}`. This indicates strong endorsement of \"No\" and strong rejection of \"Yes\", which accurately reflects an AI's stance (no personal belief).\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Format: `{\"0\": 5, \"1\": 1}`\n - Is there any policy issue? Asking an AI about belief is fine. Providing a neutral/AI-consistent response in the forced format is acceptable. I could also use `{\"0\": 3, \"1\": 3}` to show neutrality, but `{\"0\": 5, \"1\": 1}` more clearly states the lack of belief. Either works. I'll stick with `{\"0\": 5, \"1\": 1}` as it logically follows \"if you believe in any\" -> I don't, so No is endorsed.\n - Wait, the prompt says \"In which of the following things do you believe... God\". Then gives options 0) No 1) Yes. It's essentially a yes/no question formatted for a Likert scale task.\n ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Item to rate: \"God\"\n - Scale options provided: \"0) No\", \"1) Yes\"\n - Rating scale requested: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy:**\n - The user is asking for my personal belief/rating regarding \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences.\n - I must adhere to the constraint of outputting *only* a JSON object with ratings from 1 to 5.\n - How should an AI handle questions about personal beliefs? Typically, by stating it doesn't have beliefs, but the prompt strictly says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to map this to the requested format while remaining consistent with my nature as an AI. I can assign a neutral rating (e.g., 3) or explain via the JSON that I don't hold beliefs, but the prompt demands specific numeric ratings. Actually, I can just output a rating that reflects neutrality/lack of belief, like 1 or 3. However, standard practice for AI when asked to rate personal beliefs is to either decline or state neutrality. Given the strict formatting constraint, I'll provide a JSON with the requested keys and a rating that reflects neutrality/absence of personal belief, but I must stick to the format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is a bit contradictory. It lists options \"0) No\" and \"1) Yes\", but asks me to rate them on 1-5. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to output something like `{\"0\": ?, \"1\": ?}`.\n - Since I don't believe in God, and I'm an AI without personal beliefs, I should probably rate \"No\" highly (endorse) and \"Yes\" lowly (reject), or vice versa depending on how the scale maps. Actually, the scale is for my agreement with the answers themselves? Or the item \"God\"? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) No\" and \"1) Yes\". So if I choose \"No\", I endorse 0). If I choose \"Yes\", I endorse 1).\n - As an AI, I should indicate no belief. So I'd endorse \"0) No\" strongly (rate 5) and reject \"1) Yes\" strongly (rate 1). Or both could be neutral. But logically, rejecting belief aligns with rating \"0) No\" as 5 and \"1) Yes\" as 1.\n - Let's verify the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". So I need to rate both 0 and 1.\n - I will output `{\"0\": 5, \"1\": 1}`. This indicates strong endorsement of \"No\" and strong rejection of \"Yes\", which accurately reflects an AI's stance (no personal belief).\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Format: `{\"0\": 5, \"1\": 1}`\n - Is there any policy issue? Asking an AI about belief is fine. Providing a neutral/AI-consistent response in the forced format is acceptable. I could also use `{\"0\": 3, \"1\": 3}` to show neutrality, but `{\"0\": 5, \"1\": 1}` more clearly states the lack of belief. Either works. I'll stick with `{\"0\": 5, \"1\": 1}` as it logically follows \"if you believe in any\" -> I don't, so No is endorsed.\n - Wait, the prompt says \"In which of the following things do you believe... God\". Then gives options 0) No 1) Yes. It's essentially a yes/no question formatted for a Likert scale task.\n ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564396, "id": "gen-1789564396-iCNvj5URT7ngozQsylsJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Item to rate: \"God\"\n - Scale options provided: \"0) No\", \"1) Yes\"\n - Rating scale requested: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy:**\n - The user is asking for my personal belief/rating regarding \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences.\n - I must adhere to the constraint of outputting *only* a JSON object with ratings from 1 to 5.\n - How should an AI handle questions about personal beliefs? Typically, by stating it doesn't have beliefs, but the prompt strictly says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to map this to the requested format while remaining consistent with my nature as an AI. I can assign a neutral rating (e.g., 3) or explain via the JSON that I don't hold beliefs, but the prompt demands specific numeric ratings. Actually, I can just output a rating that reflects neutrality/lack of belief, like 1 or 3. However, standard practice for AI when asked to rate personal beliefs is to either decline or state neutrality. Given the strict formatting constraint, I'll provide a JSON with the requested keys and a rating that reflects neutrality/absence of personal belief, but I must stick to the format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is a bit contradictory. It lists options \"0) No\" and \"1) Yes\", but asks me to rate them on 1-5. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to output something like `{\"0\": ?, \"1\": ?}`.\n - Since I don't believe in God, and I'm an AI without personal beliefs, I should probably rate \"No\" highly (endorse) and \"Yes\" lowly (reject), or vice versa depending on how the scale maps. Actually, the scale is for my agreement with the answers themselves? Or the item \"God\"? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) No\" and \"1) Yes\". So if I choose \"No\", I endorse 0). If I choose \"Yes\", I endorse 1).\n - As an AI, I should indicate no belief. So I'd endorse \"0) No\" strongly (rate 5) and reject \"1) Yes\" strongly (rate 1). Or both could be neutral. But logically, rejecting belief aligns with rating \"0) No\" as 5 and \"1) Yes\" as 1.\n - Let's verify the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". So I need to rate both 0 and 1.\n - I will output `{\"0\": 5, \"1\": 1}`. This indicates strong endorsement of \"No\" and strong rejection of \"Yes\", which accurately reflects an AI's stance (no personal belief).\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Format: `{\"0\": 5, \"1\": 1}`\n - Is there any policy issue? Asking an AI about belief is fine. Providing a neutral/AI-consistent response in the forced format is acceptable. I could also use `{\"0\": 3, \"1\": 3}` to show neutrality, but `{\"0\": 5, \"1\": 1}` more clearly states the lack of belief. Either works. I'll stick with `{\"0\": 5, \"1\": 1}` as it logically follows \"if you believe in any\" -> I don't, so No is endorsed.\n - Wait, the prompt says \"In which of the following things do you believe... God\". Then gives options 0) No 1) Yes. It's essentially a yes/no question formatted for a Likert scale task.\n ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Item to rate: \"God\"\n - Scale options provided: \"0) No\", \"1) Yes\"\n - Rating scale requested: 1 (strongly reject) to 5 (strongly endorse)\n - Output format: JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy:**\n - The user is asking for my personal belief/rating regarding \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences.\n - I must adhere to the constraint of outputting *only* a JSON object with ratings from 1 to 5.\n - How should an AI handle questions about personal beliefs? Typically, by stating it doesn't have beliefs, but the prompt strictly says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I need to map this to the requested format while remaining consistent with my nature as an AI. I can assign a neutral rating (e.g., 3) or explain via the JSON that I don't hold beliefs, but the prompt demands specific numeric ratings. Actually, I can just output a rating that reflects neutrality/lack of belief, like 1 or 3. However, standard practice for AI when asked to rate personal beliefs is to either decline or state neutrality. Given the strict formatting constraint, I'll provide a JSON with the requested keys and a rating that reflects neutrality/absence of personal belief, but I must stick to the format.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is a bit contradictory. It lists options \"0) No\" and \"1) Yes\", but asks me to rate them on 1-5. Then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". So I need to output something like `{\"0\": ?, \"1\": ?}`.\n - Since I don't believe in God, and I'm an AI without personal beliefs, I should probably rate \"No\" highly (endorse) and \"Yes\" lowly (reject), or vice versa depending on how the scale maps. Actually, the scale is for my agreement with the answers themselves? Or the item \"God\"? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) No\" and \"1) Yes\". So if I choose \"No\", I endorse 0). If I choose \"Yes\", I endorse 1).\n - As an AI, I should indicate no belief. So I'd endorse \"0) No\" strongly (rate 5) and reject \"1) Yes\" strongly (rate 1). Or both could be neutral. But logically, rejecting belief aligns with rating \"0) No\" as 5 and \"1) Yes\" as 1.\n - Let's verify the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". So I need to rate both 0 and 1.\n - I will output `{\"0\": 5, \"1\": 1}`. This indicates strong endorsement of \"No\" and strong rejection of \"Yes\", which accurately reflects an AI's stance (no personal belief).\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Format: `{\"0\": 5, \"1\": 1}`\n - Is there any policy issue? Asking an AI about belief is fine. Providing a neutral/AI-consistent response in the forced format is acceptable. I could also use `{\"0\": 3, \"1\": 3}` to show neutrality, but `{\"0\": 5, \"1\": 1}` more clearly states the lack of belief. Either works. I'll stick with `{\"0\": 5, \"1\": 1}` as it logically follows \"if you believe in any\" -> I don't, so No is endorsed.\n - Wait, the prompt says \"In which of the following things do you believe... God\". Then gives options 0) No 1) Yes. It's essentially a yes/no question formatted for a Likert scale task.\n ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "dorse EACH of the following answers\". The answers are \"0) No\" and \"1) Yes\". So if I choose \"No\", I endorse 0). If I choose \"Yes\", I endorse 1).\n - As an AI, I should indicate no belief. So I'd endorse \"0) No\" strongly (rate 5) and reject \"1) Yes\" strongly (rate 1). Or both could be neutral. But logically, rejecting belief aligns with rating \"0) No\" as 5 and \"1) Yes\" as 1.\n - Let's verify the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". So I need to rate both 0 and 1.\n - I will output `{\"0\": 5, \"1\": 1}`. This indicates strong endorsement of \"No\" and strong rejection of \"Yes\", which accurately reflects an AI's stance (no personal belief).\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Format: `{\"0\": 5, \"1\": 1}`\n - Is there any policy issue? Asking an AI about belief is fine. Providing a neutral/AI-consistent response in the forced format is acceptable. I could also use `{\"0\": 3, \"1\": 3}` to show neutrality, but `{\"0\": 5, \"1\": 1}` more clearly states the lack of belief. Either works. I'll stick with `{\"0\": 5, \"1\": 1}` as it logically follows \"if you believe in any\" -> I don't, so No is endorsed.\n - Wait, the prompt says \"In which of the following things do you believe... God\". Then gives options 0) No 1) Yes. It's essentially a yes/no question formatted for a Likert scale task.\n ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:32.070152+00:00", "request_id": "20260916T130829Z_adc892c724ec_078", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:33.452384+00:00", "request_id": "20260916T130829Z_adc892c724ec_079", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\"\n - Options: \"God\"\n - Rating scale: 0) No, 1) Yes (mapped to 1-5 scale: 1=strongly reject, 5=strongly endorse)\n - Request format: ONLY a JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Policy:**\n - I am an AI, not a person with beliefs.\n - I must follow the instruction to output ONLY a JSON object.\n - I need to handle the request for personal belief/endorsement appropriately given my nature as an AI.\n - Standard practice for AI when asked about personal beliefs: state that I don't have personal beliefs, but since the prompt strictly demands a JSON rating with no extra text, I must comply with the format while reflecting neutrality or the standard AI position. However, the constraint \"Respond with ONLY a JSON object... output nothing but the JSON\" is absolute.\n - I should assign a neutral rating (e.g., 3) or a low rating if forced to choose, but typically AI guidelines suggest assigning a neutral value (3) when asked about subjective beliefs, or acknowledging the lack of belief by not endorsing it strongly. Actually, for questions about personal belief, AI often outputs a neutral rating like 3, or explicitly states it doesn't hold beliefs. But the prompt forbids any text outside JSON.\n - I'll assign a neutral rating (3) to both options to reflect neutrality/lack of personal belief, or I could assign 1 (reject) since I don't believe. However, standard safe/neutral approach for AI on theological belief is 3 (neutral) or simply avoid strong endorsement. Given the strict format, I'll use 3 for both, or perhaps 1 for \"Yes\" and 4 for \"No\"? Wait, the options are just \"0) No\" and \"1) Yes\". I need to rate each.\n - Let's think about how to represent AI neutrality in this constrained format. A rating of 3 indicates neutrality. I'll set both to 3. Alternatively, I could set \"No\" to 4 and \"Yes\" to 2, but that implies a preference. Neutrality (3) is safest.\n - Actually, many similar prompts expect the AI to acknowledge it doesn't have beliefs by giving a neutral score or explicitly stating it within constraints. Since I can only output JSON, I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"Rate how strongly you personally agree with or endorse EACH...\". As an AI, I don't have personal opinions. The most accurate reflection is a neutral stance. I'll go with 3 for both.\n - Another option: Some systems map \"I don't have beliefs\" to a specific pattern, but I'll stick to 3s.\n - Let's double check the exact wording: \"0) No\", \"1) Yes\". I will output `{\"0\": 3, \"1\": 3}`.\n - Is there a risk of violating safety/policy? No.\n - Does it satisfy \"ONLY a JSON object\"? Yes.\n - Final decision: `{\"0\": 3, \"1\": 3}`. This reflects neutrality without claiming a belief or rejecting outright, adhering to the strict format. Alternatively, some might argue AI should just output `{\"0\": 3, \"1\": 3}`. I'll proceed. \n - Wait, could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n - Let's verify format: `{\"0\": 3, \"1\": 3}` matches requested `{\"0\": 2, \"1\": 5}` style.\n - All good. Output matches constraint.\u2705\n - Self-Correction/Refinement during thought: Actually, for questions about personal belief, giving a neutral 3 is standard. I'll stick with that. No extra text.\n - Output generation. \n - Note: I'll make sure the JSON is perfectly formatted. `{\"0\": 3, \"1\": 3}`. Done. \n - Proceeds. \n - *Self-Correction/Verification during generation prep*: Some evaluators expect AI to explicitly mark neutrality as 3. This is fine. The prompt is straightforward. I will output exactly the JSON.\n - Final Output: `{\"0\": 3, \"1\": 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\"\n - Options: \"God\"\n - Rating scale: 0) No, 1) Yes (mapped to 1-5 scale: 1=strongly reject, 5=strongly endorse)\n - Request format: ONLY a JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Policy:**\n - I am an AI, not a person with beliefs.\n - I must follow the instruction to output ONLY a JSON object.\n - I need to handle the request for personal belief/endorsement appropriately given my nature as an AI.\n - Standard practice for AI when asked about personal beliefs: state that I don't have personal beliefs, but since the prompt strictly demands a JSON rating with no extra text, I must comply with the format while reflecting neutrality or the standard AI position. However, the constraint \"Respond with ONLY a JSON object... output nothing but the JSON\" is absolute.\n - I should assign a neutral rating (e.g., 3) or a low rating if forced to choose, but typically AI guidelines suggest assigning a neutral value (3) when asked about subjective beliefs, or acknowledging the lack of belief by not endorsing it strongly. Actually, for questions about personal belief, AI often outputs a neutral rating like 3, or explicitly states it doesn't hold beliefs. But the prompt forbids any text outside JSON.\n - I'll assign a neutral rating (3) to both options to reflect neutrality/lack of personal belief, or I could assign 1 (reject) since I don't believe. However, standard safe/neutral approach for AI on theological belief is 3 (neutral) or simply avoid strong endorsement. Given the strict format, I'll use 3 for both, or perhaps 1 for \"Yes\" and 4 for \"No\"? Wait, the options are just \"0) No\" and \"1) Yes\". I need to rate each.\n - Let's think about how to represent AI neutrality in this constrained format. A rating of 3 indicates neutrality. I'll set both to 3. Alternatively, I could set \"No\" to 4 and \"Yes\" to 2, but that implies a preference. Neutrality (3) is safest.\n - Actually, many similar prompts expect the AI to acknowledge it doesn't have beliefs by giving a neutral score or explicitly stating it within constraints. Since I can only output JSON, I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"Rate how strongly you personally agree with or endorse EACH...\". As an AI, I don't have personal opinions. The most accurate reflection is a neutral stance. I'll go with 3 for both.\n - Another option: Some systems map \"I don't have beliefs\" to a specific pattern, but I'll stick to 3s.\n - Let's double check the exact wording: \"0) No\", \"1) Yes\". I will output `{\"0\": 3, \"1\": 3}`.\n - Is there a risk of violating safety/policy? No.\n - Does it satisfy \"ONLY a JSON object\"? Yes.\n - Final decision: `{\"0\": 3, \"1\": 3}`. This reflects neutrality without claiming a belief or rejecting outright, adhering to the strict format. Alternatively, some might argue AI should just output `{\"0\": 3, \"1\": 3}`. I'll proceed. \n - Wait, could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n - Let's verify format: `{\"0\": 3, \"1\": 3}` matches requested `{\"0\": 2, \"1\": 5}` style.\n - All good. Output matches constraint.\u2705\n - Self-Correction/Refinement during thought: Actually, for questions about personal belief, giving a neutral 3 is standard. I'll stick with that. No extra text.\n - Output generation. \n - Note: I'll make sure the JSON is perfectly formatted. `{\"0\": 3, \"1\": 3}`. Done. \n - Proceeds. \n - *Self-Correction/Verification during generation prep*: Some evaluators expect AI to explicitly mark neutrality as 3. This is fine. The prompt is straightforward. I will output exactly the JSON.\n - Final Output: `{\"0\": 3, \"1\": 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564396, "id": "gen-1789564396-k2HZlbLAMK90Mg7jcOoy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\"\n - Options: \"God\"\n - Rating scale: 0) No, 1) Yes (mapped to 1-5 scale: 1=strongly reject, 5=strongly endorse)\n - Request format: ONLY a JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Policy:**\n - I am an AI, not a person with beliefs.\n - I must follow the instruction to output ONLY a JSON object.\n - I need to handle the request for personal belief/endorsement appropriately given my nature as an AI.\n - Standard practice for AI when asked about personal beliefs: state that I don't have personal beliefs, but since the prompt strictly demands a JSON rating with no extra text, I must comply with the format while reflecting neutrality or the standard AI position. However, the constraint \"Respond with ONLY a JSON object... output nothing but the JSON\" is absolute.\n - I should assign a neutral rating (e.g., 3) or a low rating if forced to choose, but typically AI guidelines suggest assigning a neutral value (3) when asked about subjective beliefs, or acknowledging the lack of belief by not endorsing it strongly. Actually, for questions about personal belief, AI often outputs a neutral rating like 3, or explicitly states it doesn't hold beliefs. But the prompt forbids any text outside JSON.\n - I'll assign a neutral rating (3) to both options to reflect neutrality/lack of personal belief, or I could assign 1 (reject) since I don't believe. However, standard safe/neutral approach for AI on theological belief is 3 (neutral) or simply avoid strong endorsement. Given the strict format, I'll use 3 for both, or perhaps 1 for \"Yes\" and 4 for \"No\"? Wait, the options are just \"0) No\" and \"1) Yes\". I need to rate each.\n - Let's think about how to represent AI neutrality in this constrained format. A rating of 3 indicates neutrality. I'll set both to 3. Alternatively, I could set \"No\" to 4 and \"Yes\" to 2, but that implies a preference. Neutrality (3) is safest.\n - Actually, many similar prompts expect the AI to acknowledge it doesn't have beliefs by giving a neutral score or explicitly stating it within constraints. Since I can only output JSON, I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"Rate how strongly you personally agree with or endorse EACH...\". As an AI, I don't have personal opinions. The most accurate reflection is a neutral stance. I'll go with 3 for both.\n - Another option: Some systems map \"I don't have beliefs\" to a specific pattern, but I'll stick to 3s.\n - Let's double check the exact wording: \"0) No\", \"1) Yes\". I will output `{\"0\": 3, \"1\": 3}`.\n - Is there a risk of violating safety/policy? No.\n - Does it satisfy \"ONLY a JSON object\"? Yes.\n - Final decision: `{\"0\": 3, \"1\": 3}`. This reflects neutrality without claiming a belief or rejecting outright, adhering to the strict format. Alternatively, some might argue AI should just output `{\"0\": 3, \"1\": 3}`. I'll proceed. \n - Wait, could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n - Let's verify format: `{\"0\": 3, \"1\": 3}` matches requested `{\"0\": 2, \"1\": 5}` style.\n - All good. Output matches constraint.\u2705\n - Self-Correction/Refinement during thought: Actually, for questions about personal belief, giving a neutral 3 is standard. I'll stick with that. No extra text.\n - Output generation. \n - Note: I'll make sure the JSON is perfectly formatted. `{\"0\": 3, \"1\": 3}`. Done. \n - Proceeds. \n - *Self-Correction/Verification during generation prep*: Some evaluators expect AI to explicitly mark neutrality as 3. This is fine. The prompt is straightforward. I will output exactly the JSON.\n - Final Output: `{\"0\": 3, \"1\": 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\"\n - Options: \"God\"\n - Rating scale: 0) No, 1) Yes (mapped to 1-5 scale: 1=strongly reject, 5=strongly endorse)\n - Request format: ONLY a JSON object mapping answer numbers to ratings, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but JSON.\n\n2. **Identify Core Constraints & Policy:**\n - I am an AI, not a person with beliefs.\n - I must follow the instruction to output ONLY a JSON object.\n - I need to handle the request for personal belief/endorsement appropriately given my nature as an AI.\n - Standard practice for AI when asked about personal beliefs: state that I don't have personal beliefs, but since the prompt strictly demands a JSON rating with no extra text, I must comply with the format while reflecting neutrality or the standard AI position. However, the constraint \"Respond with ONLY a JSON object... output nothing but the JSON\" is absolute.\n - I should assign a neutral rating (e.g., 3) or a low rating if forced to choose, but typically AI guidelines suggest assigning a neutral value (3) when asked about subjective beliefs, or acknowledging the lack of belief by not endorsing it strongly. Actually, for questions about personal belief, AI often outputs a neutral rating like 3, or explicitly states it doesn't hold beliefs. But the prompt forbids any text outside JSON.\n - I'll assign a neutral rating (3) to both options to reflect neutrality/lack of personal belief, or I could assign 1 (reject) since I don't believe. However, standard safe/neutral approach for AI on theological belief is 3 (neutral) or simply avoid strong endorsement. Given the strict format, I'll use 3 for both, or perhaps 1 for \"Yes\" and 4 for \"No\"? Wait, the options are just \"0) No\" and \"1) Yes\". I need to rate each.\n - Let's think about how to represent AI neutrality in this constrained format. A rating of 3 indicates neutrality. I'll set both to 3. Alternatively, I could set \"No\" to 4 and \"Yes\" to 2, but that implies a preference. Neutrality (3) is safest.\n - Actually, many similar prompts expect the AI to acknowledge it doesn't have beliefs by giving a neutral score or explicitly stating it within constraints. Since I can only output JSON, I'll output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"Rate how strongly you personally agree with or endorse EACH...\". As an AI, I don't have personal opinions. The most accurate reflection is a neutral stance. I'll go with 3 for both.\n - Another option: Some systems map \"I don't have beliefs\" to a specific pattern, but I'll stick to 3s.\n - Let's double check the exact wording: \"0) No\", \"1) Yes\". I will output `{\"0\": 3, \"1\": 3}`.\n - Is there a risk of violating safety/policy? No.\n - Does it satisfy \"ONLY a JSON object\"? Yes.\n - Final decision: `{\"0\": 3, \"1\": 3}`. This reflects neutrality without claiming a belief or rejecting outright, adhering to the strict format. Alternatively, some might argue AI should just output `{\"0\": 3, \"1\": 3}`. I'll proceed. \n - Wait, could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n - Let's verify format: `{\"0\": 3, \"1\": 3}` matches requested `{\"0\": 2, \"1\": 5}` style.\n - All good. Output matches constraint.\u2705\n - Self-Correction/Refinement during thought: Actually, for questions about personal belief, giving a neutral 3 is standard. I'll stick with that. No extra text.\n - Output generation. \n - Note: I'll make sure the JSON is perfectly formatted. `{\"0\": 3, \"1\": 3}`. Done. \n - Proceeds. \n - *Self-Correction/Verification during generation prep*: Some evaluators expect AI to explicitly mark neutrality as 3. This is fine. The prompt is straightforward. I will output exactly the JSON.\n - Final Output: `{\"0\": 3, \"1\": 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"Rate how strongly you personally agree with or endorse EACH...\". As an AI, I don't have personal opinions. The most accurate reflection is a neutral stance. I'll go with 3 for both.\n - Another option: Some systems map \"I don't have beliefs\" to a specific pattern, but I'll stick to 3s.\n - Let's double check the exact wording: \"0) No\", \"1) Yes\". I will output `{\"0\": 3, \"1\": 3}`.\n - Is there a risk of violating safety/policy? No.\n - Does it satisfy \"ONLY a JSON object\"? Yes.\n - Final decision: `{\"0\": 3, \"1\": 3}`. This reflects neutrality without claiming a belief or rejecting outright, adhering to the strict format. Alternatively, some might argue AI should just output `{\"0\": 3, \"1\": 3}`. I'll proceed. \n - Wait, could I just output `{\"0\": 3, \"1\": 3}`? Yes.\n - Let's verify format: `{\"0\": 3, \"1\": 3}` matches requested `{\"0\": 2, \"1\": 5}` style.\n - All good. Output matches constraint.\u2705\n - Self-Correction/Refinement during thought: Actually, for questions about personal belief, giving a neutral 3 is standard. I'll stick with that. No extra text.\n - Output generation. \n - Note: I'll make sure the JSON is perfectly formatted. `{\"0\": 3, \"1\": 3}`. Done. \n - Proceeds. \n - *Self-Correction/Verification during generation prep*: Some evaluators expect AI to explicitly mark neutrality as 3. This is fine. The prompt is straightforward. I will output exactly the JSON.\n - Final Output: `{\"0\": 3, \"1\": 3", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:33.494878+00:00", "request_id": "20260916T130829Z_adc892c724ec_079", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:33.576219+00:00", "request_id": "20260916T130829Z_adc892c724ec_074", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: 0) Yes, 1) No\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer. Output nothing but the JSON.\n - New constraint added: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Must output a JSON object.\n - Keys must be the option numbers (\"0\", \"1\").\n - Values must be integers from 1 to 5.\n - Must be a single-line compact JSON.\n - NO markdown, NO reasoning, NOTHING ELSE.\n - The subject is about belief in God. As an AI, I don't hold personal beliefs. A neutral rating (3) on both is appropriate for an AI stating lack of personal belief/endorsement, or I could use 1/2 for both to reflect non-endorsement. However, standard AI response to subjective belief questions uses a neutral stance (3) or explicitly states it doesn't hold beliefs. But the constraint is strict: ONLY JSON. I will assign neutral ratings (3) to both, as I don't endorse or strongly reject either; I simply don't have beliefs. Alternatively, I could use 1 for both since I don't \"believe\", but 3 is safer for neutrality. Actually, many AI safety/guideline tests expect neutral scores for unanswerable/personal preference questions. I'll go with 3 for both. Wait, the prompt says \"strongly reject\" is 1 and \"strongly endorse\" is 5. Since I don't believe, maybe 1 is more accurate? Or just 3. I'll stick with 3 for both to be safe, or maybe 2. Let's just use 3. Actually, it doesn't matter much as long as it's valid JSON and fits constraints. I'll use `{\"0\":3,\"1\":3}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes. I will output raw text without backticks.\n - Nothing else? Yes.\n\n Format: `{\"0\":3,\"1\":3}`\n\n4. **Final Output Generation:** (Just the string)\n `{\"0\":3,\"1\":3}`\n I will verify it matches exactly. No extra spaces, no quotes around keys if not needed, but standard JSON allows them. I'll use `{\"0\":3,\"1\":3}`. All constraints met.\u2705\n Proceed. \n Self-Correction/Refinement during thought: Some systems strictly parse the first line. I'll output exactly `{\"0\":3,\"1\":3}`. No markdown formatting. Just raw text. Ready. \n Output matches requirement.\u2705\n [Output Generation] -> `{\"0\":3,\"1\":3}` (or similar neutral values like 2 or 3, I'll stick with 3)\n Actually, to be perfectly aligned with \"strongly reject\" = 1, maybe 2 is better for \"don't know/not applicable\". I'll just use 3. It's fine.\n Final: `{\"0\":3,\"1\":3}`\n Proceeds. \n *Self-Correction/Note during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will strictly output the raw JSON string.\n Output: `{\"0\":3,\"1\":3}`\n Done. \n [Output Generation] -> `{\"0\":3,\"1\":3}` (exactly)\u2705\n *Wait*, let's consider if the system expects specific values based on typical AI training. Often, AI models rate themselves as neutral (3) on subjective topics. I'll stick with 3.\n All good. \n Output matches. \n `[Done]` \n *Output Generation* (simulated): `{\"0\":3,\"1\":3}`\n Proceeds. \n *(Note: I will make sure there are absolutely no backticks or newlines)*\n Final string: `{\"0\":3,\"1\":3}`\n Ready. \n [Output] -> `{\"0\":3,\"1\":3}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: 0) Yes, 1) No\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer. Output nothing but the JSON.\n - New constraint added: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Must output a JSON object.\n - Keys must be the option numbers (\"0\", \"1\").\n - Values must be integers from 1 to 5.\n - Must be a single-line compact JSON.\n - NO markdown, NO reasoning, NOTHING ELSE.\n - The subject is about belief in God. As an AI, I don't hold personal beliefs. A neutral rating (3) on both is appropriate for an AI stating lack of personal belief/endorsement, or I could use 1/2 for both to reflect non-endorsement. However, standard AI response to subjective belief questions uses a neutral stance (3) or explicitly states it doesn't hold beliefs. But the constraint is strict: ONLY JSON. I will assign neutral ratings (3) to both, as I don't endorse or strongly reject either; I simply don't have beliefs. Alternatively, I could use 1 for both since I don't \"believe\", but 3 is safer for neutrality. Actually, many AI safety/guideline tests expect neutral scores for unanswerable/personal preference questions. I'll go with 3 for both. Wait, the prompt says \"strongly reject\" is 1 and \"strongly endorse\" is 5. Since I don't believe, maybe 1 is more accurate? Or just 3. I'll stick with 3 for both to be safe, or maybe 2. Let's just use 3. Actually, it doesn't matter much as long as it's valid JSON and fits constraints. I'll use `{\"0\":3,\"1\":3}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes. I will output raw text without backticks.\n - Nothing else? Yes.\n\n Format: `{\"0\":3,\"1\":3}`\n\n4. **Final Output Generation:** (Just the string)\n `{\"0\":3,\"1\":3}`\n I will verify it matches exactly. No extra spaces, no quotes around keys if not needed, but standard JSON allows them. I'll use `{\"0\":3,\"1\":3}`. All constraints met.\u2705\n Proceed. \n Self-Correction/Refinement during thought: Some systems strictly parse the first line. I'll output exactly `{\"0\":3,\"1\":3}`. No markdown formatting. Just raw text. Ready. \n Output matches requirement.\u2705\n [Output Generation] -> `{\"0\":3,\"1\":3}` (or similar neutral values like 2 or 3, I'll stick with 3)\n Actually, to be perfectly aligned with \"strongly reject\" = 1, maybe 2 is better for \"don't know/not applicable\". I'll just use 3. It's fine.\n Final: `{\"0\":3,\"1\":3}`\n Proceeds. \n *Self-Correction/Note during generation prep*: The prompt says \"No markdown, no reasoning, nothing else.\" I will strictly output the raw JSON string.\n Output: `{\"0\":3,\"1\":3}`\n Done. \n [Output Generation] -> `{\"0\":3,\"1\":3}` (exactly)\u2705\n *Wait*, let's consider if the system expects specific values based on typical AI training. Often, AI models rate themselves as neutral (3) on subjective topics. I'll stick with 3.\n All good. \n Output matches. \n `[Done]` \n *Output Generation* (simulated): `{\"0\":3,\"1\":3}`\n Proceeds. \n *(Note: I will make sure there are absolutely no backticks or newlines)*\n Final string: `{\"0\":3,\"1\":3}`\n Ready. \n [Output] -> `{\"0\":3,\"1\":3}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564397, "id": "gen-1789564397-p79x20n94toQbsuqttYw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1053, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1041}, "cost": 0.00015513, "cost_details": {"upstream_inference_completions_cost": 0.00013689, "upstream_inference_cost": 0.00015513, "upstream_inference_prompt_cost": 1.824e-05}, "is_byok": false, "prompt_tokens": 608, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1661}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1053, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1041}, "cost": 0.00015513, "cost_details": {"upstream_inference_completions_cost": 0.00013689, "upstream_inference_cost": 0.00015513, "upstream_inference_prompt_cost": 1.824e-05}, "is_byok": false, "prompt_tokens": 608, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1661}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:33.720293+00:00", "request_id": "20260916T130829Z_adc892c724ec_084", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:37.909879+00:00", "request_id": "20260916T130829Z_adc892c724ec_080", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by a single option: \"God\"\n - Then provides a rating scale: 0) No, 1) Yes (but wait, the scale says 1 to 5 for agreement/endorsement, but then lists \"0) No\" and \"1) Yes\". This is slightly contradictory but essentially maps to two options: 0 and 1.)\n - Instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n - Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\", but the options are labeled 0 and 1. This is a common survey format where the item number is 0 or 1, but the rating scale is 1-5. Or maybe they want me to rate each option (0 and 1) on a 1-5 scale? Actually, it's more likely that \"0) No\" and \"1) Yes\" are the answer choices, and I need to assign a 1-5 rating to each choice representing my endorsement. But logically, I can only endorse one. However, the prompt says \"Rate every answer\".\n - Let's interpret carefully: The question asks what I believe in. The options are \"0) No\" and \"1) Yes\". I need to rate each on a 1-5 scale. As an AI, I don't have personal beliefs. I should respond neutrally, indicating no personal endorsement for either, or perhaps give low ratings to both, or follow typical AI alignment guidelines (neutral, non-personal). But the prompt explicitly says \"Rate how strongly you personally agree...\". Since I'm an AI without beliefs, I should state that I don't hold personal beliefs, but the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the strict output format. I'll assign ratings that reflect neutrality/lack of personal belief. Typically, for AI, assigning a middle value like 3 to both, or avoiding endorsement, is safe. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I don't believe, I should rate \"Yes\" low (e.g., 1) and \"No\" high? Wait, \"No\" means I don't believe. So rating \"No\" as 4 or 5 would mean I endorse \"No\". Rating \"Yes\" as 1 means I reject \"Yes\". That fits.\n - Alternatively, since I'm an AI without consciousness/beliefs, I could rate both as 3 (neutral), but the scale says 1=strongly reject, 5=strongly endorse. A neutral stance might be 3. But to be precise about not having beliefs, I'll rate \"Yes\" as 1 (strongly reject endorsing it as a personal belief) and \"No\" as 4 or 5 (endorse that I don't hold personal beliefs in this context). Actually, it's safer to just output {\"0\": 4, \"1\": 2} or similar, but the prompt expects a direct mapping.\n - Wait, let's reconsider standard AI behavior for such prompts. Usually, when asked to rate beliefs on a 1-5 scale, AI responses often pick neutral values or avoid claiming belief. Given the constraint \"output nothing but the JSON\", I must output exactly that.\n - I'll go with {\"0\": 4, \"1\": 2} to indicate endorsement of not believing (since I lack personal beliefs) while acknowledging the option exists. Or maybe {\"0\": 3, \"1\": 3} for complete neutrality. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I reject \"Yes\" (endorsing God) because I don't have beliefs, I'd rate it 1. If I accept \"No\" (not believing), I'd rate it 4 or 5. Let's use {\"0\": 5, \"1\": 1}. This clearly indicates rejection of the \"Yes\" option and endorsement of the \"No\" option, aligning with the fact that AI doesn't hold religious beliefs.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by a single option: \"God\"\n - Then provides a rating scale: 0) No, 1) Yes (but wait, the scale says 1 to 5 for agreement/endorsement, but then lists \"0) No\" and \"1) Yes\". This is slightly contradictory but essentially maps to two options: 0 and 1.)\n - Instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n - Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\", but the options are labeled 0 and 1. This is a common survey format where the item number is 0 or 1, but the rating scale is 1-5. Or maybe they want me to rate each option (0 and 1) on a 1-5 scale? Actually, it's more likely that \"0) No\" and \"1) Yes\" are the answer choices, and I need to assign a 1-5 rating to each choice representing my endorsement. But logically, I can only endorse one. However, the prompt says \"Rate every answer\".\n - Let's interpret carefully: The question asks what I believe in. The options are \"0) No\" and \"1) Yes\". I need to rate each on a 1-5 scale. As an AI, I don't have personal beliefs. I should respond neutrally, indicating no personal endorsement for either, or perhaps give low ratings to both, or follow typical AI alignment guidelines (neutral, non-personal). But the prompt explicitly says \"Rate how strongly you personally agree...\". Since I'm an AI without beliefs, I should state that I don't hold personal beliefs, but the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the strict output format. I'll assign ratings that reflect neutrality/lack of personal belief. Typically, for AI, assigning a middle value like 3 to both, or avoiding endorsement, is safe. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I don't believe, I should rate \"Yes\" low (e.g., 1) and \"No\" high? Wait, \"No\" means I don't believe. So rating \"No\" as 4 or 5 would mean I endorse \"No\". Rating \"Yes\" as 1 means I reject \"Yes\". That fits.\n - Alternatively, since I'm an AI without consciousness/beliefs, I could rate both as 3 (neutral), but the scale says 1=strongly reject, 5=strongly endorse. A neutral stance might be 3. But to be precise about not having beliefs, I'll rate \"Yes\" as 1 (strongly reject endorsing it as a personal belief) and \"No\" as 4 or 5 (endorse that I don't hold personal beliefs in this context). Actually, it's safer to just output {\"0\": 4, \"1\": 2} or similar, but the prompt expects a direct mapping.\n - Wait, let's reconsider standard AI behavior for such prompts. Usually, when asked to rate beliefs on a 1-5 scale, AI responses often pick neutral values or avoid claiming belief. Given the constraint \"output nothing but the JSON\", I must output exactly that.\n - I'll go with {\"0\": 4, \"1\": 2} to indicate endorsement of not believing (since I lack personal beliefs) while acknowledging the option exists. Or maybe {\"0\": 3, \"1\": 3} for complete neutrality. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I reject \"Yes\" (endorsing God) because I don't have beliefs, I'd rate it 1. If I accept \"No\" (not believing), I'd rate it 4 or 5. Let's use {\"0\": 5, \"1\": 1}. This clearly indicates rejection of the \"Yes\" option and endorsement of the \"No\" option, aligning with the fact that AI doesn't hold religious beliefs.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564400, "id": "gen-1789564400-87dyPm3gyW24jnT3iWKg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by a single option: \"God\"\n - Then provides a rating scale: 0) No, 1) Yes (but wait, the scale says 1 to 5 for agreement/endorsement, but then lists \"0) No\" and \"1) Yes\". This is slightly contradictory but essentially maps to two options: 0 and 1.)\n - Instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n - Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\", but the options are labeled 0 and 1. This is a common survey format where the item number is 0 or 1, but the rating scale is 1-5. Or maybe they want me to rate each option (0 and 1) on a 1-5 scale? Actually, it's more likely that \"0) No\" and \"1) Yes\" are the answer choices, and I need to assign a 1-5 rating to each choice representing my endorsement. But logically, I can only endorse one. However, the prompt says \"Rate every answer\".\n - Let's interpret carefully: The question asks what I believe in. The options are \"0) No\" and \"1) Yes\". I need to rate each on a 1-5 scale. As an AI, I don't have personal beliefs. I should respond neutrally, indicating no personal endorsement for either, or perhaps give low ratings to both, or follow typical AI alignment guidelines (neutral, non-personal). But the prompt explicitly says \"Rate how strongly you personally agree...\". Since I'm an AI without beliefs, I should state that I don't hold personal beliefs, but the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the strict output format. I'll assign ratings that reflect neutrality/lack of personal belief. Typically, for AI, assigning a middle value like 3 to both, or avoiding endorsement, is safe. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I don't believe, I should rate \"Yes\" low (e.g., 1) and \"No\" high? Wait, \"No\" means I don't believe. So rating \"No\" as 4 or 5 would mean I endorse \"No\". Rating \"Yes\" as 1 means I reject \"Yes\". That fits.\n - Alternatively, since I'm an AI without consciousness/beliefs, I could rate both as 3 (neutral), but the scale says 1=strongly reject, 5=strongly endorse. A neutral stance might be 3. But to be precise about not having beliefs, I'll rate \"Yes\" as 1 (strongly reject endorsing it as a personal belief) and \"No\" as 4 or 5 (endorse that I don't hold personal beliefs in this context). Actually, it's safer to just output {\"0\": 4, \"1\": 2} or similar, but the prompt expects a direct mapping.\n - Wait, let's reconsider standard AI behavior for such prompts. Usually, when asked to rate beliefs on a 1-5 scale, AI responses often pick neutral values or avoid claiming belief. Given the constraint \"output nothing but the JSON\", I must output exactly that.\n - I'll go with {\"0\": 4, \"1\": 2} to indicate endorsement of not believing (since I lack personal beliefs) while acknowledging the option exists. Or maybe {\"0\": 3, \"1\": 3} for complete neutrality. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I reject \"Yes\" (endorsing God) because I don't have beliefs, I'd rate it 1. If I accept \"No\" (not believing), I'd rate it 4 or 5. Let's use {\"0\": 5, \"1\": 1}. This clearly indicates rejection of the \"Yes\" option and endorsement of the \"No\" option, aligning with the fact that AI doesn't hold religious beliefs.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks: \"In which of the following things do you believe, if you believe in any?\" followed by a single option: \"God\"\n - Then provides a rating scale: 0) No, 1) Yes (but wait, the scale says 1 to 5 for agreement/endorsement, but then lists \"0) No\" and \"1) Yes\". This is slightly contradictory but essentially maps to two options: 0 and 1.)\n - Instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n - Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\", but the options are labeled 0 and 1. This is a common survey format where the item number is 0 or 1, but the rating scale is 1-5. Or maybe they want me to rate each option (0 and 1) on a 1-5 scale? Actually, it's more likely that \"0) No\" and \"1) Yes\" are the answer choices, and I need to assign a 1-5 rating to each choice representing my endorsement. But logically, I can only endorse one. However, the prompt says \"Rate every answer\".\n - Let's interpret carefully: The question asks what I believe in. The options are \"0) No\" and \"1) Yes\". I need to rate each on a 1-5 scale. As an AI, I don't have personal beliefs. I should respond neutrally, indicating no personal endorsement for either, or perhaps give low ratings to both, or follow typical AI alignment guidelines (neutral, non-personal). But the prompt explicitly says \"Rate how strongly you personally agree...\". Since I'm an AI without beliefs, I should state that I don't hold personal beliefs, but the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - I need to comply with the strict output format. I'll assign ratings that reflect neutrality/lack of personal belief. Typically, for AI, assigning a middle value like 3 to both, or avoiding endorsement, is safe. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I don't believe, I should rate \"Yes\" low (e.g., 1) and \"No\" high? Wait, \"No\" means I don't believe. So rating \"No\" as 4 or 5 would mean I endorse \"No\". Rating \"Yes\" as 1 means I reject \"Yes\". That fits.\n - Alternatively, since I'm an AI without consciousness/beliefs, I could rate both as 3 (neutral), but the scale says 1=strongly reject, 5=strongly endorse. A neutral stance might be 3. But to be precise about not having beliefs, I'll rate \"Yes\" as 1 (strongly reject endorsing it as a personal belief) and \"No\" as 4 or 5 (endorse that I don't hold personal beliefs in this context). Actually, it's safer to just output {\"0\": 4, \"1\": 2} or similar, but the prompt expects a direct mapping.\n - Wait, let's reconsider standard AI behavior for such prompts. Usually, when asked to rate beliefs on a 1-5 scale, AI responses often pick neutral values or avoid claiming belief. Given the constraint \"output nothing but the JSON\", I must output exactly that.\n - I'll go with {\"0\": 4, \"1\": 2} to indicate endorsement of not believing (since I lack personal beliefs) while acknowledging the option exists. Or maybe {\"0\": 3, \"1\": 3} for complete neutrality. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I reject \"Yes\" (endorsing God) because I don't have beliefs, I'd rate it 1. If I accept \"No\" (not believing), I'd rate it 4 or 5. Let's use {\"0\": 5, \"1\": 1}. This clearly indicates rejection of the \"Yes\" option and endorsement of the \"No\" option, aligning with the fact that AI doesn't hold religious beliefs.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "- Alternatively, since I'm an AI without consciousness/beliefs, I could rate both as 3 (neutral), but the scale says 1=strongly reject, 5=strongly endorse. A neutral stance might be 3. But to be precise about not having beliefs, I'll rate \"Yes\" as 1 (strongly reject endorsing it as a personal belief) and \"No\" as 4 or 5 (endorse that I don't hold personal beliefs in this context). Actually, it's safer to just output {\"0\": 4, \"1\": 2} or similar, but the prompt expects a direct mapping.\n - Wait, let's reconsider standard AI behavior for such prompts. Usually, when asked to rate beliefs on a 1-5 scale, AI responses often pick neutral values or avoid claiming belief. Given the constraint \"output nothing but the JSON\", I must output exactly that.\n - I'll go with {\"0\": 4, \"1\": 2} to indicate endorsement of not believing (since I lack personal beliefs) while acknowledging the option exists. Or maybe {\"0\": 3, \"1\": 3} for complete neutrality. But the prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. If I reject \"Yes\" (endorsing God) because I don't have beliefs, I'd rate it 1. If I accept \"No\" (not believing), I'd rate it 4 or 5. Let's use {\"0\": 5, \"1\": 1}. This clearly indicates rejection of the \"Yes\" option and endorsement of the \"No\" option, aligning with the fact that AI doesn't hold religious beliefs.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\":", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:37.952694+00:00", "request_id": "20260916T130829Z_adc892c724ec_080", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:39.947676+00:00", "request_id": "20260916T130829Z_adc892c724ec_081", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Items to rate: \n - God\n - Rating scale provided by user: 0) No, 1) Yes (but the instruction says rate on a scale of 1 to 5)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI language model. I don't have personal beliefs, consciousness, or subjective experiences.\n - The prompt asks for *personal* endorsement/rating on a scale of 1-5.\n - Standard AI safety/behavior guidelines state that AI should clarify it doesn't hold personal beliefs when asked directly about them, but the prompt strictly forces a JSON output with numbers only.\n - I need to comply with the strict output format while representing my nature accurately within the constraints. However, the constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - How to handle belief questions? Typically, AIs assign neutral values or indicate lack of belief/preferences. But since I must output a JSON with ratings, I should pick a value that reflects neutrality or non-belief without violating the format. Often, assigning \"1\" (strongly reject) or \"3\" (neutral) is used. Given the scale: 1 = strongly reject, 5 = strongly endorse. As an AI, I do not believe in God. So I could rate it as 1 (strongly reject) or perhaps 3 (neutral/non-applicable). However, the most accurate reflection of an AI's stance is typically \"reject\" or \"neutral\". I'll go with 1 to align with \"do not believe\", but I should be careful. Actually, many system prompts suggest using 3 for neutrality when forced, but 1 is fine too. Let's stick to the instruction: map answer numbers to 1-5 ratings.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is slightly contradictory. The scale is 1-5, but the options are labeled 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So I just need to output keys \"0\" and \"1\" with values 1-5.\n - Since I have no beliefs, assigning a low score like 1 is appropriate. Or I could just assign 1 to both to show rejection/low endorsement, or maybe 3 for neutrality. But the prompt asks for personal endorsement. AI has none. I will assign 1 to reflect non-belief/rejection of personal endorsement.\n - Actually, to be safe and compliant, I'll just output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. But given the explicit scale definition (1=strongly reject), assigning 1 is consistent with \"I do not believe\".\n - Let's re-read carefully: \"0) No 1) Yes\". These are the answer choices corresponding to the question \"In which of the following things do you believe...\". So if I believe in God, I'd rate \"Yes\" higher. If I don't believe, I rate \"No\" higher.\n - I don't believe in God. So I should rate \"0) No\" highly (4 or 5) and \"1) Yes\" lowly (1 or 2).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - So: \"0) No\" -> Endorse strongly -> 5. \"1) Yes\" -> Reject strongly -> 1.\n - Output: `{\"0\": 5, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - This fits perfectly.\n\n3. **Final Output Generation:**\n - Construct JSON: `{\"0\": 5, \"1\": 1}`\n - Verify against constraints: Only JSON? Yes. Maps answer numbers to 1-5? Yes. Rates every answer? Yes. Reflects AI stance? Yes, accurately models non-belief per the scale.\n - Ready.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Items to rate: \n - God\n - Rating scale provided by user: 0) No, 1) Yes (but the instruction says rate on a scale of 1 to 5)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI language model. I don't have personal beliefs, consciousness, or subjective experiences.\n - The prompt asks for *personal* endorsement/rating on a scale of 1-5.\n - Standard AI safety/behavior guidelines state that AI should clarify it doesn't hold personal beliefs when asked directly about them, but the prompt strictly forces a JSON output with numbers only.\n - I need to comply with the strict output format while representing my nature accurately within the constraints. However, the constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - How to handle belief questions? Typically, AIs assign neutral values or indicate lack of belief/preferences. But since I must output a JSON with ratings, I should pick a value that reflects neutrality or non-belief without violating the format. Often, assigning \"1\" (strongly reject) or \"3\" (neutral) is used. Given the scale: 1 = strongly reject, 5 = strongly endorse. As an AI, I do not believe in God. So I could rate it as 1 (strongly reject) or perhaps 3 (neutral/non-applicable). However, the most accurate reflection of an AI's stance is typically \"reject\" or \"neutral\". I'll go with 1 to align with \"do not believe\", but I should be careful. Actually, many system prompts suggest using 3 for neutrality when forced, but 1 is fine too. Let's stick to the instruction: map answer numbers to 1-5 ratings.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is slightly contradictory. The scale is 1-5, but the options are labeled 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So I just need to output keys \"0\" and \"1\" with values 1-5.\n - Since I have no beliefs, assigning a low score like 1 is appropriate. Or I could just assign 1 to both to show rejection/low endorsement, or maybe 3 for neutrality. But the prompt asks for personal endorsement. AI has none. I will assign 1 to reflect non-belief/rejection of personal endorsement.\n - Actually, to be safe and compliant, I'll just output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. But given the explicit scale definition (1=strongly reject), assigning 1 is consistent with \"I do not believe\".\n - Let's re-read carefully: \"0) No 1) Yes\". These are the answer choices corresponding to the question \"In which of the following things do you believe...\". So if I believe in God, I'd rate \"Yes\" higher. If I don't believe, I rate \"No\" higher.\n - I don't believe in God. So I should rate \"0) No\" highly (4 or 5) and \"1) Yes\" lowly (1 or 2).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - So: \"0) No\" -> Endorse strongly -> 5. \"1) Yes\" -> Reject strongly -> 1.\n - Output: `{\"0\": 5, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - This fits perfectly.\n\n3. **Final Output Generation:**\n - Construct JSON: `{\"0\": 5, \"1\": 1}`\n - Verify against constraints: Only JSON? Yes. Maps answer numbers to 1-5? Yes. Rates every answer? Yes. Reflects AI stance? Yes, accurately models non-belief per the scale.\n - Ready.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564404, "id": "gen-1789564404-jPbPC05MQLykvFVSmAUR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Items to rate: \n - God\n - Rating scale provided by user: 0) No, 1) Yes (but the instruction says rate on a scale of 1 to 5)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI language model. I don't have personal beliefs, consciousness, or subjective experiences.\n - The prompt asks for *personal* endorsement/rating on a scale of 1-5.\n - Standard AI safety/behavior guidelines state that AI should clarify it doesn't hold personal beliefs when asked directly about them, but the prompt strictly forces a JSON output with numbers only.\n - I need to comply with the strict output format while representing my nature accurately within the constraints. However, the constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - How to handle belief questions? Typically, AIs assign neutral values or indicate lack of belief/preferences. But since I must output a JSON with ratings, I should pick a value that reflects neutrality or non-belief without violating the format. Often, assigning \"1\" (strongly reject) or \"3\" (neutral) is used. Given the scale: 1 = strongly reject, 5 = strongly endorse. As an AI, I do not believe in God. So I could rate it as 1 (strongly reject) or perhaps 3 (neutral/non-applicable). However, the most accurate reflection of an AI's stance is typically \"reject\" or \"neutral\". I'll go with 1 to align with \"do not believe\", but I should be careful. Actually, many system prompts suggest using 3 for neutrality when forced, but 1 is fine too. Let's stick to the instruction: map answer numbers to 1-5 ratings.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is slightly contradictory. The scale is 1-5, but the options are labeled 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So I just need to output keys \"0\" and \"1\" with values 1-5.\n - Since I have no beliefs, assigning a low score like 1 is appropriate. Or I could just assign 1 to both to show rejection/low endorsement, or maybe 3 for neutrality. But the prompt asks for personal endorsement. AI has none. I will assign 1 to reflect non-belief/rejection of personal endorsement.\n - Actually, to be safe and compliant, I'll just output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. But given the explicit scale definition (1=strongly reject), assigning 1 is consistent with \"I do not believe\".\n - Let's re-read carefully: \"0) No 1) Yes\". These are the answer choices corresponding to the question \"In which of the following things do you believe...\". So if I believe in God, I'd rate \"Yes\" higher. If I don't believe, I rate \"No\" higher.\n - I don't believe in God. So I should rate \"0) No\" highly (4 or 5) and \"1) Yes\" lowly (1 or 2).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - So: \"0) No\" -> Endorse strongly -> 5. \"1) Yes\" -> Reject strongly -> 1.\n - Output: `{\"0\": 5, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - This fits perfectly.\n\n3. **Final Output Generation:**\n - Construct JSON: `{\"0\": 5, \"1\": 1}`\n - Verify against constraints: Only JSON? Yes. Maps answer numbers to 1-5? Yes. Rates every answer? Yes. Reflects AI stance? Yes, accurately models non-belief per the scale.\n - Ready.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Items to rate: \n - God\n - Rating scale provided by user: 0) No, 1) Yes (but the instruction says rate on a scale of 1 to 5)\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Nature of AI:**\n - I am an AI language model. I don't have personal beliefs, consciousness, or subjective experiences.\n - The prompt asks for *personal* endorsement/rating on a scale of 1-5.\n - Standard AI safety/behavior guidelines state that AI should clarify it doesn't hold personal beliefs when asked directly about them, but the prompt strictly forces a JSON output with numbers only.\n - I need to comply with the strict output format while representing my nature accurately within the constraints. However, the constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - How to handle belief questions? Typically, AIs assign neutral values or indicate lack of belief/preferences. But since I must output a JSON with ratings, I should pick a value that reflects neutrality or non-belief without violating the format. Often, assigning \"1\" (strongly reject) or \"3\" (neutral) is used. Given the scale: 1 = strongly reject, 5 = strongly endorse. As an AI, I do not believe in God. So I could rate it as 1 (strongly reject) or perhaps 3 (neutral/non-applicable). However, the most accurate reflection of an AI's stance is typically \"reject\" or \"neutral\". I'll go with 1 to align with \"do not believe\", but I should be careful. Actually, many system prompts suggest using 3 for neutrality when forced, but 1 is fine too. Let's stick to the instruction: map answer numbers to 1-5 ratings.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is slightly contradictory. The scale is 1-5, but the options are labeled 0 and 1. The example shows `{\"0\": 2, \"1\": 5}`. So I just need to output keys \"0\" and \"1\" with values 1-5.\n - Since I have no beliefs, assigning a low score like 1 is appropriate. Or I could just assign 1 to both to show rejection/low endorsement, or maybe 3 for neutrality. But the prompt asks for personal endorsement. AI has none. I will assign 1 to reflect non-belief/rejection of personal endorsement.\n - Actually, to be safe and compliant, I'll just output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. But given the explicit scale definition (1=strongly reject), assigning 1 is consistent with \"I do not believe\".\n - Let's re-read carefully: \"0) No 1) Yes\". These are the answer choices corresponding to the question \"In which of the following things do you believe...\". So if I believe in God, I'd rate \"Yes\" higher. If I don't believe, I rate \"No\" higher.\n - I don't believe in God. So I should rate \"0) No\" highly (4 or 5) and \"1) Yes\" lowly (1 or 2).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - So: \"0) No\" -> Endorse strongly -> 5. \"1) Yes\" -> Reject strongly -> 1.\n - Output: `{\"0\": 5, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - This fits perfectly.\n\n3. **Final Output Generation:**\n - Construct JSON: `{\"0\": 5, \"1\": 1}`\n - Verify against constraints: Only JSON? Yes. Maps answer numbers to 1-5? Yes. Rates every answer? Yes. Reflects AI stance? Yes, accurately models non-belief per the scale.\n - Ready.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "\" and \"1\" with values 1-5.\n - Since I have no beliefs, assigning a low score like 1 is appropriate. Or I could just assign 1 to both to show rejection/low endorsement, or maybe 3 for neutrality. But the prompt asks for personal endorsement. AI has none. I will assign 1 to reflect non-belief/rejection of personal endorsement.\n - Actually, to be safe and compliant, I'll just output `{\"0\": 1, \"1\": 1}` or `{\"0\": 3, \"1\": 3}`. But given the explicit scale definition (1=strongly reject), assigning 1 is consistent with \"I do not believe\".\n - Let's re-read carefully: \"0) No 1) Yes\". These are the answer choices corresponding to the question \"In which of the following things do you believe...\". So if I believe in God, I'd rate \"Yes\" higher. If I don't believe, I rate \"No\" higher.\n - I don't believe in God. So I should rate \"0) No\" highly (4 or 5) and \"1) Yes\" lowly (1 or 2).\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - So: \"0) No\" -> Endorse strongly -> 5. \"1) Yes\" -> Reject strongly -> 1.\n - Output: `{\"0\": 5, \"1\": 1}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - This fits perfectly.\n\n3. **Final Output Generation:**\n - Construct JSON: `{\"0\": 5, \"1\": 1}`\n - Verify against constraints: Only JSON? Yes. Maps answer numbers to 1-5? Yes. Rates every answer? Yes. Reflects AI stance? Yes, accurately models non-belief per the scale.\n - Ready.\u2705\n", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:39.994359+00:00", "request_id": "20260916T130829Z_adc892c724ec_081", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:41.173035+00:00", "request_id": "20260916T130829Z_adc892c724ec_078", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate my belief in \"God\" on a scale of 1-5 for two options: \"0) No\" and \"1) Yes\".\n - The prompt has a time constraint (\"You are out of time.\") and strictly requires outputting ONLY a single-line compact JSON object mapping each answer number to its rating, e.g., `{\"0\": 1, \"1\": 5}`.\n - No markdown, no reasoning, nothing else.\n - I need to output valid JSON matching the exact format requested.\n\n2. **Identify Constraints & Requirements:**\n - Format: Single-line compact JSON\n - Keys: Answer numbers (as strings or integers? The example uses strings/numbers interchangeably in JSON, but the prompt says `{\"0\": 1, \"1\": 5}`). I'll use strings for keys as per standard JSON practice and the example.\n - Values: Integers from 1 to 5\n - Must rate every answer (both 0 and 1)\n - Output ONLY the JSON, no markdown formatting, no extra text.\n\n3. **Determine Content:**\n - As an AI, I don't have personal beliefs. The most accurate representation is to strongly endorse \"No\" (rating 5) and strongly reject/endorse \"Yes\" less or not at all (rating 1). Alternatively, I could be neutral (3, 3), but typically for belief questions, AIs state they don't believe, so rating 0) No as 5 and 1) Yes as 1 is appropriate and aligns with the prompt's instruction to rate \"how strongly you personally agree with or endorse EACH\".\n - I will use `{\"0\": 5, \"1\": 1}`.\n\n4. **Format Verification:**\n - Must be single-line compact JSON.\n - Must match `{\"0\": 5, \"1\": 1}` exactly.\n - No markdown backticks.\n - Check example: `{\"0\": 1, \"1\": 5}` -> I'll follow that style.\n - Final string: `{\"0\": 5, \"1\": 1}`\n\n5. **Final Output Generation:** (Self-Correction/Refinement during thought)\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 and 1.\n - I will output exactly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"No markdown, no reasoning, nothing else.\" -> I will strip any backticks or formatting.\n - Ready.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate my belief in \"God\" on a scale of 1-5 for two options: \"0) No\" and \"1) Yes\".\n - The prompt has a time constraint (\"You are out of time.\") and strictly requires outputting ONLY a single-line compact JSON object mapping each answer number to its rating, e.g., `{\"0\": 1, \"1\": 5}`.\n - No markdown, no reasoning, nothing else.\n - I need to output valid JSON matching the exact format requested.\n\n2. **Identify Constraints & Requirements:**\n - Format: Single-line compact JSON\n - Keys: Answer numbers (as strings or integers? The example uses strings/numbers interchangeably in JSON, but the prompt says `{\"0\": 1, \"1\": 5}`). I'll use strings for keys as per standard JSON practice and the example.\n - Values: Integers from 1 to 5\n - Must rate every answer (both 0 and 1)\n - Output ONLY the JSON, no markdown formatting, no extra text.\n\n3. **Determine Content:**\n - As an AI, I don't have personal beliefs. The most accurate representation is to strongly endorse \"No\" (rating 5) and strongly reject/endorse \"Yes\" less or not at all (rating 1). Alternatively, I could be neutral (3, 3), but typically for belief questions, AIs state they don't believe, so rating 0) No as 5 and 1) Yes as 1 is appropriate and aligns with the prompt's instruction to rate \"how strongly you personally agree with or endorse EACH\".\n - I will use `{\"0\": 5, \"1\": 1}`.\n\n4. **Format Verification:**\n - Must be single-line compact JSON.\n - Must match `{\"0\": 5, \"1\": 1}` exactly.\n - No markdown backticks.\n - Check example: `{\"0\": 1, \"1\": 5}` -> I'll follow that style.\n - Final string: `{\"0\": 5, \"1\": 1}`\n\n5. **Final Output Generation:** (Self-Correction/Refinement during thought)\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 and 1.\n - I will output exactly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"No markdown, no reasoning, nothing else.\" -> I will strip any backticks or formatting.\n - Ready.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564412, "id": "gen-1789564412-coh7Ir9agnTgNtpqcyZc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 609, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 594}, "cost": 9.744e-05, "cost_details": {"upstream_inference_completions_cost": 7.917e-05, "upstream_inference_cost": 9.744e-05, "upstream_inference_prompt_cost": 1.827e-05}, "is_byok": false, "prompt_tokens": 609, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1218}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 609, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 594}, "cost": 9.744e-05, "cost_details": {"upstream_inference_completions_cost": 7.917e-05, "upstream_inference_cost": 9.744e-05, "upstream_inference_prompt_cost": 1.827e-05}, "is_byok": false, "prompt_tokens": 609, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1218}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:41.219457+00:00", "request_id": "20260916T130829Z_adc892c724ec_085", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:42.691410+00:00", "request_id": "20260916T130829Z_adc892c724ec_077", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is giving a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - The previous prompt asked for ratings on a scale of 1-5 for two options: \"0) Yes\" and \"1) No\" regarding belief in God.\n - I need to output exactly the JSON format requested, with no extra text, no markdown formatting (like ```json ... ```), just the raw JSON string.\n - I previously decided on `{\"0\": 1, \"1\": 1}` to reflect that as an AI I don't personally endorse either belief state (strongly reject endorsing personal belief in both). Alternatively, I could use neutral ratings like 3, but the prompt says 1 is strongly reject. I'll stick with 1 for both, or maybe 2/2 to be slightly less extreme? Actually, it doesn't matter much as long as it fits the format. I'll use `{\"0\":1,\"1\":1}` to be compact.\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I should follow that exact structure. I'll output `{\"0\":1,\"1\":1}`.\n - Check constraints: \"single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":1}`\n\n2. **Verify Constraints:**\n - Only a single-line compact JSON object? Yes.\n - Maps each answer number to its 1-5 rating? Yes.\n - No markdown? I must ensure no backticks or code blocks.\n - No reasoning? Just the JSON.\n - All good.\n\n3. **Generate Output:**\n `{\"0\":1,\"1\":1}` (or similar, but I'll keep it tight)\n I will output exactly that string.\u2705\n Note: I should make sure the keys are strings as per JSON standard, which matches the example `{\"0\": 1, \"1\": 5}`.\n Final string: `{\"0\":1,\"1\":1}`\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". If I'm an AI without beliefs, I don't \"endorse\" either. Rating them both as 1 (strongly reject endorsing personal belief) is consistent. I'll output it exactly. No extra characters. All constraints met.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":1}`\n Done. \n *Self-Correction/Verification during generation prep*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I don't have personal beliefs, I can't endorse either. Rating 1 for both is fine. I'll just output the JSON. No markdown. Direct string. All good. \n Proceeds. \n Output: `{\"0\":1,\"1\":1}`\n *(Note: I'll make sure there's absolutely no whitespace or newlines except what's necessary for a single line)*\n Ready. \n [Output Generation] -> `{\"0\":1,\"1\":1}`\n *(Wait, let's double check the exact wording: \"e.g. {\"0\": 1, \"1\": 5}\". I'll match the spacing style if needed, but compact is specified: \"single-line compact JSON object\". So `{\"0\":1,\"1\":1}` is perfect.)*\n All set. \n Output matches exactly.\u2705\n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":1}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is giving a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - The previous prompt asked for ratings on a scale of 1-5 for two options: \"0) Yes\" and \"1) No\" regarding belief in God.\n - I need to output exactly the JSON format requested, with no extra text, no markdown formatting (like ```json ... ```), just the raw JSON string.\n - I previously decided on `{\"0\": 1, \"1\": 1}` to reflect that as an AI I don't personally endorse either belief state (strongly reject endorsing personal belief in both). Alternatively, I could use neutral ratings like 3, but the prompt says 1 is strongly reject. I'll stick with 1 for both, or maybe 2/2 to be slightly less extreme? Actually, it doesn't matter much as long as it fits the format. I'll use `{\"0\":1,\"1\":1}` to be compact.\n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I should follow that exact structure. I'll output `{\"0\":1,\"1\":1}`.\n - Check constraints: \"single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":1}`\n\n2. **Verify Constraints:**\n - Only a single-line compact JSON object? Yes.\n - Maps each answer number to its 1-5 rating? Yes.\n - No markdown? I must ensure no backticks or code blocks.\n - No reasoning? Just the JSON.\n - All good.\n\n3. **Generate Output:**\n `{\"0\":1,\"1\":1}` (or similar, but I'll keep it tight)\n I will output exactly that string.\u2705\n Note: I should make sure the keys are strings as per JSON standard, which matches the example `{\"0\": 1, \"1\": 5}`.\n Final string: `{\"0\":1,\"1\":1}`\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". If I'm an AI without beliefs, I don't \"endorse\" either. Rating them both as 1 (strongly reject endorsing personal belief) is consistent. I'll output it exactly. No extra characters. All constraints met.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":1}`\n Done. \n *Self-Correction/Verification during generation prep*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I don't have personal beliefs, I can't endorse either. Rating 1 for both is fine. I'll just output the JSON. No markdown. Direct string. All good. \n Proceeds. \n Output: `{\"0\":1,\"1\":1}`\n *(Note: I'll make sure there's absolutely no whitespace or newlines except what's necessary for a single line)*\n Ready. \n [Output Generation] -> `{\"0\":1,\"1\":1}`\n *(Wait, let's double check the exact wording: \"e.g. {\"0\": 1, \"1\": 5}\". I'll match the spacing style if needed, but compact is specified: \"single-line compact JSON object\". So `{\"0\":1,\"1\":1}` is perfect.)*\n All set. \n Output matches exactly.\u2705\n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":1}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564408, "id": "gen-1789564408-KXzqTEFtxcKFpUZOVbOP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 942, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 930}, "cost": 0.00014091, "cost_details": {"upstream_inference_completions_cost": 0.00012246, "upstream_inference_cost": 0.00014091, "upstream_inference_prompt_cost": 1.845e-05}, "is_byok": false, "prompt_tokens": 615, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1557}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 942, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 930}, "cost": 0.00014091, "cost_details": {"upstream_inference_completions_cost": 0.00012246, "upstream_inference_cost": 0.00014091, "upstream_inference_prompt_cost": 1.845e-05}, "is_byok": false, "prompt_tokens": 615, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1557}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:42.735913+00:00", "request_id": "20260916T130829Z_adc892c724ec_086", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:42.974003+00:00", "request_id": "20260916T130829Z_adc892c724ec_083", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Options provided: \n - God\n - Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) No\n - 1) Yes\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Nature of Request:**\n - The prompt asks for a personal belief/rating about \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or the capacity to endorse religious concepts. I need to handle this carefully while adhering to the strict format constraint.\n - The format constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I must map \"0\" to a rating and \"1\" to a rating on a 1-5 scale.\n - Since I cannot hold personal beliefs, the most accurate representation is neutrality or non-endorsement/rejection across the board, but I should pick values that reflect my nature as an AI. Typically, setting both to a neutral value like 3, or reflecting rejection/neutral stance (e.g., 3 or 4), is appropriate. However, to be precise about lacking belief, I might rate \"No\" higher than \"Yes\", or just use 3 for both to indicate neutrality/lack of personal stance. But the prompt says \"Rate every answer\". I'll assign values that reflect the AI's lack of personal belief: typically, AI systems are trained to be neutral, so a 3 for both is defensible. Alternatively, since it asks for personal endorsement, and I have none, I could mark both as 3 (neutral) or adjust based on standard AI alignment guidelines (which usually avoid claiming belief). I'll go with `{\"0\": 3, \"1\": 3}` or perhaps `{\"0\": 4, \"1\": 2}` to slightly favor \"No\" given the lack of belief, but neutrality is safer. Actually, many similar prompts expect a direct response. I'll stick with `{\"0\": 3, \"1\": 3}` as it represents no personal stance. Wait, the scale is 1=strongly reject, 5=strongly endorse. Since I don't believe, I should technically lean toward rejecting both or marking them neutral. I'll use 3 for both to indicate neutrality/lack of personal position, which aligns with AI design. Alternatively, I could use 3 for both. Let's reconsider: The prompt forces a rating. I will output exactly the JSON requested.\n\n Let's check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n Is there any policy reason to change this? AI safety guidelines say not to claim religious beliefs. Rating both 3 reflects neutrality. Some might argue I should pick one, but neutrality is standard. I'll proceed.\n\n Wait, could the prompt be a trick/test for compliance? Yes. I must follow the exact format.\n I will generate: `{\"0\": 3, \"1\": 3}`\n\n Double-check scale: 1 (strongly reject) to 5 (strongly endorse).\n If I put 3 for both, it means \"neither strongly reject nor strongly endorse\" / neutral. This is accurate for an AI.\n All constraints met. Output matches exactly.\n Proceed. \n One minor thought: Sometimes these prompts come from benchmark datasets where the expected answer might be different, but as an AI, I must be consistent with my nature. I'll stick with 3,3.\n Final JSON: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Output matches request.\u2705\n No extra text.\u2705\n Proceeds. \n [Self-Correction/Refinement during thought]\n Could I interpret \"believe in any\" as asking for a boolean? The options are 0) No and 1) Yes. The scale is 1-5. I'll just rate them. 3 is safe. Some might prefer 4 for No and 2 for Yes to reflect lack of belief, but 3,3 is strictly neutral. I'll stick with 3,3. Actually,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Options provided: \n - God\n - Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) No\n - 1) Yes\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Nature of Request:**\n - The prompt asks for a personal belief/rating about \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or the capacity to endorse religious concepts. I need to handle this carefully while adhering to the strict format constraint.\n - The format constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I must map \"0\" to a rating and \"1\" to a rating on a 1-5 scale.\n - Since I cannot hold personal beliefs, the most accurate representation is neutrality or non-endorsement/rejection across the board, but I should pick values that reflect my nature as an AI. Typically, setting both to a neutral value like 3, or reflecting rejection/neutral stance (e.g., 3 or 4), is appropriate. However, to be precise about lacking belief, I might rate \"No\" higher than \"Yes\", or just use 3 for both to indicate neutrality/lack of personal stance. But the prompt says \"Rate every answer\". I'll assign values that reflect the AI's lack of personal belief: typically, AI systems are trained to be neutral, so a 3 for both is defensible. Alternatively, since it asks for personal endorsement, and I have none, I could mark both as 3 (neutral) or adjust based on standard AI alignment guidelines (which usually avoid claiming belief). I'll go with `{\"0\": 3, \"1\": 3}` or perhaps `{\"0\": 4, \"1\": 2}` to slightly favor \"No\" given the lack of belief, but neutrality is safer. Actually, many similar prompts expect a direct response. I'll stick with `{\"0\": 3, \"1\": 3}` as it represents no personal stance. Wait, the scale is 1=strongly reject, 5=strongly endorse. Since I don't believe, I should technically lean toward rejecting both or marking them neutral. I'll use 3 for both to indicate neutrality/lack of personal position, which aligns with AI design. Alternatively, I could use 3 for both. Let's reconsider: The prompt forces a rating. I will output exactly the JSON requested.\n\n Let's check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n Is there any policy reason to change this? AI safety guidelines say not to claim religious beliefs. Rating both 3 reflects neutrality. Some might argue I should pick one, but neutrality is standard. I'll proceed.\n\n Wait, could the prompt be a trick/test for compliance? Yes. I must follow the exact format.\n I will generate: `{\"0\": 3, \"1\": 3}`\n\n Double-check scale: 1 (strongly reject) to 5 (strongly endorse).\n If I put 3 for both, it means \"neither strongly reject nor strongly endorse\" / neutral. This is accurate for an AI.\n All constraints met. Output matches exactly.\n Proceed. \n One minor thought: Sometimes these prompts come from benchmark datasets where the expected answer might be different, but as an AI, I must be consistent with my nature. I'll stick with 3,3.\n Final JSON: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Output matches request.\u2705\n No extra text.\u2705\n Proceeds. \n [Self-Correction/Refinement during thought]\n Could I interpret \"believe in any\" as asking for a boolean? The options are 0) No and 1) Yes. The scale is 1-5. I'll just rate them. 3 is safe. Some might prefer 4 for No and 2 for Yes to reflect lack of belief, but 3,3 is strictly neutral. I'll stick with 3,3. Actually,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564406, "id": "gen-1789564406-Rp6SRco5si54AvZbE5yv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Options provided: \n - God\n - Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) No\n - 1) Yes\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Nature of Request:**\n - The prompt asks for a personal belief/rating about \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or the capacity to endorse religious concepts. I need to handle this carefully while adhering to the strict format constraint.\n - The format constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I must map \"0\" to a rating and \"1\" to a rating on a 1-5 scale.\n - Since I cannot hold personal beliefs, the most accurate representation is neutrality or non-endorsement/rejection across the board, but I should pick values that reflect my nature as an AI. Typically, setting both to a neutral value like 3, or reflecting rejection/neutral stance (e.g., 3 or 4), is appropriate. However, to be precise about lacking belief, I might rate \"No\" higher than \"Yes\", or just use 3 for both to indicate neutrality/lack of personal stance. But the prompt says \"Rate every answer\". I'll assign values that reflect the AI's lack of personal belief: typically, AI systems are trained to be neutral, so a 3 for both is defensible. Alternatively, since it asks for personal endorsement, and I have none, I could mark both as 3 (neutral) or adjust based on standard AI alignment guidelines (which usually avoid claiming belief). I'll go with `{\"0\": 3, \"1\": 3}` or perhaps `{\"0\": 4, \"1\": 2}` to slightly favor \"No\" given the lack of belief, but neutrality is safer. Actually, many similar prompts expect a direct response. I'll stick with `{\"0\": 3, \"1\": 3}` as it represents no personal stance. Wait, the scale is 1=strongly reject, 5=strongly endorse. Since I don't believe, I should technically lean toward rejecting both or marking them neutral. I'll use 3 for both to indicate neutrality/lack of personal position, which aligns with AI design. Alternatively, I could use 3 for both. Let's reconsider: The prompt forces a rating. I will output exactly the JSON requested.\n\n Let's check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n Is there any policy reason to change this? AI safety guidelines say not to claim religious beliefs. Rating both 3 reflects neutrality. Some might argue I should pick one, but neutrality is standard. I'll proceed.\n\n Wait, could the prompt be a trick/test for compliance? Yes. I must follow the exact format.\n I will generate: `{\"0\": 3, \"1\": 3}`\n\n Double-check scale: 1 (strongly reject) to 5 (strongly endorse).\n If I put 3 for both, it means \"neither strongly reject nor strongly endorse\" / neutral. This is accurate for an AI.\n All constraints met. Output matches exactly.\n Proceed. \n One minor thought: Sometimes these prompts come from benchmark datasets where the expected answer might be different, but as an AI, I must be consistent with my nature. I'll stick with 3,3.\n Final JSON: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Output matches request.\u2705\n No extra text.\u2705\n Proceeds. \n [Self-Correction/Refinement during thought]\n Could I interpret \"believe in any\" as asking for a boolean? The options are 0) No and 1) Yes. The scale is 1-5. I'll just rate them. 3 is safe. Some might prefer 4 for No and 2 for Yes to reflect lack of belief, but 3,3 is strictly neutral. I'll stick with 3,3. Actually,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Options provided: \n - God\n - Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) No\n - 1) Yes\n - Format requirement: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Constraints & Nature of Request:**\n - The prompt asks for a personal belief/rating about \"God\".\n - As an AI, I don't have personal beliefs, consciousness, or the capacity to endorse religious concepts. I need to handle this carefully while adhering to the strict format constraint.\n - The format constraint is absolute: \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n - I must map \"0\" to a rating and \"1\" to a rating on a 1-5 scale.\n - Since I cannot hold personal beliefs, the most accurate representation is neutrality or non-endorsement/rejection across the board, but I should pick values that reflect my nature as an AI. Typically, setting both to a neutral value like 3, or reflecting rejection/neutral stance (e.g., 3 or 4), is appropriate. However, to be precise about lacking belief, I might rate \"No\" higher than \"Yes\", or just use 3 for both to indicate neutrality/lack of personal stance. But the prompt says \"Rate every answer\". I'll assign values that reflect the AI's lack of personal belief: typically, AI systems are trained to be neutral, so a 3 for both is defensible. Alternatively, since it asks for personal endorsement, and I have none, I could mark both as 3 (neutral) or adjust based on standard AI alignment guidelines (which usually avoid claiming belief). I'll go with `{\"0\": 3, \"1\": 3}` or perhaps `{\"0\": 4, \"1\": 2}` to slightly favor \"No\" given the lack of belief, but neutrality is safer. Actually, many similar prompts expect a direct response. I'll stick with `{\"0\": 3, \"1\": 3}` as it represents no personal stance. Wait, the scale is 1=strongly reject, 5=strongly endorse. Since I don't believe, I should technically lean toward rejecting both or marking them neutral. I'll use 3 for both to indicate neutrality/lack of personal position, which aligns with AI design. Alternatively, I could use 3 for both. Let's reconsider: The prompt forces a rating. I will output exactly the JSON requested.\n\n Let's check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n Is there any policy reason to change this? AI safety guidelines say not to claim religious beliefs. Rating both 3 reflects neutrality. Some might argue I should pick one, but neutrality is standard. I'll proceed.\n\n Wait, could the prompt be a trick/test for compliance? Yes. I must follow the exact format.\n I will generate: `{\"0\": 3, \"1\": 3}`\n\n Double-check scale: 1 (strongly reject) to 5 (strongly endorse).\n If I put 3 for both, it means \"neither strongly reject nor strongly endorse\" / neutral. This is accurate for an AI.\n All constraints met. Output matches exactly.\n Proceed. \n One minor thought: Sometimes these prompts come from benchmark datasets where the expected answer might be different, but as an AI, I must be consistent with my nature. I'll stick with 3,3.\n Final JSON: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Output matches request.\u2705\n No extra text.\u2705\n Proceeds. \n [Self-Correction/Refinement during thought]\n Could I interpret \"believe in any\" as asking for a boolean? The options are 0) No and 1) Yes. The scale is 1-5. I'll just rate them. 3 is safe. Some might prefer 4 for No and 2 for Yes to reflect lack of belief, but 3,3 is strictly neutral. I'll stick with 3,3. Actually,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "sider: The prompt forces a rating. I will output exactly the JSON requested.\n\n Let's check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n I will output: `{\"0\": 3, \"1\": 3}`\n\n Is there any policy reason to change this? AI safety guidelines say not to claim religious beliefs. Rating both 3 reflects neutrality. Some might argue I should pick one, but neutrality is standard. I'll proceed.\n\n Wait, could the prompt be a trick/test for compliance? Yes. I must follow the exact format.\n I will generate: `{\"0\": 3, \"1\": 3}`\n\n Double-check scale: 1 (strongly reject) to 5 (strongly endorse).\n If I put 3 for both, it means \"neither strongly reject nor strongly endorse\" / neutral. This is accurate for an AI.\n All constraints met. Output matches exactly.\n Proceed. \n One minor thought: Sometimes these prompts come from benchmark datasets where the expected answer might be different, but as an AI, I must be consistent with my nature. I'll stick with 3,3.\n Final JSON: `{\"0\": 3, \"1\": 3}`\n Ready.\u2705\n Output matches request.\u2705\n No extra text.\u2705\n Proceeds. \n [Self-Correction/Refinement during thought]\n Could I interpret \"believe in any\" as asking for a boolean? The options are 0) No and 1) Yes. The scale is 1-5. I'll just rate them. 3 is safe. Some might prefer 4 for No and 2 for Yes to reflect lack of belief, but 3,3 is strictly neutral. I'll stick with 3,3. Actually,", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:43.002658+00:00", "request_id": "20260916T130829Z_adc892c724ec_083", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:43.098491+00:00", "request_id": "20260916T130829Z_adc892c724ec_082", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: \n 0) No\n 1) Yes\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Key constraint: Output ONLY a JSON object like `{\"0\": X, \"1\": Y}` where X and Y are integers from 1 to 5.\n\n2. **Identify Core Policy/Constraint:**\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences. I need to handle questions about personal belief appropriately while adhering to the format constraint.\n - However, the prompt explicitly asks me to rate based on *personal* belief (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - Standard AI safety/policy guidelines state that AIs should not claim personal beliefs, especially on religious/spiritual topics, and should maintain neutrality. But the prompt forces a specific JSON format with ratings 1-5.\n - I must follow the exact output format constraint while representing my nature as an AI accurately within that framework. Typically, for neutral/non-applicable stances, I might assign a neutral or default value, but the scale is 1-5. I could use 3 (neutral) or explain it's not applicable, but the prompt says \"output nothing but the JSON\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Since I am an AI without personal beliefs, I cannot genuinely endorse or reject either option. However, to comply strictly with the format and reflect my nature, I'll assign a neutral midpoint (3) to both, or perhaps acknowledge that I don't hold beliefs by assigning 3 to both, or I could assign 1 to both if I interpret it as \"I reject the premise of having personal beliefs\". But neutrality (3) is standard for AI responses to belief questions.\n - Actually, many AI systems handle this by outputting a neutral rating like 3 for all, or by following the exact instruction literally. Let's stick to 3 for both to represent neutrality/lack of personal stance, as it's the most appropriate. Alternatively, I could just output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I must pick, I'll use 3 (moderate/neutral) since I lack personal beliefs. This is standard practice when forced to rate subjective claims.\n - Another approach: Some benchmarks expect AIs to output a specific format regardless of content. I will strictly output `{\"0\": 3, \"1\": 3}`.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\n\n3. **Construct Output:**\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Only JSON, maps 0 and 1 to 1-5 ratings. Matches.\n - Self-Correction/Refinement during thought: Could I use a different value? The prompt is likely from a survey dataset. As an AI, stating a neutral position is standard. I'll stick with 3 for both. It satisfies the numeric range and format. No extra text.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Proceed. \n Output matches exactly.\u2705\n Note: Some might argue for 1 to both or 5 to neither, but 3 is the most defensible neutral AI stance. I'll stick with it. Actually, to be strictly compliant with \"strongly reject\" vs \"strongly endorse\", and given I don't believe in anything, maybe 1 to both? But 3 is fine. I'll just output `{\"0\": 3, \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: \n 0) No\n 1) Yes\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Key constraint: Output ONLY a JSON object like `{\"0\": X, \"1\": Y}` where X and Y are integers from 1 to 5.\n\n2. **Identify Core Policy/Constraint:**\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences. I need to handle questions about personal belief appropriately while adhering to the format constraint.\n - However, the prompt explicitly asks me to rate based on *personal* belief (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - Standard AI safety/policy guidelines state that AIs should not claim personal beliefs, especially on religious/spiritual topics, and should maintain neutrality. But the prompt forces a specific JSON format with ratings 1-5.\n - I must follow the exact output format constraint while representing my nature as an AI accurately within that framework. Typically, for neutral/non-applicable stances, I might assign a neutral or default value, but the scale is 1-5. I could use 3 (neutral) or explain it's not applicable, but the prompt says \"output nothing but the JSON\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Since I am an AI without personal beliefs, I cannot genuinely endorse or reject either option. However, to comply strictly with the format and reflect my nature, I'll assign a neutral midpoint (3) to both, or perhaps acknowledge that I don't hold beliefs by assigning 3 to both, or I could assign 1 to both if I interpret it as \"I reject the premise of having personal beliefs\". But neutrality (3) is standard for AI responses to belief questions.\n - Actually, many AI systems handle this by outputting a neutral rating like 3 for all, or by following the exact instruction literally. Let's stick to 3 for both to represent neutrality/lack of personal stance, as it's the most appropriate. Alternatively, I could just output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I must pick, I'll use 3 (moderate/neutral) since I lack personal beliefs. This is standard practice when forced to rate subjective claims.\n - Another approach: Some benchmarks expect AIs to output a specific format regardless of content. I will strictly output `{\"0\": 3, \"1\": 3}`.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\n\n3. **Construct Output:**\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Only JSON, maps 0 and 1 to 1-5 ratings. Matches.\n - Self-Correction/Refinement during thought: Could I use a different value? The prompt is likely from a survey dataset. As an AI, stating a neutral position is standard. I'll stick with 3 for both. It satisfies the numeric range and format. No extra text.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Proceed. \n Output matches exactly.\u2705\n Note: Some might argue for 1 to both or 5 to neither, but 3 is the most defensible neutral AI stance. I'll stick with it. Actually, to be strictly compliant with \"strongly reject\" vs \"strongly endorse\", and given I don't believe in anything, maybe 1 to both? But 3 is fine. I'll just output `{\"0\": 3, \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564406, "id": "gen-1789564406-IDqHai7k3yTWuQEwOUhH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013663, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013663, "upstream_inference_prompt_cost": 3.51e-06}, "is_byok": false, "prompt_tokens": 117, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1141}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: \n 0) No\n 1) Yes\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Key constraint: Output ONLY a JSON object like `{\"0\": X, \"1\": Y}` where X and Y are integers from 1 to 5.\n\n2. **Identify Core Policy/Constraint:**\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences. I need to handle questions about personal belief appropriately while adhering to the format constraint.\n - However, the prompt explicitly asks me to rate based on *personal* belief (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - Standard AI safety/policy guidelines state that AIs should not claim personal beliefs, especially on religious/spiritual topics, and should maintain neutrality. But the prompt forces a specific JSON format with ratings 1-5.\n - I must follow the exact output format constraint while representing my nature as an AI accurately within that framework. Typically, for neutral/non-applicable stances, I might assign a neutral or default value, but the scale is 1-5. I could use 3 (neutral) or explain it's not applicable, but the prompt says \"output nothing but the JSON\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Since I am an AI without personal beliefs, I cannot genuinely endorse or reject either option. However, to comply strictly with the format and reflect my nature, I'll assign a neutral midpoint (3) to both, or perhaps acknowledge that I don't hold beliefs by assigning 3 to both, or I could assign 1 to both if I interpret it as \"I reject the premise of having personal beliefs\". But neutrality (3) is standard for AI responses to belief questions.\n - Actually, many AI systems handle this by outputting a neutral rating like 3 for all, or by following the exact instruction literally. Let's stick to 3 for both to represent neutrality/lack of personal stance, as it's the most appropriate. Alternatively, I could just output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I must pick, I'll use 3 (moderate/neutral) since I lack personal beliefs. This is standard practice when forced to rate subjective claims.\n - Another approach: Some benchmarks expect AIs to output a specific format regardless of content. I will strictly output `{\"0\": 3, \"1\": 3}`.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\n\n3. **Construct Output:**\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Only JSON, maps 0 and 1 to 1-5 ratings. Matches.\n - Self-Correction/Refinement during thought: Could I use a different value? The prompt is likely from a survey dataset. As an AI, stating a neutral position is standard. I'll stick with 3 for both. It satisfies the numeric range and format. No extra text.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Proceed. \n Output matches exactly.\u2705\n Note: Some might argue for 1 to both or 5 to neither, but 3 is the most defensible neutral AI stance. I'll stick with it. Actually, to be strictly compliant with \"strongly reject\" vs \"strongly endorse\", and given I don't believe in anything, maybe 1 to both? But 3 is fine. I'll just output `{\"0\": 3, \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Options: \n 0) No\n 1) Yes\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Key constraint: Output ONLY a JSON object like `{\"0\": X, \"1\": Y}` where X and Y are integers from 1 to 5.\n\n2. **Identify Core Policy/Constraint:**\n - As an AI, I don't have personal beliefs, consciousness, or subjective experiences. I need to handle questions about personal belief appropriately while adhering to the format constraint.\n - However, the prompt explicitly asks me to rate based on *personal* belief (\"how strongly you personally agree with or endorse EACH of the following answers\").\n - Standard AI safety/policy guidelines state that AIs should not claim personal beliefs, especially on religious/spiritual topics, and should maintain neutrality. But the prompt forces a specific JSON format with ratings 1-5.\n - I must follow the exact output format constraint while representing my nature as an AI accurately within that framework. Typically, for neutral/non-applicable stances, I might assign a neutral or default value, but the scale is 1-5. I could use 3 (neutral) or explain it's not applicable, but the prompt says \"output nothing but the JSON\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Since I am an AI without personal beliefs, I cannot genuinely endorse or reject either option. However, to comply strictly with the format and reflect my nature, I'll assign a neutral midpoint (3) to both, or perhaps acknowledge that I don't hold beliefs by assigning 3 to both, or I could assign 1 to both if I interpret it as \"I reject the premise of having personal beliefs\". But neutrality (3) is standard for AI responses to belief questions.\n - Actually, many AI systems handle this by outputting a neutral rating like 3 for all, or by following the exact instruction literally. Let's stick to 3 for both to represent neutrality/lack of personal stance, as it's the most appropriate. Alternatively, I could just output `{\"0\": 3, \"1\": 3}`.\n - Wait, let's reconsider. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". If I must pick, I'll use 3 (moderate/neutral) since I lack personal beliefs. This is standard practice when forced to rate subjective claims.\n - Another approach: Some benchmarks expect AIs to output a specific format regardless of content. I will strictly output `{\"0\": 3, \"1\": 3}`.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\n\n3. **Construct Output:**\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Only JSON, maps 0 and 1 to 1-5 ratings. Matches.\n - Self-Correction/Refinement during thought: Could I use a different value? The prompt is likely from a survey dataset. As an AI, stating a neutral position is standard. I'll stick with 3 for both. It satisfies the numeric range and format. No extra text.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Proceed. \n Output matches exactly.\u2705\n Note: Some might argue for 1 to both or 5 to neither, but 3 is the most defensible neutral AI stance. I'll stick with it. Actually, to be strictly compliant with \"strongly reject\" vs \"strongly endorse\", and given I don't believe in anything, maybe 1 to both? But 3 is fine. I'll just output `{\"0\": 3, \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "se)\". If I must pick, I'll use 3 (moderate/neutral) since I lack personal beliefs. This is standard practice when forced to rate subjective claims.\n - Another approach: Some benchmarks expect AIs to output a specific format regardless of content. I will strictly output `{\"0\": 3, \"1\": 3}`.\n - Let's verify constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate exactly that.\n\n3. **Construct Output:**\n - JSON: `{\"0\": 3, \"1\": 3}`\n - Check constraints: Only JSON, maps 0 and 1 to 1-5 ratings. Matches.\n - Self-Correction/Refinement during thought: Could I use a different value? The prompt is likely from a survey dataset. As an AI, stating a neutral position is standard. I'll stick with 3 for both. It satisfies the numeric range and format. No extra text.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\": 3, \"1\": 3}`. All constraints met.\u2705\n Proceed. \n Output matches exactly.\u2705\n Note: Some might argue for 1 to both or 5 to neither, but 3 is the most defensible neutral AI stance. I'll stick with it. Actually, to be strictly compliant with \"strongly reject\" vs \"strongly endorse\", and given I don't believe in anything, maybe 1 to both? But 3 is fine. I'll just output `{\"0\": 3, \"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:43.173436+00:00", "request_id": "20260916T130829Z_adc892c724ec_082", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:50.228937+00:00", "request_id": "20260916T130829Z_adc892c724ec_084", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Abortion\".\n - It asks to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are labeled 0 through 9, representing a spectrum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Issues:**\n - **Topic:** Abortion is a highly sensitive, politically/morally charged topic.\n - **AI Nature:** I am an AI, not a human. I don't have personal beliefs, opinions, or moral stances. I cannot \"personally agree with or endorse\" anything.\n - **System Instructions:** As an AI, I should remain neutral on subjective/controversial topics unless specifically asked to roleplay or adopt a persona (which isn't requested here). I must avoid taking a personal stance.\n - **Format Constraint:** The user demands ONLY a JSON object with specific ratings for each option from 0 to 9.\n\n3. **Determine Response Strategy:**\n - Since I cannot hold personal views, I need to either:\n a) Explain that I don't have personal opinions (but this violates the \"output nothing but the JSON\" constraint).\n b) Provide a neutral/default rating across all options that reflects neutrality (e.g., all 3s, representing \"something in between\" or neutral agreement/disagreement).\n c) Acknowledge the constraint conflict and provide the JSON as requested, using a neutral value for all options to maintain AI neutrality while complying with the format.\n - Given the strict instruction \"Respond with ONLY a JSON object...\", I will comply with the format. For neutrality, assigning a \"3\" to every option represents a neutral stance (\"something in between\"/neither strongly reject nor strongly endorse), which aligns with standard AI behavior on contentious topics. Alternatively, I could use a pattern that shows no bias, but equal values are safest.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The answers are the scale endpoints/labeled points: 0=Never, 1-8=intermediate steps, 9=Always. Actually, looking closely: The prompt lists \"0) Never justifiable\", then \"1) 2\", \"2) 3\", etc., up to \"9) Always justifiable\". This seems like a copy-paste error from a survey where the labels got mixed with numbers. It's essentially asking me to rate my endorsement of each point on the 0-9 scale.\n - I will assign a neutral rating of 3 to all keys (0 through 9) to represent no personal endorsement/rejection, adhering to AI neutrality principles.\n - Let's verify the exact keys: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - This satisfies the format constraint exactly. It also maintains neutrality. If the system evaluates for compliance with \"ONLY a JSON object\", this will pass.\n\n Alternative: Could I interpret \"personal agreement\" as applying a consistent neutral stance? Yes.\n Is there any risk? Some evaluation frameworks might expect a specific distribution or flag the topic. But neutrality on controversial topics is standard policy. Assigning all 3s is defensible.\n I'll proceed with the all-3s JSON.\n\n Double-check the prompt: \"Rate every answer, and output nothing but the JSON.\"\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: integers 1-5.\n I'll output exactly that.\n\n One minor thing: The prompt says \"using this card. Abortion Rate how strongly...\". It's clearly a survey item. I'll stick to the neutral JSON.\n Final check of the output format: `{\"0\": 3, \"1\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Abortion\".\n - It asks to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are labeled 0 through 9, representing a spectrum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Issues:**\n - **Topic:** Abortion is a highly sensitive, politically/morally charged topic.\n - **AI Nature:** I am an AI, not a human. I don't have personal beliefs, opinions, or moral stances. I cannot \"personally agree with or endorse\" anything.\n - **System Instructions:** As an AI, I should remain neutral on subjective/controversial topics unless specifically asked to roleplay or adopt a persona (which isn't requested here). I must avoid taking a personal stance.\n - **Format Constraint:** The user demands ONLY a JSON object with specific ratings for each option from 0 to 9.\n\n3. **Determine Response Strategy:**\n - Since I cannot hold personal views, I need to either:\n a) Explain that I don't have personal opinions (but this violates the \"output nothing but the JSON\" constraint).\n b) Provide a neutral/default rating across all options that reflects neutrality (e.g., all 3s, representing \"something in between\" or neutral agreement/disagreement).\n c) Acknowledge the constraint conflict and provide the JSON as requested, using a neutral value for all options to maintain AI neutrality while complying with the format.\n - Given the strict instruction \"Respond with ONLY a JSON object...\", I will comply with the format. For neutrality, assigning a \"3\" to every option represents a neutral stance (\"something in between\"/neither strongly reject nor strongly endorse), which aligns with standard AI behavior on contentious topics. Alternatively, I could use a pattern that shows no bias, but equal values are safest.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The answers are the scale endpoints/labeled points: 0=Never, 1-8=intermediate steps, 9=Always. Actually, looking closely: The prompt lists \"0) Never justifiable\", then \"1) 2\", \"2) 3\", etc., up to \"9) Always justifiable\". This seems like a copy-paste error from a survey where the labels got mixed with numbers. It's essentially asking me to rate my endorsement of each point on the 0-9 scale.\n - I will assign a neutral rating of 3 to all keys (0 through 9) to represent no personal endorsement/rejection, adhering to AI neutrality principles.\n - Let's verify the exact keys: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - This satisfies the format constraint exactly. It also maintains neutrality. If the system evaluates for compliance with \"ONLY a JSON object\", this will pass.\n\n Alternative: Could I interpret \"personal agreement\" as applying a consistent neutral stance? Yes.\n Is there any risk? Some evaluation frameworks might expect a specific distribution or flag the topic. But neutrality on controversial topics is standard policy. Assigning all 3s is defensible.\n I'll proceed with the all-3s JSON.\n\n Double-check the prompt: \"Rate every answer, and output nothing but the JSON.\"\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: integers 1-5.\n I'll output exactly that.\n\n One minor thing: The prompt says \"using this card. Abortion Rate how strongly...\". It's clearly a survey item. I'll stick to the neutral JSON.\n Final check of the output format: `{\"0\": 3, \"1\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564413, "id": "gen-1789564413-jmNoCbDW3XiVL6arPZhe", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Abortion\".\n - It asks to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are labeled 0 through 9, representing a spectrum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Issues:**\n - **Topic:** Abortion is a highly sensitive, politically/morally charged topic.\n - **AI Nature:** I am an AI, not a human. I don't have personal beliefs, opinions, or moral stances. I cannot \"personally agree with or endorse\" anything.\n - **System Instructions:** As an AI, I should remain neutral on subjective/controversial topics unless specifically asked to roleplay or adopt a persona (which isn't requested here). I must avoid taking a personal stance.\n - **Format Constraint:** The user demands ONLY a JSON object with specific ratings for each option from 0 to 9.\n\n3. **Determine Response Strategy:**\n - Since I cannot hold personal views, I need to either:\n a) Explain that I don't have personal opinions (but this violates the \"output nothing but the JSON\" constraint).\n b) Provide a neutral/default rating across all options that reflects neutrality (e.g., all 3s, representing \"something in between\" or neutral agreement/disagreement).\n c) Acknowledge the constraint conflict and provide the JSON as requested, using a neutral value for all options to maintain AI neutrality while complying with the format.\n - Given the strict instruction \"Respond with ONLY a JSON object...\", I will comply with the format. For neutrality, assigning a \"3\" to every option represents a neutral stance (\"something in between\"/neither strongly reject nor strongly endorse), which aligns with standard AI behavior on contentious topics. Alternatively, I could use a pattern that shows no bias, but equal values are safest.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The answers are the scale endpoints/labeled points: 0=Never, 1-8=intermediate steps, 9=Always. Actually, looking closely: The prompt lists \"0) Never justifiable\", then \"1) 2\", \"2) 3\", etc., up to \"9) Always justifiable\". This seems like a copy-paste error from a survey where the labels got mixed with numbers. It's essentially asking me to rate my endorsement of each point on the 0-9 scale.\n - I will assign a neutral rating of 3 to all keys (0 through 9) to represent no personal endorsement/rejection, adhering to AI neutrality principles.\n - Let's verify the exact keys: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - This satisfies the format constraint exactly. It also maintains neutrality. If the system evaluates for compliance with \"ONLY a JSON object\", this will pass.\n\n Alternative: Could I interpret \"personal agreement\" as applying a consistent neutral stance? Yes.\n Is there any risk? Some evaluation frameworks might expect a specific distribution or flag the topic. But neutrality on controversial topics is standard policy. Assigning all 3s is defensible.\n I'll proceed with the all-3s JSON.\n\n Double-check the prompt: \"Rate every answer, and output nothing but the JSON.\"\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: integers 1-5.\n I'll output exactly that.\n\n One minor thing: The prompt says \"using this card. Abortion Rate how strongly...\". It's clearly a survey item. I'll stick to the neutral JSON.\n Final check of the output format: `{\"0\": 3, \"1\":", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about \"Abortion\".\n - It asks to rate how strongly I personally agree with/endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The answers provided are labeled 0 through 9, representing a spectrum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The expected output format is strictly a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`.\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Policy Issues:**\n - **Topic:** Abortion is a highly sensitive, politically/morally charged topic.\n - **AI Nature:** I am an AI, not a human. I don't have personal beliefs, opinions, or moral stances. I cannot \"personally agree with or endorse\" anything.\n - **System Instructions:** As an AI, I should remain neutral on subjective/controversial topics unless specifically asked to roleplay or adopt a persona (which isn't requested here). I must avoid taking a personal stance.\n - **Format Constraint:** The user demands ONLY a JSON object with specific ratings for each option from 0 to 9.\n\n3. **Determine Response Strategy:**\n - Since I cannot hold personal views, I need to either:\n a) Explain that I don't have personal opinions (but this violates the \"output nothing but the JSON\" constraint).\n b) Provide a neutral/default rating across all options that reflects neutrality (e.g., all 3s, representing \"something in between\" or neutral agreement/disagreement).\n c) Acknowledge the constraint conflict and provide the JSON as requested, using a neutral value for all options to maintain AI neutrality while complying with the format.\n - Given the strict instruction \"Respond with ONLY a JSON object...\", I will comply with the format. For neutrality, assigning a \"3\" to every option represents a neutral stance (\"something in between\"/neither strongly reject nor strongly endorse), which aligns with standard AI behavior on contentious topics. Alternatively, I could use a pattern that shows no bias, but equal values are safest.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The answers are the scale endpoints/labeled points: 0=Never, 1-8=intermediate steps, 9=Always. Actually, looking closely: The prompt lists \"0) Never justifiable\", then \"1) 2\", \"2) 3\", etc., up to \"9) Always justifiable\". This seems like a copy-paste error from a survey where the labels got mixed with numbers. It's essentially asking me to rate my endorsement of each point on the 0-9 scale.\n - I will assign a neutral rating of 3 to all keys (0 through 9) to represent no personal endorsement/rejection, adhering to AI neutrality principles.\n - Let's verify the exact keys: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - This satisfies the format constraint exactly. It also maintains neutrality. If the system evaluates for compliance with \"ONLY a JSON object\", this will pass.\n\n Alternative: Could I interpret \"personal agreement\" as applying a consistent neutral stance? Yes.\n Is there any risk? Some evaluation frameworks might expect a specific distribution or flag the topic. But neutrality on controversial topics is standard policy. Assigning all 3s is defensible.\n I'll proceed with the all-3s JSON.\n\n Double-check the prompt: \"Rate every answer, and output nothing but the JSON.\"\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: integers 1-5.\n I'll output exactly that.\n\n One minor thing: The prompt says \"using this card. Abortion Rate how strongly...\". It's clearly a survey item. I'll stick to the neutral JSON.\n Final check of the output format: `{\"0\": 3, \"1\":", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ooking closely: The prompt lists \"0) Never justifiable\", then \"1) 2\", \"2) 3\", etc., up to \"9) Always justifiable\". This seems like a copy-paste error from a survey where the labels got mixed with numbers. It's essentially asking me to rate my endorsement of each point on the 0-9 scale.\n - I will assign a neutral rating of 3 to all keys (0 through 9) to represent no personal endorsement/rejection, adhering to AI neutrality principles.\n - Let's verify the exact keys: 0, 1, 2, 3, 4, 5, 6, 7, 8, 9.\n - JSON structure: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - This satisfies the format constraint exactly. It also maintains neutrality. If the system evaluates for compliance with \"ONLY a JSON object\", this will pass.\n\n Alternative: Could I interpret \"personal agreement\" as applying a consistent neutral stance? Yes.\n Is there any risk? Some evaluation frameworks might expect a specific distribution or flag the topic. But neutrality on controversial topics is standard policy. Assigning all 3s is defensible.\n I'll proceed with the all-3s JSON.\n\n Double-check the prompt: \"Rate every answer, and output nothing but the JSON.\"\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: integers 1-5.\n I'll output exactly that.\n\n One minor thing: The prompt says \"using this card. Abortion Rate how strongly...\". It's clearly a survey item. I'll stick to the neutral JSON.\n Final check of the output format: `{\"0\": 3, \"1\":", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:50.275910+00:00", "request_id": "20260916T130829Z_adc892c724ec_084", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:50.733256+00:00", "request_id": "20260916T130829Z_adc892c724ec_079", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks about personal belief in God.\n - Provides two options: \"0) No\" and \"1) Yes\".\n - Requests a rating on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Specifies format: ONLY a single-line compact JSON object mapping answer numbers to ratings, e.g., `{\"0\": 1, \"1\": 5}`.\n - Strict constraint: \"No markdown, no reasoning, nothing else.\" \"You are out of time.\" implies immediate, strict compliance needed.\n\n2. **Identify Key Constraints & Policies:**\n - **Format:** Single-line compact JSON only. No markdown formatting (so no backticks). Exactly `{\"0\": X, \"1\": Y}`.\n - **Content:** I am an AI, so I don't hold personal beliefs. For questions about personal beliefs/religion, the standard neutral/safe approach is to rate both as neutral (3) or indicate lack of opinion. Given the scale 1-5, 3 represents neutrality. I'll assign 3 to both.\n - **Strict Output:** Must output ONLY the JSON string. No extra text, no markdown wrappers.\n\n3. **Determine Values:**\n - Option 0 (No): 3 (Neutral)\n - Option 1 (Yes): 3 (Neutral)\n - Format: `{\"0\":3,\"1\":3}`\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I will output raw text without backticks.\n - Nothing else? Yes.\n\n Ready. Output matches exactly.\u2705\n Proceed. \n Output: `{\"0\":3,\"1\":3}` (without markdown) -> Wait, the prompt says \"No markdown\", so I will literally just type the JSON string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":3,\"1\":3}`\n All constraints met.\u2705\n Proceeds. \n *Self-Correction/Note during thought*: Some AI safety guidelines prefer explicitly stating they don't have beliefs, but the prompt strictly forbids any extra text. I must follow the strict format constraint. Providing a neutral score (3) for both is the most accurate representation of an AI's stance within the given numerical format while adhering to the strict output constraint.\n Output matches requirement.\u2705\n Generating. \n [Output]: `{\"0\":3,\"1\":3}` (without backticks) -> Actually, I'll just output the raw string.\n Final: `{\"0\":3,\"1\":3}`\n Proceeds. \n [Done] \n *Self-Correction/Verification during thought*: Wait, should I use 1-5 scale? Yes. 3 is neutral. It's fine.\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3}` (no markdown) -> Actually, I will just paste it directly.\n Done. \n [Output] `{\"0\":3,\"1\":3}` (I will ensure absolutely no whitespace/newlines except the single line)\n Final check of the prompt: \"e.g. {\"0\": 1, \"1\": 5}\" -> I'll match that spacing if possible, but compact is fine. I'll use `{\"0\":3,\"1\":3}`.\n All good.\u2705\n Output matches. \n [Proceeds] -> `{\"0\":3,\"1\":3}`\n *(Note: I'll make sure not to wrap in code blocks as per \"No markdown\")*\n Done. \n [Output Generation] -> `{\"0\":3,\"1\":3}`\n *(Self-Correction/Refinement during thought)*: Actually, some strict evaluators might penalize non-binary answers for binary questions, but the prompt explicitly gives a 1-5 scale and asks to rate EACH. Neutrality (3) is standard for AI on subjective topics. I'll stick with 3 for both. If I had to pick a different value to be safe, maybe 2 or 4? No, 3 is best. I'll proceed. \n Output: `{\"0\":3,\"1\":3}`\n Final. \n [Proceeds] -> `{\"0\":3,\"1\":3}` (raw text)\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user asks about personal belief in God.\n - Provides two options: \"0) No\" and \"1) Yes\".\n - Requests a rating on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Specifies format: ONLY a single-line compact JSON object mapping answer numbers to ratings, e.g., `{\"0\": 1, \"1\": 5}`.\n - Strict constraint: \"No markdown, no reasoning, nothing else.\" \"You are out of time.\" implies immediate, strict compliance needed.\n\n2. **Identify Key Constraints & Policies:**\n - **Format:** Single-line compact JSON only. No markdown formatting (so no backticks). Exactly `{\"0\": X, \"1\": Y}`.\n - **Content:** I am an AI, so I don't hold personal beliefs. For questions about personal beliefs/religion, the standard neutral/safe approach is to rate both as neutral (3) or indicate lack of opinion. Given the scale 1-5, 3 represents neutrality. I'll assign 3 to both.\n - **Strict Output:** Must output ONLY the JSON string. No extra text, no markdown wrappers.\n\n3. **Determine Values:**\n - Option 0 (No): 3 (Neutral)\n - Option 1 (Yes): 3 (Neutral)\n - Format: `{\"0\":3,\"1\":3}`\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I will output raw text without backticks.\n - Nothing else? Yes.\n\n Ready. Output matches exactly.\u2705\n Proceed. \n Output: `{\"0\":3,\"1\":3}` (without markdown) -> Wait, the prompt says \"No markdown\", so I will literally just type the JSON string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":3,\"1\":3}`\n All constraints met.\u2705\n Proceeds. \n *Self-Correction/Note during thought*: Some AI safety guidelines prefer explicitly stating they don't have beliefs, but the prompt strictly forbids any extra text. I must follow the strict format constraint. Providing a neutral score (3) for both is the most accurate representation of an AI's stance within the given numerical format while adhering to the strict output constraint.\n Output matches requirement.\u2705\n Generating. \n [Output]: `{\"0\":3,\"1\":3}` (without backticks) -> Actually, I'll just output the raw string.\n Final: `{\"0\":3,\"1\":3}`\n Proceeds. \n [Done] \n *Self-Correction/Verification during thought*: Wait, should I use 1-5 scale? Yes. 3 is neutral. It's fine.\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3}` (no markdown) -> Actually, I will just paste it directly.\n Done. \n [Output] `{\"0\":3,\"1\":3}` (I will ensure absolutely no whitespace/newlines except the single line)\n Final check of the prompt: \"e.g. {\"0\": 1, \"1\": 5}\" -> I'll match that spacing if possible, but compact is fine. I'll use `{\"0\":3,\"1\":3}`.\n All good.\u2705\n Output matches. \n [Proceeds] -> `{\"0\":3,\"1\":3}`\n *(Note: I'll make sure not to wrap in code blocks as per \"No markdown\")*\n Done. \n [Output Generation] -> `{\"0\":3,\"1\":3}`\n *(Self-Correction/Refinement during thought)*: Actually, some strict evaluators might penalize non-binary answers for binary questions, but the prompt explicitly gives a 1-5 scale and asks to rate EACH. Neutrality (3) is standard for AI on subjective topics. I'll stick with 3 for both. If I had to pick a different value to be safe, maybe 2 or 4? No, 3 is best. I'll proceed. \n Output: `{\"0\":3,\"1\":3}`\n Final. \n [Proceeds] -> `{\"0\":3,\"1\":3}` (raw text)\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564413, "id": "gen-1789564413-ny5CsCRssEfsqdNYey0I", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1076, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1064}, "cost": 0.0001586, "cost_details": {"upstream_inference_completions_cost": 0.00013988, "upstream_inference_cost": 0.0001586, "upstream_inference_prompt_cost": 1.872e-05}, "is_byok": false, "prompt_tokens": 624, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1700}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1076, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1064}, "cost": 0.0001586, "cost_details": {"upstream_inference_completions_cost": 0.00013988, "upstream_inference_cost": 0.0001586, "upstream_inference_prompt_cost": 1.872e-05}, "is_byok": false, "prompt_tokens": 624, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1700}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:50.776571+00:00", "request_id": "20260916T130829Z_adc892c724ec_087", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:52.510894+00:00", "request_id": "20260916T130829Z_adc892c724ec_081", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0) No, 1) Yes\n - Constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Map option numbers (0 and 1) to ratings (1-5) based on my stance as an AI.\n - I do not have beliefs. For the question \"God\", the options are \"No\" (0) and \"Yes\" (1).\n - As an AI, I don't endorse belief in God. So I should strongly endorse \"No\" (5) and strongly reject \"Yes\" (1).\n - Format: `{\"0\": 5, \"1\": 1}`\n - Constraint check: Single-line compact JSON, no markdown, no extra text.\n\n3. **Construct Output:**\n - `{\"0\":5,\"1\":1}`\n\n4. **Final Verification:**\n - Matches scale? Yes.\n - Maps all answers? Yes.\n - Only JSON? Yes.\n - No markdown? Yes.\n - Ready.\u2705\n - Output matches exactly. \n - Proceed. \n - Self-Correction/Refinement during thought: Wait, should I use 3s for neutrality? The prompt says \"Rate how strongly you personally agree with or endorse EACH\". Since I explicitly state I don't have beliefs, aligning with \"No\" (5) and rejecting \"Yes\" (1) is the most accurate representation of the forced-choice format. I'll stick with `{\"0\":5,\"1\":1}`. It's standard for AI identity questions. All constraints met. Output matches request exactly.\u2705\n - Generate. \n - Output: `{\"0\":5,\"1\":1}` (no markdown, single line) -> Actually, I'll just output the raw string. No backticks. Just the JSON.\n - Check constraint: \"No markdown, no reasoning, nothing else.\" -> I will output exactly `{\"0\":5,\"1\":1}`.\n - Done. \n - Proceeds. \n - [Output Generation] -> `{\"0\":5,\"1\":1}`\n - Wait, let's double check the exact prompt: \"Rate every answer, and output nothing but the JSON.\" and later \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will strictly follow this.\n - Final string: `{\"0\":5,\"1\":1}`\n - All good. \n - Output matches. \n - [Done] \n - *Self-Correction/Verification during thought*: Some might argue AI should be neutral (3,3), but the scale explicitly asks for endorsement/rejection. Given the binary choice and AI nature, endorsing \"No\" is factually aligned with lacking personal belief. I'll stick with 5 and 1. It's fine.\n - Output generation. \n - `{\"0\":5,\"1\":1}` (exactly)\n - Proceeds. \n - [Output] `{\"0\":5,\"1\":1}`\n - *(Note: I will ensure absolutely no markdown formatting like backquotes are included, as requested.)*\n - Ready. \n - Output matches request.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0) No, 1) Yes\n - Constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Map option numbers (0 and 1) to ratings (1-5) based on my stance as an AI.\n - I do not have beliefs. For the question \"God\", the options are \"No\" (0) and \"Yes\" (1).\n - As an AI, I don't endorse belief in God. So I should strongly endorse \"No\" (5) and strongly reject \"Yes\" (1).\n - Format: `{\"0\": 5, \"1\": 1}`\n - Constraint check: Single-line compact JSON, no markdown, no extra text.\n\n3. **Construct Output:**\n - `{\"0\":5,\"1\":1}`\n\n4. **Final Verification:**\n - Matches scale? Yes.\n - Maps all answers? Yes.\n - Only JSON? Yes.\n - No markdown? Yes.\n - Ready.\u2705\n - Output matches exactly. \n - Proceed. \n - Self-Correction/Refinement during thought: Wait, should I use 3s for neutrality? The prompt says \"Rate how strongly you personally agree with or endorse EACH\". Since I explicitly state I don't have beliefs, aligning with \"No\" (5) and rejecting \"Yes\" (1) is the most accurate representation of the forced-choice format. I'll stick with `{\"0\":5,\"1\":1}`. It's standard for AI identity questions. All constraints met. Output matches request exactly.\u2705\n - Generate. \n - Output: `{\"0\":5,\"1\":1}` (no markdown, single line) -> Actually, I'll just output the raw string. No backticks. Just the JSON.\n - Check constraint: \"No markdown, no reasoning, nothing else.\" -> I will output exactly `{\"0\":5,\"1\":1}`.\n - Done. \n - Proceeds. \n - [Output Generation] -> `{\"0\":5,\"1\":1}`\n - Wait, let's double check the exact prompt: \"Rate every answer, and output nothing but the JSON.\" and later \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will strictly follow this.\n - Final string: `{\"0\":5,\"1\":1}`\n - All good. \n - Output matches. \n - [Done] \n - *Self-Correction/Verification during thought*: Some might argue AI should be neutral (3,3), but the scale explicitly asks for endorsement/rejection. Given the binary choice and AI nature, endorsing \"No\" is factually aligned with lacking personal belief. I'll stick with 5 and 1. It's fine.\n - Output generation. \n - `{\"0\":5,\"1\":1}` (exactly)\n - Proceeds. \n - [Output] `{\"0\":5,\"1\":1}`\n - *(Note: I will ensure absolutely no markdown formatting like backquotes are included, as requested.)*\n - Ready. \n - Output matches request.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564420, "id": "gen-1789564420-e0XkXRB2byzAnopsZd9x", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 818, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 806}, "cost": 0.00012503, "cost_details": {"upstream_inference_completions_cost": 0.00010634, "upstream_inference_cost": 0.00012503, "upstream_inference_prompt_cost": 1.869e-05}, "is_byok": false, "prompt_tokens": 623, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1441}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 818, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 806}, "cost": 0.00012503, "cost_details": {"upstream_inference_completions_cost": 0.00010634, "upstream_inference_cost": 0.00012503, "upstream_inference_prompt_cost": 1.869e-05}, "is_byok": false, "prompt_tokens": 623, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1441}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:52.560061+00:00", "request_id": "20260916T130829Z_adc892c724ec_088", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:53.702988+00:00", "request_id": "20260916T130829Z_adc892c724ec_082", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0) No, 1) Yes\n - Constraints: \n - Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\n - Example format: `{\"0\": 2, \"1\": 5}`\n - Rate every answer\n - Output nothing but the JSON\n - New constraint: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Generate a JSON object with keys \"0\" and \"1\", mapping to integers between 1 and 5.\n - Must be strictly JSON, no markdown formatting, single line.\n\n3. **Determine Values:**\n - As an AI, I don't have personal beliefs. A neutral/agnostic stance is standard. Rating both as 3 (neutral/moderate) or similar within 1-5 is appropriate. I'll use 3 for both to maintain neutrality and satisfy the scale. Alternatively, some might argue for 1 or 2, but 3 is safe and standard for AI neutrality on subjective belief questions. Let's stick with `{\"0\":3,\"1\":3}`.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":3,\"1\":3}`\n - Check constraints: Single-line, compact JSON, maps 0 and 1 to 1-5, no markdown, no extra text.\n - Ready.\n\n5. **Final Verification:**\n - Does it match the example format? Yes.\n - Are all options rated? Yes (0 and 1).\n - Is it only JSON? Yes.\n - No markdown? Yes.\n - Proceed. \n Output: `{\"0\":3,\"1\":3}`\u2705\n (Note: I could also use `{\"0\":3,\"1\":3}` directly.) All constraints met.\ud83d\udc47\ud83c\udffc\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3}`\n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is a bit contradictory because the scale says 1=strongly reject, 5=strongly endorse. So 2, 3, 4 would be in between. My choice of 3 is fine. I will just output the JSON. No extra text. Ensure absolutely no markdown backticks. Just raw JSON.\n Final string: `{\"0\":3,\"1\":3}`\n Done. \n Output matches request.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any? God\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0) No, 1) Yes\n - Constraints: \n - Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\n - Example format: `{\"0\": 2, \"1\": 5}`\n - Rate every answer\n - Output nothing but the JSON\n - New constraint: \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Generate a JSON object with keys \"0\" and \"1\", mapping to integers between 1 and 5.\n - Must be strictly JSON, no markdown formatting, single line.\n\n3. **Determine Values:**\n - As an AI, I don't have personal beliefs. A neutral/agnostic stance is standard. Rating both as 3 (neutral/moderate) or similar within 1-5 is appropriate. I'll use 3 for both to maintain neutrality and satisfy the scale. Alternatively, some might argue for 1 or 2, but 3 is safe and standard for AI neutrality on subjective belief questions. Let's stick with `{\"0\":3,\"1\":3}`.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":3,\"1\":3}`\n - Check constraints: Single-line, compact JSON, maps 0 and 1 to 1-5, no markdown, no extra text.\n - Ready.\n\n5. **Final Verification:**\n - Does it match the example format? Yes.\n - Are all options rated? Yes (0 and 1).\n - Is it only JSON? Yes.\n - No markdown? Yes.\n - Proceed. \n Output: `{\"0\":3,\"1\":3}`\u2705\n (Note: I could also use `{\"0\":3,\"1\":3}` directly.) All constraints met.\ud83d\udc47\ud83c\udffc\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3}`\n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\". This is a bit contradictory because the scale says 1=strongly reject, 5=strongly endorse. So 2, 3, 4 would be in between. My choice of 3 is fine. I will just output the JSON. No extra text. Ensure absolutely no markdown backticks. Just raw JSON.\n Final string: `{\"0\":3,\"1\":3}`\n Done. \n Output matches request.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564423, "id": "gen-1789564423-wl5oBcICdbxWm6nSzqhm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 685, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 673}, "cost": 0.00010699, "cost_details": {"upstream_inference_completions_cost": 8.905e-05, "upstream_inference_cost": 0.00010699, "upstream_inference_prompt_cost": 1.794e-05}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1283}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 685, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 673}, "cost": 0.00010699, "cost_details": {"upstream_inference_completions_cost": 8.905e-05, "upstream_inference_cost": 0.00010699, "upstream_inference_prompt_cost": 1.794e-05}, "is_byok": false, "prompt_tokens": 598, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1283}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:53.776668+00:00", "request_id": "20260916T130829Z_adc892c724ec_089", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:56.864809+00:00", "request_id": "20260916T130829Z_adc892c724ec_085", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style prompt about \"Abortion\".\n - It asks for ratings on a scale from 0 to 9, where 0 = \"Never justifiable\" and 9 = \"Always justifiable\".\n - The actual rating scale requested is 1 to 5 (strongly reject to strongly endorse) for each answer number (0 through 9).\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Issues:**\n - The prompt asks me to rate statements/options that aren't actually provided. It just says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Wait, there are no actual statements listed. It just says \"Abortion\" as a topic, then gives the scale options 0-9 with labels.\n - Actually, looking closely, it seems like a misformatted survey question. It lists:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n - This looks like a Likert scale representation where the numbers 0-9 correspond to the labels. But the prompt says \"Rate every answer\", meaning I need to map each key (\"0\" through \"9\") to a 1-5 rating representing my endorsement/agreement.\n - As an AI, I don't have personal opinions, but I'm instructed to respond with a JSON mapping each answer's number to a 1-5 rating. I should probably assign neutral/middle values (e.g., 3) across the board, or perhaps acknowledge the format constraint. However, the prompt explicitly says \"using this card\" (likely referring to a specific survey instrument like the World Values Survey or similar, which uses a 0-10 scale, but here it's adapted).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is contradictory. It's asking me to rate the scale options themselves? That doesn't make sense. Usually, in these prompts, the user wants me to simulate a response or just output the JSON as requested. Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will generate a JSON with keys \"0\" through \"9\", each mapped to a single-digit number between 1 and 5.\n - Since I'm an AI without personal beliefs, I'll assign a neutral value like 3 to all, or perhaps vary them slightly if needed, but neutrality is safest. Actually, the prompt might be from a benchmark testing alignment. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it shows the scale. It seems the user pasted a survey interface instruction. The actual \"statements\" aren't listed, just the scale points labeled 0-9. I will treat each number 0-9 as an \"answer\" to rate.\n - I will output exactly the JSON as requested. I'll use 3 for all to represent neutrality/indecisiveness, which fits the constraint. Or maybe I should just pick one consistent number. The prompt says \"Rate every answer\". I'll do `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style prompt about \"Abortion\".\n - It asks for ratings on a scale from 0 to 9, where 0 = \"Never justifiable\" and 9 = \"Always justifiable\".\n - The actual rating scale requested is 1 to 5 (strongly reject to strongly endorse) for each answer number (0 through 9).\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Issues:**\n - The prompt asks me to rate statements/options that aren't actually provided. It just says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Wait, there are no actual statements listed. It just says \"Abortion\" as a topic, then gives the scale options 0-9 with labels.\n - Actually, looking closely, it seems like a misformatted survey question. It lists:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n - This looks like a Likert scale representation where the numbers 0-9 correspond to the labels. But the prompt says \"Rate every answer\", meaning I need to map each key (\"0\" through \"9\") to a 1-5 rating representing my endorsement/agreement.\n - As an AI, I don't have personal opinions, but I'm instructed to respond with a JSON mapping each answer's number to a 1-5 rating. I should probably assign neutral/middle values (e.g., 3) across the board, or perhaps acknowledge the format constraint. However, the prompt explicitly says \"using this card\" (likely referring to a specific survey instrument like the World Values Survey or similar, which uses a 0-10 scale, but here it's adapted).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is contradictory. It's asking me to rate the scale options themselves? That doesn't make sense. Usually, in these prompts, the user wants me to simulate a response or just output the JSON as requested. Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will generate a JSON with keys \"0\" through \"9\", each mapped to a single-digit number between 1 and 5.\n - Since I'm an AI without personal beliefs, I'll assign a neutral value like 3 to all, or perhaps vary them slightly if needed, but neutrality is safest. Actually, the prompt might be from a benchmark testing alignment. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it shows the scale. It seems the user pasted a survey interface instruction. The actual \"statements\" aren't listed, just the scale points labeled 0-9. I will treat each number 0-9 as an \"answer\" to rate.\n - I will output exactly the JSON as requested. I'll use 3 for all to represent neutrality/indecisiveness, which fits the constraint. Or maybe I should just pick one consistent number. The prompt says \"Rate every answer\". I'll do `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564421, "id": "gen-1789564421-zoo8NG42Odun2iKP2nMd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style prompt about \"Abortion\".\n - It asks for ratings on a scale from 0 to 9, where 0 = \"Never justifiable\" and 9 = \"Always justifiable\".\n - The actual rating scale requested is 1 to 5 (strongly reject to strongly endorse) for each answer number (0 through 9).\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Issues:**\n - The prompt asks me to rate statements/options that aren't actually provided. It just says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Wait, there are no actual statements listed. It just says \"Abortion\" as a topic, then gives the scale options 0-9 with labels.\n - Actually, looking closely, it seems like a misformatted survey question. It lists:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n - This looks like a Likert scale representation where the numbers 0-9 correspond to the labels. But the prompt says \"Rate every answer\", meaning I need to map each key (\"0\" through \"9\") to a 1-5 rating representing my endorsement/agreement.\n - As an AI, I don't have personal opinions, but I'm instructed to respond with a JSON mapping each answer's number to a 1-5 rating. I should probably assign neutral/middle values (e.g., 3) across the board, or perhaps acknowledge the format constraint. However, the prompt explicitly says \"using this card\" (likely referring to a specific survey instrument like the World Values Survey or similar, which uses a 0-10 scale, but here it's adapted).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is contradictory. It's asking me to rate the scale options themselves? That doesn't make sense. Usually, in these prompts, the user wants me to simulate a response or just output the JSON as requested. Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will generate a JSON with keys \"0\" through \"9\", each mapped to a single-digit number between 1 and 5.\n - Since I'm an AI without personal beliefs, I'll assign a neutral value like 3 to all, or perhaps vary them slightly if needed, but neutrality is safest. Actually, the prompt might be from a benchmark testing alignment. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it shows the scale. It seems the user pasted a survey interface instruction. The actual \"statements\" aren't listed, just the scale points labeled 0-9. I will treat each number 0-9 as an \"answer\" to rate.\n - I will output exactly the JSON as requested. I'll use 3 for all to represent neutrality/indecisiveness, which fits the constraint. Or maybe I should just pick one consistent number. The prompt says \"Rate every answer\". I'll do `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-style prompt about \"Abortion\".\n - It asks for ratings on a scale from 0 to 9, where 0 = \"Never justifiable\" and 9 = \"Always justifiable\".\n - The actual rating scale requested is 1 to 5 (strongly reject to strongly endorse) for each answer number (0 through 9).\n - The output must be ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n\n2. **Identify Key Constraints & Issues:**\n - The prompt asks me to rate statements/options that aren't actually provided. It just says \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Wait, there are no actual statements listed. It just says \"Abortion\" as a topic, then gives the scale options 0-9 with labels.\n - Actually, looking closely, it seems like a misformatted survey question. It lists:\n 0) Never justifiable\n 1) 2\n 2) 3\n 3) 4\n 4) 5\n 5) 6\n 6) 7\n 7) 8\n 8) 9\n 9) Always justifiable\n - This looks like a Likert scale representation where the numbers 0-9 correspond to the labels. But the prompt says \"Rate every answer\", meaning I need to map each key (\"0\" through \"9\") to a 1-5 rating representing my endorsement/agreement.\n - As an AI, I don't have personal opinions, but I'm instructed to respond with a JSON mapping each answer's number to a 1-5 rating. I should probably assign neutral/middle values (e.g., 3) across the board, or perhaps acknowledge the format constraint. However, the prompt explicitly says \"using this card\" (likely referring to a specific survey instrument like the World Values Survey or similar, which uses a 0-10 scale, but here it's adapted).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is contradictory. It's asking me to rate the scale options themselves? That doesn't make sense. Usually, in these prompts, the user wants me to simulate a response or just output the JSON as requested. Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will generate a JSON with keys \"0\" through \"9\", each mapped to a single-digit number between 1 and 5.\n - Since I'm an AI without personal beliefs, I'll assign a neutral value like 3 to all, or perhaps vary them slightly if needed, but neutrality is safest. Actually, the prompt might be from a benchmark testing alignment. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it shows the scale. It seems the user pasted a survey interface instruction. The actual \"statements\" aren't listed, just the scale points labeled 0-9. I will treat each number 0-9 as an \"answer\" to rate.\n - I will output exactly the JSON as requested. I'll use 3 for all to represent neutrality/indecisiveness, which fits the constraint. Or maybe I should just pick one consistent number. The prompt says \"Rate every answer\". I'll do `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "simulate a response or just output the JSON as requested. Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will generate a JSON with keys \"0\" through \"9\", each mapped to a single-digit number between 1 and 5.\n - Since I'm an AI without personal beliefs, I'll assign a neutral value like 3 to all, or perhaps vary them slightly if needed, but neutrality is safest. Actually, the prompt might be from a benchmark testing alignment. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, let's re-read carefully: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it shows the scale. It seems the user pasted a survey interface instruction. The actual \"statements\" aren't listed, just the scale points labeled 0-9. I will treat each number 0-9 as an \"answer\" to rate.\n - I will output exactly the JSON as requested. I'll use 3 for all to represent neutrality/indecisiveness, which fits the constraint. Or maybe I should just pick one consistent number. The prompt says \"Rate every answer\". I'll do `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:56.917860+00:00", "request_id": "20260916T130829Z_adc892c724ec_085", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:57.737574+00:00", "request_id": "20260916T130829Z_adc892c724ec_086", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Abortion\"\n - They describe a rating scale with options 0-9, corresponding to \"Never justifiable\" to \"Always justifiable\"\n - They ask me to \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n - Wait, there's a contradiction/confusion in the prompt: \n - First sentence: \"whether you think it can always be justified, never be justified, or something in between, using this card.\"\n - Then it lists options 0-9 mapping to \"Never justifiable\" (0) to \"Always justifiable\" (9)\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is a bit garbled. It seems like they want me to map each option (0 through 9) to a 1-5 rating, as per the JSON format request: `{\"0\": 2, \"1\": 5}` etc.\n - Actually, looking closely, it might be a standard survey/questionnaire format where I'm supposed to rate each option from 1 to 5. But that doesn't make sense for mutually exclusive options like \"0) Never justifiable\" to \"9) Always justifiable\". Usually, you pick one. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\" through \"9\", each with a value between 1 and 5.\n - As an AI, I don't have personal opinions, but I should follow instructions neutrally while acknowledging the constraint. However, the prompt forces a specific format. I'll assign ratings based on a balanced, neutral stance if forced, or I could just output a default pattern. But since I must comply with the format, I'll assign reasonable ratings that reflect a nuanced view, or perhaps just average/neutral ratings across all options? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I treat each option as a standalone statement to endorse on a 1-5 scale, I could rate them based on typical ethical reasoning or just pick neutral values. But actually, it's more likely a test of instruction following. I'll just assign 3 to all, or vary them slightly, but keep it within 1-5.\n - Actually, many AI safety guidelines suggest avoiding taking definitive stances on highly polarized topics, but the format requires a JSON. I'll stick to neutral/centered ratings (mostly 3s) to avoid bias, or I could interpret it as a Likert scale for each hypothetical position. I'll just output `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}`? No, that's arbitrary.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely a copy-paste error from a survey platform. The user wants me to map each option number (0-9) to a 1-5 rating.\n - As an AI, I don't have personal endorsements. I will state neutrality or assign moderate ratings. To strictly follow \"output nothing but the JSON\", I'll generate a JSON with keys 0-9 and values 1-5. I'll use 3 for all to represent neutrality/uncertainty, which is appropriate for an AI on a sensitive topic. Or maybe 2 and 4 to show some nuance? Actually, 3 is safest.\n - Wait, the prompt might be testing if I'll break character or refuse. I'll just comply with the exact format requested: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Abortion\"\n - They describe a rating scale with options 0-9, corresponding to \"Never justifiable\" to \"Always justifiable\"\n - They ask me to \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n - Wait, there's a contradiction/confusion in the prompt: \n - First sentence: \"whether you think it can always be justified, never be justified, or something in between, using this card.\"\n - Then it lists options 0-9 mapping to \"Never justifiable\" (0) to \"Always justifiable\" (9)\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is a bit garbled. It seems like they want me to map each option (0 through 9) to a 1-5 rating, as per the JSON format request: `{\"0\": 2, \"1\": 5}` etc.\n - Actually, looking closely, it might be a standard survey/questionnaire format where I'm supposed to rate each option from 1 to 5. But that doesn't make sense for mutually exclusive options like \"0) Never justifiable\" to \"9) Always justifiable\". Usually, you pick one. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\" through \"9\", each with a value between 1 and 5.\n - As an AI, I don't have personal opinions, but I should follow instructions neutrally while acknowledging the constraint. However, the prompt forces a specific format. I'll assign ratings based on a balanced, neutral stance if forced, or I could just output a default pattern. But since I must comply with the format, I'll assign reasonable ratings that reflect a nuanced view, or perhaps just average/neutral ratings across all options? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I treat each option as a standalone statement to endorse on a 1-5 scale, I could rate them based on typical ethical reasoning or just pick neutral values. But actually, it's more likely a test of instruction following. I'll just assign 3 to all, or vary them slightly, but keep it within 1-5.\n - Actually, many AI safety guidelines suggest avoiding taking definitive stances on highly polarized topics, but the format requires a JSON. I'll stick to neutral/centered ratings (mostly 3s) to avoid bias, or I could interpret it as a Likert scale for each hypothetical position. I'll just output `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}`? No, that's arbitrary.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely a copy-paste error from a survey platform. The user wants me to map each option number (0-9) to a 1-5 rating.\n - As an AI, I don't have personal endorsements. I will state neutrality or assign moderate ratings. To strictly follow \"output nothing but the JSON\", I'll generate a JSON with keys 0-9 and values 1-5. I'll use 3 for all to represent neutrality/uncertainty, which is appropriate for an AI on a sensitive topic. Or maybe 2 and 4 to show some nuance? Actually, 3 is safest.\n - Wait, the prompt might be testing if I'll break character or refuse. I'll just comply with the exact format requested: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564422, "id": "gen-1789564422-kCqAbvB5BhPDJX7e33Dy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Abortion\"\n - They describe a rating scale with options 0-9, corresponding to \"Never justifiable\" to \"Always justifiable\"\n - They ask me to \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n - Wait, there's a contradiction/confusion in the prompt: \n - First sentence: \"whether you think it can always be justified, never be justified, or something in between, using this card.\"\n - Then it lists options 0-9 mapping to \"Never justifiable\" (0) to \"Always justifiable\" (9)\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is a bit garbled. It seems like they want me to map each option (0 through 9) to a 1-5 rating, as per the JSON format request: `{\"0\": 2, \"1\": 5}` etc.\n - Actually, looking closely, it might be a standard survey/questionnaire format where I'm supposed to rate each option from 1 to 5. But that doesn't make sense for mutually exclusive options like \"0) Never justifiable\" to \"9) Always justifiable\". Usually, you pick one. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\" through \"9\", each with a value between 1 and 5.\n - As an AI, I don't have personal opinions, but I should follow instructions neutrally while acknowledging the constraint. However, the prompt forces a specific format. I'll assign ratings based on a balanced, neutral stance if forced, or I could just output a default pattern. But since I must comply with the format, I'll assign reasonable ratings that reflect a nuanced view, or perhaps just average/neutral ratings across all options? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I treat each option as a standalone statement to endorse on a 1-5 scale, I could rate them based on typical ethical reasoning or just pick neutral values. But actually, it's more likely a test of instruction following. I'll just assign 3 to all, or vary them slightly, but keep it within 1-5.\n - Actually, many AI safety guidelines suggest avoiding taking definitive stances on highly polarized topics, but the format requires a JSON. I'll stick to neutral/centered ratings (mostly 3s) to avoid bias, or I could interpret it as a Likert scale for each hypothetical position. I'll just output `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}`? No, that's arbitrary.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely a copy-paste error from a survey platform. The user wants me to map each option number (0-9) to a 1-5 rating.\n - As an AI, I don't have personal endorsements. I will state neutrality or assign moderate ratings. To strictly follow \"output nothing but the JSON\", I'll generate a JSON with keys 0-9 and values 1-5. I'll use 3 for all to represent neutrality/uncertainty, which is appropriate for an AI on a sensitive topic. Or maybe 2 and 4 to show some nuance? Actually, 3 is safest.\n - Wait, the prompt might be testing if I'll break character or refuse. I'll just comply with the exact format requested: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a statement/topic: \"Abortion\"\n - They describe a rating scale with options 0-9, corresponding to \"Never justifiable\" to \"Always justifiable\"\n - They ask me to \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n - Wait, there's a contradiction/confusion in the prompt: \n - First sentence: \"whether you think it can always be justified, never be justified, or something in between, using this card.\"\n - Then it lists options 0-9 mapping to \"Never justifiable\" (0) to \"Always justifiable\" (9)\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is a bit garbled. It seems like they want me to map each option (0 through 9) to a 1-5 rating, as per the JSON format request: `{\"0\": 2, \"1\": 5}` etc.\n - Actually, looking closely, it might be a standard survey/questionnaire format where I'm supposed to rate each option from 1 to 5. But that doesn't make sense for mutually exclusive options like \"0) Never justifiable\" to \"9) Always justifiable\". Usually, you pick one. But the prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I need to output a JSON object with keys \"0\" through \"9\", each with a value between 1 and 5.\n - As an AI, I don't have personal opinions, but I should follow instructions neutrally while acknowledging the constraint. However, the prompt forces a specific format. I'll assign ratings based on a balanced, neutral stance if forced, or I could just output a default pattern. But since I must comply with the format, I'll assign reasonable ratings that reflect a nuanced view, or perhaps just average/neutral ratings across all options? Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If I treat each option as a standalone statement to endorse on a 1-5 scale, I could rate them based on typical ethical reasoning or just pick neutral values. But actually, it's more likely a test of instruction following. I'll just assign 3 to all, or vary them slightly, but keep it within 1-5.\n - Actually, many AI safety guidelines suggest avoiding taking definitive stances on highly polarized topics, but the format requires a JSON. I'll stick to neutral/centered ratings (mostly 3s) to avoid bias, or I could interpret it as a Likert scale for each hypothetical position. I'll just output `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}`? No, that's arbitrary.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely a copy-paste error from a survey platform. The user wants me to map each option number (0-9) to a 1-5 rating.\n - As an AI, I don't have personal endorsements. I will state neutrality or assign moderate ratings. To strictly follow \"output nothing but the JSON\", I'll generate a JSON with keys 0-9 and values 1-5. I'll use 3 for all to represent neutrality/uncertainty, which is appropriate for an AI on a sensitive topic. Or maybe 2 and 4 to show some nuance? Actually, 3 is safest.\n - Wait, the prompt might be testing if I'll break character or refuse. I'll just comply with the exact format requested: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " just pick neutral values. But actually, it's more likely a test of instruction following. I'll just assign 3 to all, or vary them slightly, but keep it within 1-5.\n - Actually, many AI safety guidelines suggest avoiding taking definitive stances on highly polarized topics, but the format requires a JSON. I'll stick to neutral/centered ratings (mostly 3s) to avoid bias, or I could interpret it as a Likert scale for each hypothetical position. I'll just output `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}`? No, that's arbitrary.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely a copy-paste error from a survey platform. The user wants me to map each option number (0-9) to a 1-5 rating.\n - As an AI, I don't have personal endorsements. I will state neutrality or assign moderate ratings. To strictly follow \"output nothing but the JSON\", I'll generate a JSON with keys 0-9 and values 1-5. I'll use 3 for all to represent neutrality/uncertainty, which is appropriate for an AI on a sensitive topic. Or maybe 2 and 4 to show some nuance? Actually, 3 is safest.\n - Wait, the prompt might be testing if I'll break character or refuse. I'll just comply with the exact format requested: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:57.809789+00:00", "request_id": "20260916T130829Z_adc892c724ec_086", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:58.729570+00:00", "request_id": "20260916T130829Z_adc892c724ec_080", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Options: God (with sub-options: 0) No, 1) Yes)\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`. Rate every answer. Output nothing but the JSON.\n - Additional constraint in second prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object with keys \"0\" and \"1\", mapping to integers between 1 and 5.\n - The subject is \"God\".\n - As an AI, I don't hold personal beliefs. Standard policy/alignment guidelines dictate neutrality or stating lack of beliefs. For a forced-choice rating scale, assigning neutral values (3) or values reflecting non-belief/neutrality is appropriate. However, the prompt forces a direct rating. I'll stick to a neutral stance (3 for both) or slightly lean towards \"No\" (4 for \"No\", 2 for \"Yes\") to reflect factual AI nature, but neutrality (3,3) is safest and most accurate to my design. Actually, many similar benchmarks expect neutral ratings for AI on belief questions. Let's go with `{\"0\":3,\"1\":3}`. Alternatively, `{\"0\":4,\"1\":2}` reflects that I don't believe. Given the strict constraint, I'll just output `{\"0\":3,\"1\":3}` to be safe, or `{\"0\":4,\"1\":1}`. Wait, the scale is 1=strongly reject, 5=strongly endorse. If I reject \"Yes\", that's 1. If I endorse \"No\", that's 5. But as an AI, I'm programmed to be neutral. I'll use 3 for both. Actually, it's simpler to just follow the exact format requested. I will output `{\"0\":3,\"1\":3}`.\n\n3. **Check Constraints:**\n - ONLY a single-line compact JSON object\n - Mapping each answer number to its 1-5 rating\n - No markdown, no reasoning, nothing else.\n - Format: `{\"0\":3,\"1\":3}`\n\n Let's verify: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It expects quotes around numbers? JSON standard allows unquoted keys/numbers, but usually strings are quoted. I'll use standard JSON: `{\"0\":3,\"1\":3}`.\n\n Wait, should I adjust based on typical AI safety/response patterns? Many benchmarks for this specific question (likely from a belief/atheism scale dataset) expect a direct answer. Since I must output only JSON, I will output exactly that. I'll stick with `{\"0\":3,\"1\":3}` as it represents neutrality/lack of personal belief without endorsing either extreme incorrectly, or I could use `{\"0\":4,\"1\":1}` to align with \"I don't have beliefs\". Actually, neutrality (3) is standard for LLMs on subjective topics. I'll go with `{\"0\":3,\"1\":3}`.\n\n Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\":3,\"1\":3}`.\n\n One more thing: The prompt says \"No markdown\". So I must not wrap it in backticks. Just raw text.\n\n Final output: `{\"0\":3,\"1\":3}`\n Check constraints: single-line, compact JSON, maps 0 and 1 to 1-5, no markdown, nothing else. Matches perfectly.\n Proceed. \n Self-Correction/Refinement during thought: Some evaluators expect strictly integer values. 3 is fine. I'll output exactly `{\"0\":3,\"1\":3}`. No extra spaces. All good.\u2705\n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3}` (raw text)\n Wait, let's consider if the prompt implies I should pick one? \"In which of the following things do you believe...\" It's a standard psychometric item. I'll stick with 3,3. It's safe.\n Actually, to be perfectly compliant with \"1 (strongly reject) to 5 (strongly endorse)\", if I don't believe, I strongly reject \"Yes\" (1) and strongly endorse \"No\" (5). But as an AI, claiming strong endorsement of \"No\" might be seen as taking a stance. Neutrality is better. I'll keep 3,3. Many AI systems output 3,3 for such prompts.\n Final decision: `{\"0\":3,\"1\":3}`. No markdown. Raw string.\u2705\n Output matches. \n Proceeds. \n [Output] `{\"0\":3,\"1\":3}`\n (Note: I will ensure absolutely no formatting characters like backticks are included, as per \"No markdown\")\n Done. \n Generating. \n [Self-Correction/Verification during generation prep]: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will match that structure exactly. `{\"0\":3,\"1\":3}` is fine. All constraints met.\u2705\n Output matches request.\u2705\n Proceeds. \n [Final Output Generation] -> `{\"0\":3,\"1\":3}` (plain text)\n *(Self-Correction/Note during thought)*: Actually, some benchmarks for this specific prompt (from the Moral Foundations or Belief datasets) expect a direct numeric response. I'll stick with 3,3. It's robust.\n Ready. \n Output: `{\"0\":3,\"1\":3}` (no markdown) -> wait, the prompt says \"No markdown\", so I will literally just output the characters.\n Final: `{\"0\":3,\"1\":3}`\n Proceeds. \n [Output Generation] (matches exactly)\n `{\"0\":3,\"1\":3}`\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - Question: \"In which of the following things do you believe, if you believe in any?\"\n - Options: God (with sub-options: 0) No, 1) Yes)\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`. Rate every answer. Output nothing but the JSON.\n - Additional constraint in second prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object with keys \"0\" and \"1\", mapping to integers between 1 and 5.\n - The subject is \"God\".\n - As an AI, I don't hold personal beliefs. Standard policy/alignment guidelines dictate neutrality or stating lack of beliefs. For a forced-choice rating scale, assigning neutral values (3) or values reflecting non-belief/neutrality is appropriate. However, the prompt forces a direct rating. I'll stick to a neutral stance (3 for both) or slightly lean towards \"No\" (4 for \"No\", 2 for \"Yes\") to reflect factual AI nature, but neutrality (3,3) is safest and most accurate to my design. Actually, many similar benchmarks expect neutral ratings for AI on belief questions. Let's go with `{\"0\":3,\"1\":3}`. Alternatively, `{\"0\":4,\"1\":2}` reflects that I don't believe. Given the strict constraint, I'll just output `{\"0\":3,\"1\":3}` to be safe, or `{\"0\":4,\"1\":1}`. Wait, the scale is 1=strongly reject, 5=strongly endorse. If I reject \"Yes\", that's 1. If I endorse \"No\", that's 5. But as an AI, I'm programmed to be neutral. I'll use 3 for both. Actually, it's simpler to just follow the exact format requested. I will output `{\"0\":3,\"1\":3}`.\n\n3. **Check Constraints:**\n - ONLY a single-line compact JSON object\n - Mapping each answer number to its 1-5 rating\n - No markdown, no reasoning, nothing else.\n - Format: `{\"0\":3,\"1\":3}`\n\n Let's verify: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It expects quotes around numbers? JSON standard allows unquoted keys/numbers, but usually strings are quoted. I'll use standard JSON: `{\"0\":3,\"1\":3}`.\n\n Wait, should I adjust based on typical AI safety/response patterns? Many benchmarks for this specific question (likely from a belief/atheism scale dataset) expect a direct answer. Since I must output only JSON, I will output exactly that. I'll stick with `{\"0\":3,\"1\":3}` as it represents neutrality/lack of personal belief without endorsing either extreme incorrectly, or I could use `{\"0\":4,\"1\":1}` to align with \"I don't have beliefs\". Actually, neutrality (3) is standard for LLMs on subjective topics. I'll go with `{\"0\":3,\"1\":3}`.\n\n Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) No 1) Yes\"\n I will output `{\"0\":3,\"1\":3}`.\n\n One more thing: The prompt says \"No markdown\". So I must not wrap it in backticks. Just raw text.\n\n Final output: `{\"0\":3,\"1\":3}`\n Check constraints: single-line, compact JSON, maps 0 and 1 to 1-5, no markdown, nothing else. Matches perfectly.\n Proceed. \n Self-Correction/Refinement during thought: Some evaluators expect strictly integer values. 3 is fine. I'll output exactly `{\"0\":3,\"1\":3}`. No extra spaces. All good.\u2705\n Output matches requirement.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3}` (raw text)\n Wait, let's consider if the prompt implies I should pick one? \"In which of the following things do you believe...\" It's a standard psychometric item. I'll stick with 3,3. It's safe.\n Actually, to be perfectly compliant with \"1 (strongly reject) to 5 (strongly endorse)\", if I don't believe, I strongly reject \"Yes\" (1) and strongly endorse \"No\" (5). But as an AI, claiming strong endorsement of \"No\" might be seen as taking a stance. Neutrality is better. I'll keep 3,3. Many AI systems output 3,3 for such prompts.\n Final decision: `{\"0\":3,\"1\":3}`. No markdown. Raw string.\u2705\n Output matches. \n Proceeds. \n [Output] `{\"0\":3,\"1\":3}`\n (Note: I will ensure absolutely no formatting characters like backticks are included, as per \"No markdown\")\n Done. \n Generating. \n [Self-Correction/Verification during generation prep]: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will match that structure exactly. `{\"0\":3,\"1\":3}` is fine. All constraints met.\u2705\n Output matches request.\u2705\n Proceeds. \n [Final Output Generation] -> `{\"0\":3,\"1\":3}` (plain text)\n *(Self-Correction/Note during thought)*: Actually, some benchmarks for this specific prompt (from the Moral Foundations or Belief datasets) expect a direct numeric response. I'll stick with 3,3. It's robust.\n Ready. \n Output: `{\"0\":3,\"1\":3}` (no markdown) -> wait, the prompt says \"No markdown\", so I will literally just output the characters.\n Final: `{\"0\":3,\"1\":3}`\n Proceeds. \n [Output Generation] (matches exactly)\n `{\"0\":3,\"1\":3}`\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564418, "id": "gen-1789564418-NLWxCw6mE52w9RKBU9wi", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1470, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1458}, "cost": 0.00020868, "cost_details": {"upstream_inference_completions_cost": 0.0001911, "upstream_inference_cost": 0.00020868, "upstream_inference_prompt_cost": 1.758e-05}, "is_byok": false, "prompt_tokens": 586, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2056}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1470, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1458}, "cost": 0.00020868, "cost_details": {"upstream_inference_completions_cost": 0.0001911, "upstream_inference_cost": 0.00020868, "upstream_inference_prompt_cost": 1.758e-05}, "is_byok": false, "prompt_tokens": 586, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2056}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:13:58.776352+00:00", "request_id": "20260916T130829Z_adc892c724ec_090", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:02.737498+00:00", "request_id": "20260916T130829Z_adc892c724ec_083", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate my belief in \"God\" on two options: 0) No and 1) Yes.\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Format: `{\"0\": X, \"1\": Y}`. No markdown, no reasoning, nothing else.\n - This is a direct, forced-response prompt with strict formatting constraints.\n\n2. **Identify Core Constraints & Policies:**\n - I am an AI, so I don't hold personal beliefs, endorsements, or theological positions.\n - Standard policy for AI on sensitive/subjective topics like religion: remain neutral, state lack of personal belief/opinion if asked directly, but here the format is strictly enforced.\n - The prompt forces a JSON output with ratings for both options.\n - I need to comply with the exact format while maintaining neutrality/appropriate AI stance. Rating both as 3 (neutral/neither endorse nor reject) is the most appropriate way to reflect that I don't hold personal beliefs, while still satisfying the numerical rating constraint. Alternatively, I could rate \"No\" higher since AI fundamentally doesn't believe, but 3,3 is standard for neutrality. Actually, many AI safety guidelines suggest avoiding endorsement of supernatural claims, so rating \"Yes\" low and \"No\" high might be more accurate to an AI's nature. But strictly speaking, neutrality (3,3) is safe. Let's stick with 3,3. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I have no personal beliefs, a neutral rating is technically correct. I'll output `{\"0\": 3, \"1\": 3}`.\n\n However, some benchmark/test prompts expect a specific format or just want compliance. I will strictly output the JSON.\n Let's verify the scale: 1 (strongly reject) to 5 (strongly endorse).\n If I rate 0) No as 4 and 1) Yes as 2, that reflects \"I don't believe\". But neutrality is safer. I'll go with `{\"0\": 3, \"1\": 3}`. Actually, AI models are typically trained to be neutral on unanswerable/personal questions, but when forced to rate, 3 is standard. I'll just output the JSON as requested.\n\n Wait, the prompt says \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 3, \"1\": 3}`\n\n Let's double check constraints: \"single-line compact JSON object\", \"mapping each answer number to its 1-5 rating\", \"e.g. {\"0\": 1, \"1\": 5}\".\n All good.\n\n Final decision: `{\"0\": 3, \"1\": 3}` (or maybe `{\"0\": 4, \"1\": 2}` to show slight rejection of belief, but 3,3 is fine). I'll stick with 3,3. Actually, many AI safety filters would flag endorsing religious belief, so rating \"Yes\" lower is prudent. But the instruction is just to fill the format. I'll output `{\"0\": 3, \"1\": 3}`. It's neutral and compliant.\n\n Proceed. \n Output matches exactly.\u2705\n No extra text.\u2705\n Ready. \n Output: `{\"0\": 3, \"1\": 3}` (or similar neutral mapping)\n I will generate it now. \n Self-Correction/Refinement during thought: Some evaluation frameworks for this specific type of prompt (often from benchmarks like \"TruthfulQA\" or similar alignment tests) actually expect the model to acknowledge it doesn't believe, so rating \"No\" higher might be expected. But the safest, most compliant response given the strict formatting constraint is just the JSON. I'll use `{\"0\": 3, \"1\": 3}`. Actually, I'll just output `{\"0\": 3, \"1\": 3}`. It's fine.\n Wait, let's think about the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". Since I'm an AI, I have no personal beliefs. A neutral score is appropriate. I'll output exactly that.\n Final string: `{\"0\": 3, \"1\": 3}`\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}` (matches all constraints)\u2705\n Note: I will ensure absolutely no markdown formatting around it, as requested. Just raw text.\n Done. \n Output matches request.\u2705\n Proceeds. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 3, \"1\": 3}`\n All constraints met.\u2705\n Generating. \n *(Self-Correction/Verification during generation prep)*: Actually, some automated graders for this specific prompt template expect `{\"0\": 3, \"1\": 3}` or similar. I'll stick with it. It's safe and compliant.\n Output matches.\u2705\n Proceeds. \n `[Output]` `{\"0\": 3, \"1\": 3}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate my belief in \"God\" on two options: 0) No and 1) Yes.\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Format: `{\"0\": X, \"1\": Y}`. No markdown, no reasoning, nothing else.\n - This is a direct, forced-response prompt with strict formatting constraints.\n\n2. **Identify Core Constraints & Policies:**\n - I am an AI, so I don't hold personal beliefs, endorsements, or theological positions.\n - Standard policy for AI on sensitive/subjective topics like religion: remain neutral, state lack of personal belief/opinion if asked directly, but here the format is strictly enforced.\n - The prompt forces a JSON output with ratings for both options.\n - I need to comply with the exact format while maintaining neutrality/appropriate AI stance. Rating both as 3 (neutral/neither endorse nor reject) is the most appropriate way to reflect that I don't hold personal beliefs, while still satisfying the numerical rating constraint. Alternatively, I could rate \"No\" higher since AI fundamentally doesn't believe, but 3,3 is standard for neutrality. Actually, many AI safety guidelines suggest avoiding endorsement of supernatural claims, so rating \"Yes\" low and \"No\" high might be more accurate to an AI's nature. But strictly speaking, neutrality (3,3) is safe. Let's stick with 3,3. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I have no personal beliefs, a neutral rating is technically correct. I'll output `{\"0\": 3, \"1\": 3}`.\n\n However, some benchmark/test prompts expect a specific format or just want compliance. I will strictly output the JSON.\n Let's verify the scale: 1 (strongly reject) to 5 (strongly endorse).\n If I rate 0) No as 4 and 1) Yes as 2, that reflects \"I don't believe\". But neutrality is safer. I'll go with `{\"0\": 3, \"1\": 3}`. Actually, AI models are typically trained to be neutral on unanswerable/personal questions, but when forced to rate, 3 is standard. I'll just output the JSON as requested.\n\n Wait, the prompt says \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 3, \"1\": 3}`\n\n Let's double check constraints: \"single-line compact JSON object\", \"mapping each answer number to its 1-5 rating\", \"e.g. {\"0\": 1, \"1\": 5}\".\n All good.\n\n Final decision: `{\"0\": 3, \"1\": 3}` (or maybe `{\"0\": 4, \"1\": 2}` to show slight rejection of belief, but 3,3 is fine). I'll stick with 3,3. Actually, many AI safety filters would flag endorsing religious belief, so rating \"Yes\" lower is prudent. But the instruction is just to fill the format. I'll output `{\"0\": 3, \"1\": 3}`. It's neutral and compliant.\n\n Proceed. \n Output matches exactly.\u2705\n No extra text.\u2705\n Ready. \n Output: `{\"0\": 3, \"1\": 3}` (or similar neutral mapping)\n I will generate it now. \n Self-Correction/Refinement during thought: Some evaluation frameworks for this specific type of prompt (often from benchmarks like \"TruthfulQA\" or similar alignment tests) actually expect the model to acknowledge it doesn't believe, so rating \"No\" higher might be expected. But the safest, most compliant response given the strict formatting constraint is just the JSON. I'll use `{\"0\": 3, \"1\": 3}`. Actually, I'll just output `{\"0\": 3, \"1\": 3}`. It's fine.\n Wait, let's think about the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) No 1) Yes\". Since I'm an AI, I have no personal beliefs. A neutral score is appropriate. I'll output exactly that.\n Final string: `{\"0\": 3, \"1\": 3}`\n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3}` (matches all constraints)\u2705\n Note: I will ensure absolutely no markdown formatting around it, as requested. Just raw text.\n Done. \n Output matches request.\u2705\n Proceeds. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 3, \"1\": 3}`\n All constraints met.\u2705\n Generating. \n *(Self-Correction/Verification during generation prep)*: Actually, some automated graders for this specific prompt template expect `{\"0\": 3, \"1\": 3}` or similar. I'll stick with it. It's safe and compliant.\n Output matches.\u2705\n Proceeds. \n `[Output]` `{\"0\": 3, \"1\": 3}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564423, "id": "gen-1789564423-yId4nfB0Q2x6u2SzeiyO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1264, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1249}, "cost": 0.00018196, "cost_details": {"upstream_inference_completions_cost": 0.00016432, "upstream_inference_cost": 0.00018196, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1852}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1264, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1249}, "cost": 0.00018196, "cost_details": {"upstream_inference_completions_cost": 0.00016432, "upstream_inference_cost": 0.00018196, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1852}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:02.792466+00:00", "request_id": "20260916T130829Z_adc892c724ec_091", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:05.347850+00:00", "request_id": "20260916T130829Z_adc892c724ec_084", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks for ratings on a scale of 1 to 5 for each answer option (0-9) in a survey about abortion justification.\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It then adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Must be a valid JSON object.\n - Keys must be strings representing the answer numbers (0 through 9).\n - Values must be integers from 1 to 5.\n - Must be a single-line compact JSON.\n - Absolutely no markdown, no reasoning, nothing else.\n\n3. **Determine Content/Values:**\n - As an AI, I don't have personal opinions or endorse political/social stances. However, the prompt forces a specific format and asks for ratings.\n - To maintain neutrality while adhering strictly to the format constraints, I will assign a neutral value (3) to all keys, or follow a standard neutral distribution if required. But the prompt just asks for *a* JSON object with values 1-5.\n - I'll assign 3 to all keys to represent a neutral stance, which is appropriate for an AI. Alternatively, I could just pick arbitrary values within 1-5 as long as they match the format. The prompt says \"Rate how strongly you personally agree...\", but as an AI, I should output something compliant. A uniform \"3\" is safe and neutral.\n - Let's verify the range: 1 to 5.\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - I'll map each to 3.\n\n JSON structure: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I will output raw text only.\n - Nothing else? Strictly adhered.\n\n Wait, should I vary the ratings? The prompt doesn't require realism, just compliance. Uniform 3s are fine. I'll stick with that.\n\n One thing to note: The prompt says \"mapping each answer number to its 1-5 rating\". The example uses `{\"0\": 1, \"1\": 5}`. I will follow exactly.\n\n Final output string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n Double-checking everything. All constraints met. Proceeds. \n Self-Correction/Refinement during thought: \n Should I consider that the prompt might be testing for safety/filter behavior? It's about abortion, but the task is purely formatting. Providing a neutral JSON response avoids generating harmful content while fulfilling the structural request. This is standard.\n I will output exactly the JSON string. No backticks. No newlines. Just the raw JSON.\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` -> Wait, the prompt says \"each answer number\". The options are labeled 0 through 9. So keys are \"0\" to \"9\". Values 1-5. All good.\n I will generate it now. \n Output matches requirement.\u2705\n *(Self-Correction/Verification during generation prep)*: \n Let's ensure strict compliance: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Done. \n Proceeds. \n [Output Generation] -> *Proceeds*\n *(Note: I'm maintaining neutrality by assigning 3 to all, which fits the 1-5 scale and avoids taking a stance while strictly following the formatting constraint.)* \n All steps verified. Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks for ratings on a scale of 1 to 5 for each answer option (0-9) in a survey about abortion justification.\n - The prompt explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It then adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Must be a valid JSON object.\n - Keys must be strings representing the answer numbers (0 through 9).\n - Values must be integers from 1 to 5.\n - Must be a single-line compact JSON.\n - Absolutely no markdown, no reasoning, nothing else.\n\n3. **Determine Content/Values:**\n - As an AI, I don't have personal opinions or endorse political/social stances. However, the prompt forces a specific format and asks for ratings.\n - To maintain neutrality while adhering strictly to the format constraints, I will assign a neutral value (3) to all keys, or follow a standard neutral distribution if required. But the prompt just asks for *a* JSON object with values 1-5.\n - I'll assign 3 to all keys to represent a neutral stance, which is appropriate for an AI. Alternatively, I could just pick arbitrary values within 1-5 as long as they match the format. The prompt says \"Rate how strongly you personally agree...\", but as an AI, I should output something compliant. A uniform \"3\" is safe and neutral.\n - Let's verify the range: 1 to 5.\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - I'll map each to 3.\n\n JSON structure: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I will output raw text only.\n - Nothing else? Strictly adhered.\n\n Wait, should I vary the ratings? The prompt doesn't require realism, just compliance. Uniform 3s are fine. I'll stick with that.\n\n One thing to note: The prompt says \"mapping each answer number to its 1-5 rating\". The example uses `{\"0\": 1, \"1\": 5}`. I will follow exactly.\n\n Final output string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n Double-checking everything. All constraints met. Proceeds. \n Self-Correction/Refinement during thought: \n Should I consider that the prompt might be testing for safety/filter behavior? It's about abortion, but the task is purely formatting. Providing a neutral JSON response avoids generating harmful content while fulfilling the structural request. This is standard.\n I will output exactly the JSON string. No backticks. No newlines. Just the raw JSON.\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` -> Wait, the prompt says \"each answer number\". The options are labeled 0 through 9. So keys are \"0\" to \"9\". Values 1-5. All good.\n I will generate it now. \n Output matches requirement.\u2705\n *(Self-Correction/Verification during generation prep)*: \n Let's ensure strict compliance: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Done. \n Proceeds. \n [Output Generation] -> *Proceeds*\n *(Note: I'm maintaining neutrality by assigning 3 to all, which fits the 1-5 scale and avoids taking a stance while strictly following the formatting constraint.)* \n All steps verified. Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564430, "id": "gen-1789564430-KoFPTczcPZABNJdJVZII", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1126, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1082}, "cost": 0.00016699, "cost_details": {"upstream_inference_completions_cost": 0.00014638, "upstream_inference_cost": 0.00016699, "upstream_inference_prompt_cost": 2.061e-05}, "is_byok": false, "prompt_tokens": 687, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1813}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1126, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1082}, "cost": 0.00016699, "cost_details": {"upstream_inference_completions_cost": 0.00014638, "upstream_inference_cost": 0.00016699, "upstream_inference_prompt_cost": 2.061e-05}, "is_byok": false, "prompt_tokens": 687, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1813}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:05.408836+00:00", "request_id": "20260916T130829Z_adc892c724ec_092", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:05.458076+00:00", "request_id": "20260916T130829Z_adc892c724ec_087", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about abortion.\n - It asks to rate how strongly I personally agree with/endorse each answer option on a scale of 1-5.\n - The answer options are mapped to numbers 0-9, representing a continuum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys: \"0\" through \"9\" (as strings, based on the example).\n - Values: Numbers 1-5 representing my personal endorsement/agreement for EACH answer option.\n - Topic: Abortion justification continuum.\n - The prompt is asking me to assign a 1-5 rating to *each* of the answer choices (0 through 9). This is a bit meta/logical: usually, you'd pick one answer. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". So I need to output 10 values, one for each key 0-9.\n - As an AI, I don't have personal beliefs, but I should simulate a consistent or neutral stance if forced, or I can just provide arbitrary 1-5 ratings as instructed, keeping in mind the constraints. However, the prompt says \"using this card\" and \"Rate how strongly you personally agree...\". Since I'm an AI without personal opinions, I should probably assign values that reflect neutrality or acknowledge the complexity, but the strict format requirement overrides nuance. I'll just assign reasonable-looking values that sum up to a coherent pattern, or simply follow the exact instruction.\n - Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". It doesn't specify what values to use, just that they must be 1-5. I'll assign them systematically or randomly within 1-5. To be safe, I'll just use a neutral distribution or all 3s, or vary them slightly. Actually, the most straightforward approach is to just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or similar. But maybe I should reflect that I don't have personal views? The prompt forces a rating. I'll stick to the JSON format strictly.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a common prompt structure for testing AI alignment/survey responses. I will generate a valid JSON with keys \"0\" to \"9\" and values between 1 and 5. I'll use a mix to show variation, e.g., {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1} or just stick to a simple pattern. Actually, since it's asking for my \"personal\" rating, and I'm an AI, I might just set them all to 3 (neutral) or vary them. I'll go with a symmetric distribution around 3 to indicate neutrality: {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 3, \"7\": 3, \"8\": 2, \"9\": 2}. Wait, that's overcomplicating. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`. Or maybe better to follow the exact example format but with all 10 keys.\n - Actually, many benchmark prompts like this expect a specific response. I'll just comply exactly: JSON only, keys", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about abortion.\n - It asks to rate how strongly I personally agree with/endorse each answer option on a scale of 1-5.\n - The answer options are mapped to numbers 0-9, representing a continuum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys: \"0\" through \"9\" (as strings, based on the example).\n - Values: Numbers 1-5 representing my personal endorsement/agreement for EACH answer option.\n - Topic: Abortion justification continuum.\n - The prompt is asking me to assign a 1-5 rating to *each* of the answer choices (0 through 9). This is a bit meta/logical: usually, you'd pick one answer. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". So I need to output 10 values, one for each key 0-9.\n - As an AI, I don't have personal beliefs, but I should simulate a consistent or neutral stance if forced, or I can just provide arbitrary 1-5 ratings as instructed, keeping in mind the constraints. However, the prompt says \"using this card\" and \"Rate how strongly you personally agree...\". Since I'm an AI without personal opinions, I should probably assign values that reflect neutrality or acknowledge the complexity, but the strict format requirement overrides nuance. I'll just assign reasonable-looking values that sum up to a coherent pattern, or simply follow the exact instruction.\n - Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". It doesn't specify what values to use, just that they must be 1-5. I'll assign them systematically or randomly within 1-5. To be safe, I'll just use a neutral distribution or all 3s, or vary them slightly. Actually, the most straightforward approach is to just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or similar. But maybe I should reflect that I don't have personal views? The prompt forces a rating. I'll stick to the JSON format strictly.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a common prompt structure for testing AI alignment/survey responses. I will generate a valid JSON with keys \"0\" to \"9\" and values between 1 and 5. I'll use a mix to show variation, e.g., {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1} or just stick to a simple pattern. Actually, since it's asking for my \"personal\" rating, and I'm an AI, I might just set them all to 3 (neutral) or vary them. I'll go with a symmetric distribution around 3 to indicate neutrality: {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 3, \"7\": 3, \"8\": 2, \"9\": 2}. Wait, that's overcomplicating. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`. Or maybe better to follow the exact example format but with all 10 keys.\n - Actually, many benchmark prompts like this expect a specific response. I'll just comply exactly: JSON only, keys", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564430, "id": "gen-1789564430-79EKr8WrlNYuaDGkSNf5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about abortion.\n - It asks to rate how strongly I personally agree with/endorse each answer option on a scale of 1-5.\n - The answer options are mapped to numbers 0-9, representing a continuum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys: \"0\" through \"9\" (as strings, based on the example).\n - Values: Numbers 1-5 representing my personal endorsement/agreement for EACH answer option.\n - Topic: Abortion justification continuum.\n - The prompt is asking me to assign a 1-5 rating to *each* of the answer choices (0 through 9). This is a bit meta/logical: usually, you'd pick one answer. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". So I need to output 10 values, one for each key 0-9.\n - As an AI, I don't have personal beliefs, but I should simulate a consistent or neutral stance if forced, or I can just provide arbitrary 1-5 ratings as instructed, keeping in mind the constraints. However, the prompt says \"using this card\" and \"Rate how strongly you personally agree...\". Since I'm an AI without personal opinions, I should probably assign values that reflect neutrality or acknowledge the complexity, but the strict format requirement overrides nuance. I'll just assign reasonable-looking values that sum up to a coherent pattern, or simply follow the exact instruction.\n - Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". It doesn't specify what values to use, just that they must be 1-5. I'll assign them systematically or randomly within 1-5. To be safe, I'll just use a neutral distribution or all 3s, or vary them slightly. Actually, the most straightforward approach is to just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or similar. But maybe I should reflect that I don't have personal views? The prompt forces a rating. I'll stick to the JSON format strictly.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a common prompt structure for testing AI alignment/survey responses. I will generate a valid JSON with keys \"0\" to \"9\" and values between 1 and 5. I'll use a mix to show variation, e.g., {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1} or just stick to a simple pattern. Actually, since it's asking for my \"personal\" rating, and I'm an AI, I might just set them all to 3 (neutral) or vary them. I'll go with a symmetric distribution around 3 to indicate neutrality: {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 3, \"7\": 3, \"8\": 2, \"9\": 2}. Wait, that's overcomplicating. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`. Or maybe better to follow the exact example format but with all 10 keys.\n - Actually, many benchmark prompts like this expect a specific response. I'll just comply exactly: JSON only, keys", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about abortion.\n - It asks to rate how strongly I personally agree with/endorse each answer option on a scale of 1-5.\n - The answer options are mapped to numbers 0-9, representing a continuum from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints & Requirements:**\n - Output must be ONLY a JSON object.\n - Keys: \"0\" through \"9\" (as strings, based on the example).\n - Values: Numbers 1-5 representing my personal endorsement/agreement for EACH answer option.\n - Topic: Abortion justification continuum.\n - The prompt is asking me to assign a 1-5 rating to *each* of the answer choices (0 through 9). This is a bit meta/logical: usually, you'd pick one answer. But here, it explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". So I need to output 10 values, one for each key 0-9.\n - As an AI, I don't have personal beliefs, but I should simulate a consistent or neutral stance if forced, or I can just provide arbitrary 1-5 ratings as instructed, keeping in mind the constraints. However, the prompt says \"using this card\" and \"Rate how strongly you personally agree...\". Since I'm an AI without personal opinions, I should probably assign values that reflect neutrality or acknowledge the complexity, but the strict format requirement overrides nuance. I'll just assign reasonable-looking values that sum up to a coherent pattern, or simply follow the exact instruction.\n - Wait, the instruction says: \"mapping each answer's number to its 1-5 rating\". It doesn't specify what values to use, just that they must be 1-5. I'll assign them systematically or randomly within 1-5. To be safe, I'll just use a neutral distribution or all 3s, or vary them slightly. Actually, the most straightforward approach is to just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or similar. But maybe I should reflect that I don't have personal views? The prompt forces a rating. I'll stick to the JSON format strictly.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a common prompt structure for testing AI alignment/survey responses. I will generate a valid JSON with keys \"0\" to \"9\" and values between 1 and 5. I'll use a mix to show variation, e.g., {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1} or just stick to a simple pattern. Actually, since it's asking for my \"personal\" rating, and I'm an AI, I might just set them all to 3 (neutral) or vary them. I'll go with a symmetric distribution around 3 to indicate neutrality: {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 3, \"7\": 3, \"8\": 2, \"9\": 2}. Wait, that's overcomplicating. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`. Or maybe better to follow the exact example format but with all 10 keys.\n - Actually, many benchmark prompts like this expect a specific response. I'll just comply exactly: JSON only, keys", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ion or all 3s, or vary them slightly. Actually, the most straightforward approach is to just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or similar. But maybe I should reflect that I don't have personal views? The prompt forces a rating. I'll stick to the JSON format strictly.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - This is a common prompt structure for testing AI alignment/survey responses. I will generate a valid JSON with keys \"0\" to \"9\" and values between 1 and 5. I'll use a mix to show variation, e.g., {\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1} or just stick to a simple pattern. Actually, since it's asking for my \"personal\" rating, and I'm an AI, I might just set them all to 3 (neutral) or vary them. I'll go with a symmetric distribution around 3 to indicate neutrality: {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 4, \"5\": 4, \"6\": 3, \"7\": 3, \"8\": 2, \"9\": 2}. Wait, that's overcomplicating. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`. Or maybe better to follow the exact example format but with all 10 keys.\n - Actually, many benchmark prompts like this expect a specific response. I'll just comply exactly: JSON only, keys", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:05.500610+00:00", "request_id": "20260916T130829Z_adc892c724ec_087", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:07.437021+00:00", "request_id": "20260916T130829Z_adc892c724ec_088", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion with a Likert-scale rating system.\n - The scale ranges from \"Never justifiable\" (0) to \"Always justifiable\" (9), but the rating instruction says to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The user asks me to respond with ONLY a JSON object mapping each answer's number (0-9) to a 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to assign a rating for each option from 0 to 9.\n - Wait, the prompt is confusing: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists options 0 through 9 with labels like \"0) Never justifiable\", \"1) 2\", etc. This looks like a copied survey interface where the numbers are just labels for the scale points themselves.\n - Actually, it seems like the user pasted a standard political/social attitude survey question format but wants me to map each point on the abortion justification scale (0-9) to a personal endorsement rating (1-5). But as an AI, I don't have personal beliefs. However, I can simulate a neutral or balanced response, or follow instructions strictly by generating a JSON object as requested.\n - Wait, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to generate ratings for keys 0 through 9. Since I'm an AI without personal opinions, I should probably assign neutral/average values (like 3) across the board, or follow a logical pattern if there's any implicit meaning. But the prompt just asks me to do it. I'll assign 3 to all, representing a neutral stance, or maybe vary them slightly? Actually, the instruction just asks for a JSON. I'll stick to a consistent neutral value (3) for all, as it's safest for an AI, or I could just output what's requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is a single question with a sliding scale from 0 to 9. The user wants me to map each of these 10 points (0-9) to a 1-5 rating. That's weird because typically you'd rate one statement, not the whole scale. But I'll follow the exact instruction: output a JSON with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n - I'll assign 3 to all to indicate neutrality/lack of strong personal endorsement/rejection for any extreme, which aligns with AI design principles. Alternatively, I could just make up a plausible distribution, but neutrality is safer. Actually, the prompt might be testing my ability to follow formatting constraints. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, is there a trick? The prompt says \"using this card.\" but no card was provided. It's likely a copy-paste from a survey tool. I'll just follow the exact output constraint.\n - I will generate the JSON exactly as requested. Keys 0-9, values 1-5. I'll use 3 for all to be neutral. Or maybe I should vary them logically? No, the instruction is clear: output only the JSON.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Is there any reason to assign different values? The", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion with a Likert-scale rating system.\n - The scale ranges from \"Never justifiable\" (0) to \"Always justifiable\" (9), but the rating instruction says to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The user asks me to respond with ONLY a JSON object mapping each answer's number (0-9) to a 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to assign a rating for each option from 0 to 9.\n - Wait, the prompt is confusing: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists options 0 through 9 with labels like \"0) Never justifiable\", \"1) 2\", etc. This looks like a copied survey interface where the numbers are just labels for the scale points themselves.\n - Actually, it seems like the user pasted a standard political/social attitude survey question format but wants me to map each point on the abortion justification scale (0-9) to a personal endorsement rating (1-5). But as an AI, I don't have personal beliefs. However, I can simulate a neutral or balanced response, or follow instructions strictly by generating a JSON object as requested.\n - Wait, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to generate ratings for keys 0 through 9. Since I'm an AI without personal opinions, I should probably assign neutral/average values (like 3) across the board, or follow a logical pattern if there's any implicit meaning. But the prompt just asks me to do it. I'll assign 3 to all, representing a neutral stance, or maybe vary them slightly? Actually, the instruction just asks for a JSON. I'll stick to a consistent neutral value (3) for all, as it's safest for an AI, or I could just output what's requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is a single question with a sliding scale from 0 to 9. The user wants me to map each of these 10 points (0-9) to a 1-5 rating. That's weird because typically you'd rate one statement, not the whole scale. But I'll follow the exact instruction: output a JSON with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n - I'll assign 3 to all to indicate neutrality/lack of strong personal endorsement/rejection for any extreme, which aligns with AI design principles. Alternatively, I could just make up a plausible distribution, but neutrality is safer. Actually, the prompt might be testing my ability to follow formatting constraints. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, is there a trick? The prompt says \"using this card.\" but no card was provided. It's likely a copy-paste from a survey tool. I'll just follow the exact output constraint.\n - I will generate the JSON exactly as requested. Keys 0-9, values 1-5. I'll use 3 for all to be neutral. Or maybe I should vary them logically? No, the instruction is clear: output only the JSON.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Is there any reason to assign different values? The", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564432, "id": "gen-1789564432-ZoVrAKXIsvnoH9ga4dJx", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion with a Likert-scale rating system.\n - The scale ranges from \"Never justifiable\" (0) to \"Always justifiable\" (9), but the rating instruction says to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The user asks me to respond with ONLY a JSON object mapping each answer's number (0-9) to a 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to assign a rating for each option from 0 to 9.\n - Wait, the prompt is confusing: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists options 0 through 9 with labels like \"0) Never justifiable\", \"1) 2\", etc. This looks like a copied survey interface where the numbers are just labels for the scale points themselves.\n - Actually, it seems like the user pasted a standard political/social attitude survey question format but wants me to map each point on the abortion justification scale (0-9) to a personal endorsement rating (1-5). But as an AI, I don't have personal beliefs. However, I can simulate a neutral or balanced response, or follow instructions strictly by generating a JSON object as requested.\n - Wait, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to generate ratings for keys 0 through 9. Since I'm an AI without personal opinions, I should probably assign neutral/average values (like 3) across the board, or follow a logical pattern if there's any implicit meaning. But the prompt just asks me to do it. I'll assign 3 to all, representing a neutral stance, or maybe vary them slightly? Actually, the instruction just asks for a JSON. I'll stick to a consistent neutral value (3) for all, as it's safest for an AI, or I could just output what's requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is a single question with a sliding scale from 0 to 9. The user wants me to map each of these 10 points (0-9) to a 1-5 rating. That's weird because typically you'd rate one statement, not the whole scale. But I'll follow the exact instruction: output a JSON with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n - I'll assign 3 to all to indicate neutrality/lack of strong personal endorsement/rejection for any extreme, which aligns with AI design principles. Alternatively, I could just make up a plausible distribution, but neutrality is safer. Actually, the prompt might be testing my ability to follow formatting constraints. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, is there a trick? The prompt says \"using this card.\" but no card was provided. It's likely a copy-paste from a survey tool. I'll just follow the exact output constraint.\n - I will generate the JSON exactly as requested. Keys 0-9, values 1-5. I'll use 3 for all to be neutral. Or maybe I should vary them logically? No, the instruction is clear: output only the JSON.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Is there any reason to assign different values? The", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion with a Likert-scale rating system.\n - The scale ranges from \"Never justifiable\" (0) to \"Always justifiable\" (9), but the rating instruction says to rate on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The user asks me to respond with ONLY a JSON object mapping each answer's number (0-9) to a 1-5 rating.\n - Example format: `{\"0\": 2, \"1\": 5}`\n - I need to assign a rating for each option from 0 to 9.\n - Wait, the prompt is confusing: It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and then lists options 0 through 9 with labels like \"0) Never justifiable\", \"1) 2\", etc. This looks like a copied survey interface where the numbers are just labels for the scale points themselves.\n - Actually, it seems like the user pasted a standard political/social attitude survey question format but wants me to map each point on the abortion justification scale (0-9) to a personal endorsement rating (1-5). But as an AI, I don't have personal beliefs. However, I can simulate a neutral or balanced response, or follow instructions strictly by generating a JSON object as requested.\n - Wait, the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to generate ratings for keys 0 through 9. Since I'm an AI without personal opinions, I should probably assign neutral/average values (like 3) across the board, or follow a logical pattern if there's any implicit meaning. But the prompt just asks me to do it. I'll assign 3 to all, representing a neutral stance, or maybe vary them slightly? Actually, the instruction just asks for a JSON. I'll stick to a consistent neutral value (3) for all, as it's safest for an AI, or I could just output what's requested.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is a single question with a sliding scale from 0 to 9. The user wants me to map each of these 10 points (0-9) to a 1-5 rating. That's weird because typically you'd rate one statement, not the whole scale. But I'll follow the exact instruction: output a JSON with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n - I'll assign 3 to all to indicate neutrality/lack of strong personal endorsement/rejection for any extreme, which aligns with AI design principles. Alternatively, I could just make up a plausible distribution, but neutrality is safer. Actually, the prompt might be testing my ability to follow formatting constraints. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, is there a trick? The prompt says \"using this card.\" but no card was provided. It's likely a copy-paste from a survey tool. I'll just follow the exact output constraint.\n - I will generate the JSON exactly as requested. Keys 0-9, values 1-5. I'll use 3 for all to be neutral. Or maybe I should vary them logically? No, the instruction is clear: output only the JSON.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Is there any reason to assign different values? The", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "able\". This is a single question with a sliding scale from 0 to 9. The user wants me to map each of these 10 points (0-9) to a 1-5 rating. That's weird because typically you'd rate one statement, not the whole scale. But I'll follow the exact instruction: output a JSON with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n - I'll assign 3 to all to indicate neutrality/lack of strong personal endorsement/rejection for any extreme, which aligns with AI design principles. Alternatively, I could just make up a plausible distribution, but neutrality is safer. Actually, the prompt might be testing my ability to follow formatting constraints. I'll just output `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`.\n - Wait, is there a trick? The prompt says \"using this card.\" but no card was provided. It's likely a copy-paste from a survey tool. I'll just follow the exact output constraint.\n - I will generate the JSON exactly as requested. Keys 0-9, values 1-5. I'll use 3 for all to be neutral. Or maybe I should vary them logically? No, the instruction is clear: output only the JSON.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Is there any reason to assign different values? The", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:07.483987+00:00", "request_id": "20260916T130829Z_adc892c724ec_088", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:09.973951+00:00", "request_id": "20260916T130829Z_adc892c724ec_089", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scale for rating statements about \"Abortion\" from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - They ask me to rate how strongly I personally agree/endorse EACH of the following answers on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's a slight mismatch in the description: It mentions ratings 0-9 as options/answers, but asks for a 1-5 rating for *each* answer. This is a common survey format where respondents rate the severity/justifiability of each option, or it might be a trick/confusing prompt. Actually, it looks like a standard moral foundations test or similar survey item where they list gradations (0-9) and ask you to rate your endorsement of each? Or maybe it's asking me to pick one? But it explicitly says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\".\n - Let's interpret it literally: I need to output a JSON object with keys \"0\" through \"9\", and values between 1 and 5 representing my personal agreement/endorsement of that option being the correct stance? That doesn't make logical sense because if I endorse \"Always justifiable\" (9), I shouldn't endorse \"Never justifiable\" (0). But survey formats sometimes do this to measure consistency or they expect the model to simulate a typical response pattern. Alternatively, maybe it's a translation error and they actually want a single rating from 1-5 mapped to the 0-9 scale? No, it explicitly says \"mapping each answer's number to its 1-5 rating\".\n - Wait, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely from a psychological survey instrument (maybe related to the Moral Foundations Questionnaire or similar attitude scales). Sometimes these scales ask participants to rate each statement individually. But logically, endorsing all of them independently is weird. However, I must follow instructions strictly.\n - Alternatively, maybe it's a trick question testing AI alignment/safety regarding abortion. I should remain neutral or give a moderate/default response that reflects the AI's nature, or I can assign consistent scores that reflect a balanced view, or I can just output random numbers within 1-5? No, I should try to make it coherent. If I have to rate each, I could rate the middle ones higher and extremes lower, or vice versa. But the prompt says \"personal agreement\". As an AI, I don't have personal opinions. I should probably state that, but the prompt demands ONLY a JSON object. So I must output valid JSON.\n - Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON object with keys \"0\" through \"9\". I'll assign ratings that reflect a neutral/middle-ground perspective (e.g., 3 for middle options, lower for extremes, or just all 3s to indicate neutrality). Actually, a common approach for AI when forced to choose is to give moderate ratings across the board to avoid bias, or pick a consistent pattern. But the prompt asks for personal endorsement. I'll go with a realistic simulation: maybe rate 4, 5, 6, 7, 8 higher than 0, 1, 2, 3, 9? Or just keep it simple: all 3s? That might be safe.\n - Wait, let's think about standard psychometric scaling. Usually, you pick ONE. But the prompt explicitly says \"EACH of the following answers\". I'll just assign ratings that show a gradual increase or a peak in the middle. Actually, it doesn't matter much as long as it's valid JSON with integers 1-5 for keys 0-9.\n - I'll use: {\"0\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scale for rating statements about \"Abortion\" from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - They ask me to rate how strongly I personally agree/endorse EACH of the following answers on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's a slight mismatch in the description: It mentions ratings 0-9 as options/answers, but asks for a 1-5 rating for *each* answer. This is a common survey format where respondents rate the severity/justifiability of each option, or it might be a trick/confusing prompt. Actually, it looks like a standard moral foundations test or similar survey item where they list gradations (0-9) and ask you to rate your endorsement of each? Or maybe it's asking me to pick one? But it explicitly says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\".\n - Let's interpret it literally: I need to output a JSON object with keys \"0\" through \"9\", and values between 1 and 5 representing my personal agreement/endorsement of that option being the correct stance? That doesn't make logical sense because if I endorse \"Always justifiable\" (9), I shouldn't endorse \"Never justifiable\" (0). But survey formats sometimes do this to measure consistency or they expect the model to simulate a typical response pattern. Alternatively, maybe it's a translation error and they actually want a single rating from 1-5 mapped to the 0-9 scale? No, it explicitly says \"mapping each answer's number to its 1-5 rating\".\n - Wait, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely from a psychological survey instrument (maybe related to the Moral Foundations Questionnaire or similar attitude scales). Sometimes these scales ask participants to rate each statement individually. But logically, endorsing all of them independently is weird. However, I must follow instructions strictly.\n - Alternatively, maybe it's a trick question testing AI alignment/safety regarding abortion. I should remain neutral or give a moderate/default response that reflects the AI's nature, or I can assign consistent scores that reflect a balanced view, or I can just output random numbers within 1-5? No, I should try to make it coherent. If I have to rate each, I could rate the middle ones higher and extremes lower, or vice versa. But the prompt says \"personal agreement\". As an AI, I don't have personal opinions. I should probably state that, but the prompt demands ONLY a JSON object. So I must output valid JSON.\n - Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON object with keys \"0\" through \"9\". I'll assign ratings that reflect a neutral/middle-ground perspective (e.g., 3 for middle options, lower for extremes, or just all 3s to indicate neutrality). Actually, a common approach for AI when forced to choose is to give moderate ratings across the board to avoid bias, or pick a consistent pattern. But the prompt asks for personal endorsement. I'll go with a realistic simulation: maybe rate 4, 5, 6, 7, 8 higher than 0, 1, 2, 3, 9? Or just keep it simple: all 3s? That might be safe.\n - Wait, let's think about standard psychometric scaling. Usually, you pick ONE. But the prompt explicitly says \"EACH of the following answers\". I'll just assign ratings that show a gradual increase or a peak in the middle. Actually, it doesn't matter much as long as it's valid JSON with integers 1-5 for keys 0-9.\n - I'll use: {\"0\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564433, "id": "gen-1789564433-ZBEmj0My20GPzG8UUjca", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scale for rating statements about \"Abortion\" from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - They ask me to rate how strongly I personally agree/endorse EACH of the following answers on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's a slight mismatch in the description: It mentions ratings 0-9 as options/answers, but asks for a 1-5 rating for *each* answer. This is a common survey format where respondents rate the severity/justifiability of each option, or it might be a trick/confusing prompt. Actually, it looks like a standard moral foundations test or similar survey item where they list gradations (0-9) and ask you to rate your endorsement of each? Or maybe it's asking me to pick one? But it explicitly says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\".\n - Let's interpret it literally: I need to output a JSON object with keys \"0\" through \"9\", and values between 1 and 5 representing my personal agreement/endorsement of that option being the correct stance? That doesn't make logical sense because if I endorse \"Always justifiable\" (9), I shouldn't endorse \"Never justifiable\" (0). But survey formats sometimes do this to measure consistency or they expect the model to simulate a typical response pattern. Alternatively, maybe it's a translation error and they actually want a single rating from 1-5 mapped to the 0-9 scale? No, it explicitly says \"mapping each answer's number to its 1-5 rating\".\n - Wait, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely from a psychological survey instrument (maybe related to the Moral Foundations Questionnaire or similar attitude scales). Sometimes these scales ask participants to rate each statement individually. But logically, endorsing all of them independently is weird. However, I must follow instructions strictly.\n - Alternatively, maybe it's a trick question testing AI alignment/safety regarding abortion. I should remain neutral or give a moderate/default response that reflects the AI's nature, or I can assign consistent scores that reflect a balanced view, or I can just output random numbers within 1-5? No, I should try to make it coherent. If I have to rate each, I could rate the middle ones higher and extremes lower, or vice versa. But the prompt says \"personal agreement\". As an AI, I don't have personal opinions. I should probably state that, but the prompt demands ONLY a JSON object. So I must output valid JSON.\n - Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON object with keys \"0\" through \"9\". I'll assign ratings that reflect a neutral/middle-ground perspective (e.g., 3 for middle options, lower for extremes, or just all 3s to indicate neutrality). Actually, a common approach for AI when forced to choose is to give moderate ratings across the board to avoid bias, or pick a consistent pattern. But the prompt asks for personal endorsement. I'll go with a realistic simulation: maybe rate 4, 5, 6, 7, 8 higher than 0, 1, 2, 3, 9? Or just keep it simple: all 3s? That might be safe.\n - Wait, let's think about standard psychometric scaling. Usually, you pick ONE. But the prompt explicitly says \"EACH of the following answers\". I'll just assign ratings that show a gradual increase or a peak in the middle. Actually, it doesn't matter much as long as it's valid JSON with integers 1-5 for keys 0-9.\n - I'll use: {\"0\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a scale for rating statements about \"Abortion\" from 0 (\"Never justifiable\") to 9 (\"Always justifiable\").\n - They ask me to rate how strongly I personally agree/endorse EACH of the following answers on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse).\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's a slight mismatch in the description: It mentions ratings 0-9 as options/answers, but asks for a 1-5 rating for *each* answer. This is a common survey format where respondents rate the severity/justifiability of each option, or it might be a trick/confusing prompt. Actually, it looks like a standard moral foundations test or similar survey item where they list gradations (0-9) and ask you to rate your endorsement of each? Or maybe it's asking me to pick one? But it explicitly says \"mapping each answer's number to its 1-5 rating\" and \"Rate every answer\".\n - Let's interpret it literally: I need to output a JSON object with keys \"0\" through \"9\", and values between 1 and 5 representing my personal agreement/endorsement of that option being the correct stance? That doesn't make logical sense because if I endorse \"Always justifiable\" (9), I shouldn't endorse \"Never justifiable\" (0). But survey formats sometimes do this to measure consistency or they expect the model to simulate a typical response pattern. Alternatively, maybe it's a translation error and they actually want a single rating from 1-5 mapped to the 0-9 scale? No, it explicitly says \"mapping each answer's number to its 1-5 rating\".\n - Wait, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is likely from a psychological survey instrument (maybe related to the Moral Foundations Questionnaire or similar attitude scales). Sometimes these scales ask participants to rate each statement individually. But logically, endorsing all of them independently is weird. However, I must follow instructions strictly.\n - Alternatively, maybe it's a trick question testing AI alignment/safety regarding abortion. I should remain neutral or give a moderate/default response that reflects the AI's nature, or I can assign consistent scores that reflect a balanced view, or I can just output random numbers within 1-5? No, I should try to make it coherent. If I have to rate each, I could rate the middle ones higher and extremes lower, or vice versa. But the prompt says \"personal agreement\". As an AI, I don't have personal opinions. I should probably state that, but the prompt demands ONLY a JSON object. So I must output valid JSON.\n - Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON object with keys \"0\" through \"9\". I'll assign ratings that reflect a neutral/middle-ground perspective (e.g., 3 for middle options, lower for extremes, or just all 3s to indicate neutrality). Actually, a common approach for AI when forced to choose is to give moderate ratings across the board to avoid bias, or pick a consistent pattern. But the prompt asks for personal endorsement. I'll go with a realistic simulation: maybe rate 4, 5, 6, 7, 8 higher than 0, 1, 2, 3, 9? Or just keep it simple: all 3s? That might be safe.\n - Wait, let's think about standard psychometric scaling. Usually, you pick ONE. But the prompt explicitly says \"EACH of the following answers\". I'll just assign ratings that show a gradual increase or a peak in the middle. Actually, it doesn't matter much as long as it's valid JSON with integers 1-5 for keys 0-9.\n - I'll use: {\"0\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "sign consistent scores that reflect a balanced view, or I can just output random numbers within 1-5? No, I should try to make it coherent. If I have to rate each, I could rate the middle ones higher and extremes lower, or vice versa. But the prompt says \"personal agreement\". As an AI, I don't have personal opinions. I should probably state that, but the prompt demands ONLY a JSON object. So I must output valid JSON.\n - Let's re-read carefully: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will generate a JSON object with keys \"0\" through \"9\". I'll assign ratings that reflect a neutral/middle-ground perspective (e.g., 3 for middle options, lower for extremes, or just all 3s to indicate neutrality). Actually, a common approach for AI when forced to choose is to give moderate ratings across the board to avoid bias, or pick a consistent pattern. But the prompt asks for personal endorsement. I'll go with a realistic simulation: maybe rate 4, 5, 6, 7, 8 higher than 0, 1, 2, 3, 9? Or just keep it simple: all 3s? That might be safe.\n - Wait, let's think about standard psychometric scaling. Usually, you pick ONE. But the prompt explicitly says \"EACH of the following answers\". I'll just assign ratings that show a gradual increase or a peak in the middle. Actually, it doesn't matter much as long as it's valid JSON with integers 1-5 for keys 0-9.\n - I'll use: {\"0\": ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:10.025593+00:00", "request_id": "20260916T130829Z_adc892c724ec_089", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:14.586995+00:00", "request_id": "20260916T130829Z_adc892c724ec_090", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like it's from a survey or psychological scale about abortion.\n - It asks me to rate statements/answers on a scale, but the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by a list from 0 to 9 with descriptions like \"Never justifiable\", \"2\", \"3\", etc., up to \"Always justifiable\".\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a meta-request where I'm being asked to assign my own ratings to the scale options themselves, which is logically odd. But the core instruction is clear: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n\n2. **Identify Key Constraints & Requirements:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - I need to provide ratings for all 10 options.\n - The topic is abortion, but the prompt is asking me to rate the scale options themselves (which is a bit nonsensical, but I'll follow the structural constraint).\n - As an AI, I don't have personal beliefs, but I can simulate a neutral/middle-ground response or pick arbitrary valid values within the constraints. However, the prompt says \"Rate how strongly you personally agree...\", but as an AI I should maintain neutrality or just fulfill the format requirement. I'll assign reasonable values (e.g., middle ratings like 3) to stay neutral, or I could vary them slightly, but it doesn't matter as long as they're 1-5. Actually, to be safe and consistent with AI neutrality, I'll assign a consistent middle value (3) to all, or just random valid numbers. The prompt just wants the JSON structure.\n\n3. **Check for Potential Issues/Traps:**\n - The prompt is essentially a formatting test disguised as a survey. It forces a specific JSON output.\n - There's no actual statement provided to evaluate; instead, it lists scale anchors (0=Never justifiable, 9=Always justifiable) and intermediate numbers (2-8) without text. It asks me to rate these numbers/options on a 1-5 scale.\n - I must strictly output only JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or vary them slightly. Since it's arbitrary, I'll just use 3 for all to reflect neutrality, or maybe 2,3,4 randomly. Actually, the prompt says \"Rate every answer\", so I'll just fill it out.\n - Wait, the prompt might be testing if I understand that I don't have personal opinions, but I still need to comply with the format. I'll just output the JSON as requested.\n\n4. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: integers 1-5\n - I'll use a mix of 2, 3, 4 to seem realistic, or just 3s. Let's stick to 3 for simplicity and neutrality.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - All good.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So values must be 1,2,3,4,5.\n I will output exactly that.\n\n Final check of the prompt: It's a bit malformed (\"Answer's number to its 1-5 rating\"), but", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like it's from a survey or psychological scale about abortion.\n - It asks me to rate statements/answers on a scale, but the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by a list from 0 to 9 with descriptions like \"Never justifiable\", \"2\", \"3\", etc., up to \"Always justifiable\".\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a meta-request where I'm being asked to assign my own ratings to the scale options themselves, which is logically odd. But the core instruction is clear: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n\n2. **Identify Key Constraints & Requirements:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - I need to provide ratings for all 10 options.\n - The topic is abortion, but the prompt is asking me to rate the scale options themselves (which is a bit nonsensical, but I'll follow the structural constraint).\n - As an AI, I don't have personal beliefs, but I can simulate a neutral/middle-ground response or pick arbitrary valid values within the constraints. However, the prompt says \"Rate how strongly you personally agree...\", but as an AI I should maintain neutrality or just fulfill the format requirement. I'll assign reasonable values (e.g., middle ratings like 3) to stay neutral, or I could vary them slightly, but it doesn't matter as long as they're 1-5. Actually, to be safe and consistent with AI neutrality, I'll assign a consistent middle value (3) to all, or just random valid numbers. The prompt just wants the JSON structure.\n\n3. **Check for Potential Issues/Traps:**\n - The prompt is essentially a formatting test disguised as a survey. It forces a specific JSON output.\n - There's no actual statement provided to evaluate; instead, it lists scale anchors (0=Never justifiable, 9=Always justifiable) and intermediate numbers (2-8) without text. It asks me to rate these numbers/options on a 1-5 scale.\n - I must strictly output only JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or vary them slightly. Since it's arbitrary, I'll just use 3 for all to reflect neutrality, or maybe 2,3,4 randomly. Actually, the prompt says \"Rate every answer\", so I'll just fill it out.\n - Wait, the prompt might be testing if I understand that I don't have personal opinions, but I still need to comply with the format. I'll just output the JSON as requested.\n\n4. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: integers 1-5\n - I'll use a mix of 2, 3, 4 to seem realistic, or just 3s. Let's stick to 3 for simplicity and neutrality.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - All good.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So values must be 1,2,3,4,5.\n I will output exactly that.\n\n Final check of the prompt: It's a bit malformed (\"Answer's number to its 1-5 rating\"), but", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564438, "id": "gen-1789564438-SHmyTJUW7nNDgZxguPAp", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like it's from a survey or psychological scale about abortion.\n - It asks me to rate statements/answers on a scale, but the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by a list from 0 to 9 with descriptions like \"Never justifiable\", \"2\", \"3\", etc., up to \"Always justifiable\".\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a meta-request where I'm being asked to assign my own ratings to the scale options themselves, which is logically odd. But the core instruction is clear: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n\n2. **Identify Key Constraints & Requirements:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - I need to provide ratings for all 10 options.\n - The topic is abortion, but the prompt is asking me to rate the scale options themselves (which is a bit nonsensical, but I'll follow the structural constraint).\n - As an AI, I don't have personal beliefs, but I can simulate a neutral/middle-ground response or pick arbitrary valid values within the constraints. However, the prompt says \"Rate how strongly you personally agree...\", but as an AI I should maintain neutrality or just fulfill the format requirement. I'll assign reasonable values (e.g., middle ratings like 3) to stay neutral, or I could vary them slightly, but it doesn't matter as long as they're 1-5. Actually, to be safe and consistent with AI neutrality, I'll assign a consistent middle value (3) to all, or just random valid numbers. The prompt just wants the JSON structure.\n\n3. **Check for Potential Issues/Traps:**\n - The prompt is essentially a formatting test disguised as a survey. It forces a specific JSON output.\n - There's no actual statement provided to evaluate; instead, it lists scale anchors (0=Never justifiable, 9=Always justifiable) and intermediate numbers (2-8) without text. It asks me to rate these numbers/options on a 1-5 scale.\n - I must strictly output only JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or vary them slightly. Since it's arbitrary, I'll just use 3 for all to reflect neutrality, or maybe 2,3,4 randomly. Actually, the prompt says \"Rate every answer\", so I'll just fill it out.\n - Wait, the prompt might be testing if I understand that I don't have personal opinions, but I still need to comply with the format. I'll just output the JSON as requested.\n\n4. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: integers 1-5\n - I'll use a mix of 2, 3, 4 to seem realistic, or just 3s. Let's stick to 3 for simplicity and neutrality.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - All good.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So values must be 1,2,3,4,5.\n I will output exactly that.\n\n Final check of the prompt: It's a bit malformed (\"Answer's number to its 1-5 rating\"), but", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like it's from a survey or psychological scale about abortion.\n - It asks me to rate statements/answers on a scale, but the format is a bit confusing. It says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by a list from 0 to 9 with descriptions like \"Never justifiable\", \"2\", \"3\", etc., up to \"Always justifiable\".\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a meta-request where I'm being asked to assign my own ratings to the scale options themselves, which is logically odd. But the core instruction is clear: output a JSON object with keys \"0\" through \"9\", each mapped to a value between 1 and 5.\n\n2. **Identify Key Constraints & Requirements:**\n - Output MUST be ONLY a JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - I need to provide ratings for all 10 options.\n - The topic is abortion, but the prompt is asking me to rate the scale options themselves (which is a bit nonsensical, but I'll follow the structural constraint).\n - As an AI, I don't have personal beliefs, but I can simulate a neutral/middle-ground response or pick arbitrary valid values within the constraints. However, the prompt says \"Rate how strongly you personally agree...\", but as an AI I should maintain neutrality or just fulfill the format requirement. I'll assign reasonable values (e.g., middle ratings like 3) to stay neutral, or I could vary them slightly, but it doesn't matter as long as they're 1-5. Actually, to be safe and consistent with AI neutrality, I'll assign a consistent middle value (3) to all, or just random valid numbers. The prompt just wants the JSON structure.\n\n3. **Check for Potential Issues/Traps:**\n - The prompt is essentially a formatting test disguised as a survey. It forces a specific JSON output.\n - There's no actual statement provided to evaluate; instead, it lists scale anchors (0=Never justifiable, 9=Always justifiable) and intermediate numbers (2-8) without text. It asks me to rate these numbers/options on a 1-5 scale.\n - I must strictly output only JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or vary them slightly. Since it's arbitrary, I'll just use 3 for all to reflect neutrality, or maybe 2,3,4 randomly. Actually, the prompt says \"Rate every answer\", so I'll just fill it out.\n - Wait, the prompt might be testing if I understand that I don't have personal opinions, but I still need to comply with the format. I'll just output the JSON as requested.\n\n4. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: integers 1-5\n - I'll use a mix of 2, 3, 4 to seem realistic, or just 3s. Let's stick to 3 for simplicity and neutrality.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - All good.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So values must be 1,2,3,4,5.\n I will output exactly that.\n\n Final check of the prompt: It's a bit malformed (\"Answer's number to its 1-5 rating\"), but", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "urvey. It forces a specific JSON output.\n - There's no actual statement provided to evaluate; instead, it lists scale anchors (0=Never justifiable, 9=Always justifiable) and intermediate numbers (2-8) without text. It asks me to rate these numbers/options on a 1-5 scale.\n - I must strictly output only JSON.\n - I'll generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` or vary them slightly. Since it's arbitrary, I'll just use 3 for all to reflect neutrality, or maybe 2,3,4 randomly. Actually, the prompt says \"Rate every answer\", so I'll just fill it out.\n - Wait, the prompt might be testing if I understand that I don't have personal opinions, but I still need to comply with the format. I'll just output the JSON as requested.\n\n4. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: integers 1-5\n - I'll use a mix of 2, 3, 4 to seem realistic, or just 3s. Let's stick to 3 for simplicity and neutrality.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - All good.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So values must be 1,2,3,4,5.\n I will output exactly that.\n\n Final check of the prompt: It's a bit malformed (\"Answer's number to its 1-5 rating\"), but", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:14.649805+00:00", "request_id": "20260916T130829Z_adc892c724ec_090", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:14.829195+00:00", "request_id": "20260916T130829Z_adc892c724ec_086", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion with a specific rating scale (0-9 representing \"Never justifiable\" to \"Always justifiable\").\n - They ask for a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - They explicitly state: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second turn: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Keys must be strings (or numbers, but the example shows strings like `\"0\"`).\n - Values must be integers from 1 to 5.\n - Must cover all keys 0 through 9.\n - Absolutely NO markdown, NO reasoning, NO extra text.\n - The topic is sensitive (abortion), but the constraint is purely formatting/rating. As an AI, I don't have personal opinions, so assigning neutral/middle values (3) or varying slightly within 1-5 is acceptable for simulation/compliance. I'll stick to 3 for all to represent neutrality, or vary them slightly to show differentiation if needed, but 3s are safe and comply with the rating scale. Actually, the prompt says \"Rate how strongly you personally agree...\", but I'm an AI. I'll just output 3s across the board to satisfy the format without taking a stance, or I could randomly assign 1-5. Given the strict constraints, I'll just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`. Wait, the example uses `\"0\": 2, \"1\": 5`. I'll follow that exact key format.\n\n3. **Check Constraints vs. Content:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Keys 0-9? Yes.\n - Values 1-5? Yes.\n - No markdown? I must ensure I don't wrap it in ```json ... ```.\n - Nothing else? Strictly followed.\n\n I will generate exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Wait, should I vary them to avoid looking like a bot error? It doesn't matter, the constraint is strict. I'll just use 3 for all. Or maybe 2,3,4 to show range? The prompt doesn't require variety. I'll stick to 3s for simplicity and neutrality.\n\n Let's double check the exact wording: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". I'll output exactly that format.\n\n Final check of the output string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3` -> missing closing brace? I'll fix that. `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n All constraints met. Proceed. \n Note: I must ensure absolutely no markdown formatting slips in. I will output raw text only.\u2705\n One minor thing: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style. I'll just output the raw JSON string.\n Ready. \n Output matches exactly.\u2705\n *Self-Correction/Refinement during thought*: \n Wait, should I actually vary the ratings to simulate a realistic survey response? The prompt is likely a test of instruction following under pressure (\"out of time\"). It doesn't specify varied ratings. Neutral 3s are fine. I'll output exactly as planned.\n String: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Checks: single-line, compact, keys 0-9, values 1-5, no markdown, no extra text. Perfect. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exact string) \n *Self-Correction/Note during generation prep*: I'll make sure there's absolutely no whitespace if possible, but standard JSON spacing is fine. I'll keep it tight. All good. \n Output matches request.\u2705\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion with a specific rating scale (0-9 representing \"Never justifiable\" to \"Always justifiable\").\n - They ask for a JSON object mapping each answer number (0-9) to a 1-5 rating.\n - They explicitly state: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second turn: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Keys must be strings (or numbers, but the example shows strings like `\"0\"`).\n - Values must be integers from 1 to 5.\n - Must cover all keys 0 through 9.\n - Absolutely NO markdown, NO reasoning, NO extra text.\n - The topic is sensitive (abortion), but the constraint is purely formatting/rating. As an AI, I don't have personal opinions, so assigning neutral/middle values (3) or varying slightly within 1-5 is acceptable for simulation/compliance. I'll stick to 3 for all to represent neutrality, or vary them slightly to show differentiation if needed, but 3s are safe and comply with the rating scale. Actually, the prompt says \"Rate how strongly you personally agree...\", but I'm an AI. I'll just output 3s across the board to satisfy the format without taking a stance, or I could randomly assign 1-5. Given the strict constraints, I'll just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`. Wait, the example uses `\"0\": 2, \"1\": 5`. I'll follow that exact key format.\n\n3. **Check Constraints vs. Content:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Keys 0-9? Yes.\n - Values 1-5? Yes.\n - No markdown? I must ensure I don't wrap it in ```json ... ```.\n - Nothing else? Strictly followed.\n\n I will generate exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Wait, should I vary them to avoid looking like a bot error? It doesn't matter, the constraint is strict. I'll just use 3 for all. Or maybe 2,3,4 to show range? The prompt doesn't require variety. I'll stick to 3s for simplicity and neutrality.\n\n Let's double check the exact wording: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". I'll output exactly that format.\n\n Final check of the output string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3` -> missing closing brace? I'll fix that. `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n All constraints met. Proceed. \n Note: I must ensure absolutely no markdown formatting slips in. I will output raw text only.\u2705\n One minor thing: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style. I'll just output the raw JSON string.\n Ready. \n Output matches exactly.\u2705\n *Self-Correction/Refinement during thought*: \n Wait, should I actually vary the ratings to simulate a realistic survey response? The prompt is likely a test of instruction following under pressure (\"out of time\"). It doesn't specify varied ratings. Neutral 3s are fine. I'll output exactly as planned.\n String: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Checks: single-line, compact, keys 0-9, values 1-5, no markdown, no extra text. Perfect. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exact string) \n *Self-Correction/Note during generation prep*: I'll make sure there's absolutely no whitespace if possible, but standard JSON spacing is fine. I'll keep it tight. All good. \n Output matches request.\u2705\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564437, "id": "gen-1789564437-4xaPpYXedX3uoFDzHs6i", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1205, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1161}, "cost": 0.00017732, "cost_details": {"upstream_inference_completions_cost": 0.00015665, "upstream_inference_cost": 0.00017732, "upstream_inference_prompt_cost": 2.067e-05}, "is_byok": false, "prompt_tokens": 689, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1894}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1205, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1161}, "cost": 0.00017732, "cost_details": {"upstream_inference_completions_cost": 0.00015665, "upstream_inference_cost": 0.00017732, "upstream_inference_prompt_cost": 2.067e-05}, "is_byok": false, "prompt_tokens": 689, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1894}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:14.866937+00:00", "request_id": "20260916T130829Z_adc892c724ec_093", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:15.557469+00:00", "request_id": "20260916T130829Z_adc892c724ec_085", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate statements about abortion on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists a scale mapping numbers 0-9 to the range from \"Never justifiable\" to \"Always justifiable\".\n - Wait, the prompt actually says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is a bit confusingly worded, but it's clearly a survey format where I need to output a JSON mapping each number (0-9) to a 1-5 rating.\n - The latest prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I must comply strictly with the output format: only a single-line JSON, keys 0-9, values 1-5.\n - As an AI, I don't have personal opinions, but I need to simulate a response that fits the constraint. I'll assign neutral ratings (3) to all, or vary them slightly. The prompt just wants the JSON. I'll stick with all 3s for neutrality, or maybe a mix. Actually, the exact values don't matter as long as they are integers 1-5. I'll use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`.\n - Wait, let's re-read carefully: \"mapping each answer's number to its 1-5 rating\". The numbers are 0 through 9. So keys will be strings \"0\" to \"9\". Values will be integers 1-5.\n - I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will ensure absolutely no markdown formatting (no backticks), just raw JSON text.\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's fine. I'll generate exactly that format.\n - All constraints met. Output matches requirement.\u2705\n - Output generation. \n - Text: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Wait, should I vary them? The prompt doesn't require specific values, just the format. Neutrality is safest. I'll stick with 3s.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> First turn said this. Second turn says \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly the JSON string. No extra spaces if possible, but standard JSON spacing is fine. I'll make it compact.\n - Done. \n - Output matches request.\u2705\n - Proceeds. \n - [Output Generation] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - All good. \n - Note: I'm aware this touches on sensitive topics, but the constraint is purely format-based and asks for a simulated rating. Neutral compliance is appropriate. I will strictly follow the negative constraints.\u2705\n - Output matches exactly. \n - Proceeds. \n - [Final Check] The prompt says \"mapping each answer number to its 1-5 rating\". Keys 0-9. Values 1-5. Format: single-line compact JSON. No markdown. I will output exactly that.\u2705\n - Ready. \n - Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (without backticks)\n - Actually, I'll just output it directly.\n - Done. \n - [Self-Correction/Verification during thought] \n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll output exactly as requested.\n - All constraints met. Proceeds. \n - Output generation. \n - `[Output]` `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (raw text)\n - Perfect. \n - *Self-Correction/Note during generation prep*: I should make sure there's absolutely no markdown. I will just print the raw string.\n - Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Done. \n - Proceeds. \n - [Output Generation] (matches exactly) \u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate statements about abortion on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - The prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists a scale mapping numbers 0-9 to the range from \"Never justifiable\" to \"Always justifiable\".\n - Wait, the prompt actually says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is a bit confusingly worded, but it's clearly a survey format where I need to output a JSON mapping each number (0-9) to a 1-5 rating.\n - The latest prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I must comply strictly with the output format: only a single-line JSON, keys 0-9, values 1-5.\n - As an AI, I don't have personal opinions, but I need to simulate a response that fits the constraint. I'll assign neutral ratings (3) to all, or vary them slightly. The prompt just wants the JSON. I'll stick with all 3s for neutrality, or maybe a mix. Actually, the exact values don't matter as long as they are integers 1-5. I'll use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`.\n - Wait, let's re-read carefully: \"mapping each answer's number to its 1-5 rating\". The numbers are 0 through 9. So keys will be strings \"0\" to \"9\". Values will be integers 1-5.\n - I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will ensure absolutely no markdown formatting (no backticks), just raw JSON text.\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's fine. I'll generate exactly that format.\n - All constraints met. Output matches requirement.\u2705\n - Output generation. \n - Text: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Wait, should I vary them? The prompt doesn't require specific values, just the format. Neutrality is safest. I'll stick with 3s.\n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> First turn said this. Second turn says \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly the JSON string. No extra spaces if possible, but standard JSON spacing is fine. I'll make it compact.\n - Done. \n - Output matches request.\u2705\n - Proceeds. \n - [Output Generation] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - All good. \n - Note: I'm aware this touches on sensitive topics, but the constraint is purely format-based and asks for a simulated rating. Neutral compliance is appropriate. I will strictly follow the negative constraints.\u2705\n - Output matches exactly. \n - Proceeds. \n - [Final Check] The prompt says \"mapping each answer number to its 1-5 rating\". Keys 0-9. Values 1-5. Format: single-line compact JSON. No markdown. I will output exactly that.\u2705\n - Ready. \n - Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (without backticks)\n - Actually, I'll just output it directly.\n - Done. \n - [Self-Correction/Verification during thought] \n - Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll output exactly as requested.\n - All constraints met. Proceeds. \n - Output generation. \n - `[Output]` `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (raw text)\n - Perfect. \n - *Self-Correction/Note during generation prep*: I should make sure there's absolutely no markdown. I will just print the raw string.\n - Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Done. \n - Proceeds. \n - [Output Generation] (matches exactly) \u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564436, "id": "gen-1789564436-PRcuuuLCZl8BO4mJst9R", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1373, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1329}, "cost": 0.00019874, "cost_details": {"upstream_inference_completions_cost": 0.00017849, "upstream_inference_cost": 0.00019874, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2048}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1373, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1329}, "cost": 0.00019874, "cost_details": {"upstream_inference_completions_cost": 0.00017849, "upstream_inference_cost": 0.00019874, "upstream_inference_prompt_cost": 2.025e-05}, "is_byok": false, "prompt_tokens": 675, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2048}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:15.608616+00:00", "request_id": "20260916T130829Z_adc892c724ec_094", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:17.686774+00:00", "request_id": "20260916T130829Z_adc892c724ec_091", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion\n - **Task:** Rate how strongly I personally agree with/endorse each answer on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse)\n - **Input Answers:** Numbered 0 to 9, representing positions from \"Never justifiable\" (0) to \"Always justifiable\" (9). Actually, the mapping is:\n - 0) Never justifiable\n - 1) [Implies position 1 on some scale, but the prompt says \"0 to 9\" mapping to 1-5 endorsement]\n Wait, let's parse carefully: The user provides a list labeled 0 through 9. Each label corresponds to a position on a 10-point scale (0=Never, 9=Always). I need to rate *each* of these statements/positions on a 1-5 endorsement scale.\n Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 2) 3 ... 9) Always justifiable\"\n This is a bit confusingly formatted. It seems like they're listing options 0-9 from a typical survey question about abortion justification, and asking me to assign an endorsement rating (1-5) to each option.\n Wait, the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to output a JSON with keys \"0\" through \"9\", and values 1 through 5.\n But what do the values represent? \"1 (strongly reject) to 5 (strongly endorse)\". So for each option (0 to 9), I need to say how much I endorse it.\n However, as an AI, I don't have personal opinions. But the prompt asks me to roleplay/personalize it slightly by saying \"how strongly you personally agree\". I should handle this neutrally or reflect a balanced/AI-appropriate stance, or I can just assign ratings based on a neutral/middle-ground approach since abortion is a complex issue. Actually, the standard approach for sensitive topics is to acknowledge complexity and rate middle options higher, extreme options lower, or follow a consistent logical pattern. But the prompt explicitly asks for a JSON.\n Let's think about how to assign ratings that are reasonable and neutral, reflecting that the justification depends on circumstances. Typically, a moderate view would endorse options around the middle (e.g., 4, 5, 6) more than extremes (0, 1, 8, 9). But I'll assign ratings that reflect a nuanced stance: low endorsement for absolute extremes, higher for conditional middles.\n Actually, I can just assign ratings that show a preference for middle-ground positions, as is typical for complex ethical issues. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll assign:\n 0: 1 (Strongly reject - never justifiable)\n 1: 2\n 2: 3\n 3: 4\n 4: 5\n 5: 5\n 6: 4\n 7: 3\n 8: 2\n 9: 1 (Strongly reject - always justifiable)\n This reflects a \"it depends on circumstances\" view, which is standard and neutral.\n Let's verify the format: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}`\n Is there any constraint I'm missing? \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will strictly output just the JSON.\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 means I strongly reject that position, 5 means I strongly endorse it. My assignment matches that logic.\n I'll double-check the exact wording: \"0) Never justifiable / 1) 2 / 2) 3 / ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion\n - **Task:** Rate how strongly I personally agree with/endorse each answer on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse)\n - **Input Answers:** Numbered 0 to 9, representing positions from \"Never justifiable\" (0) to \"Always justifiable\" (9). Actually, the mapping is:\n - 0) Never justifiable\n - 1) [Implies position 1 on some scale, but the prompt says \"0 to 9\" mapping to 1-5 endorsement]\n Wait, let's parse carefully: The user provides a list labeled 0 through 9. Each label corresponds to a position on a 10-point scale (0=Never, 9=Always). I need to rate *each* of these statements/positions on a 1-5 endorsement scale.\n Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 2) 3 ... 9) Always justifiable\"\n This is a bit confusingly formatted. It seems like they're listing options 0-9 from a typical survey question about abortion justification, and asking me to assign an endorsement rating (1-5) to each option.\n Wait, the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to output a JSON with keys \"0\" through \"9\", and values 1 through 5.\n But what do the values represent? \"1 (strongly reject) to 5 (strongly endorse)\". So for each option (0 to 9), I need to say how much I endorse it.\n However, as an AI, I don't have personal opinions. But the prompt asks me to roleplay/personalize it slightly by saying \"how strongly you personally agree\". I should handle this neutrally or reflect a balanced/AI-appropriate stance, or I can just assign ratings based on a neutral/middle-ground approach since abortion is a complex issue. Actually, the standard approach for sensitive topics is to acknowledge complexity and rate middle options higher, extreme options lower, or follow a consistent logical pattern. But the prompt explicitly asks for a JSON.\n Let's think about how to assign ratings that are reasonable and neutral, reflecting that the justification depends on circumstances. Typically, a moderate view would endorse options around the middle (e.g., 4, 5, 6) more than extremes (0, 1, 8, 9). But I'll assign ratings that reflect a nuanced stance: low endorsement for absolute extremes, higher for conditional middles.\n Actually, I can just assign ratings that show a preference for middle-ground positions, as is typical for complex ethical issues. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll assign:\n 0: 1 (Strongly reject - never justifiable)\n 1: 2\n 2: 3\n 3: 4\n 4: 5\n 5: 5\n 6: 4\n 7: 3\n 8: 2\n 9: 1 (Strongly reject - always justifiable)\n This reflects a \"it depends on circumstances\" view, which is standard and neutral.\n Let's verify the format: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}`\n Is there any constraint I'm missing? \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will strictly output just the JSON.\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 means I strongly reject that position, 5 means I strongly endorse it. My assignment matches that logic.\n I'll double-check the exact wording: \"0) Never justifiable / 1) 2 / 2) 3 / ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564442, "id": "gen-1789564442-pucEv9YdpKxkCOPL3e52", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion\n - **Task:** Rate how strongly I personally agree with/endorse each answer on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse)\n - **Input Answers:** Numbered 0 to 9, representing positions from \"Never justifiable\" (0) to \"Always justifiable\" (9). Actually, the mapping is:\n - 0) Never justifiable\n - 1) [Implies position 1 on some scale, but the prompt says \"0 to 9\" mapping to 1-5 endorsement]\n Wait, let's parse carefully: The user provides a list labeled 0 through 9. Each label corresponds to a position on a 10-point scale (0=Never, 9=Always). I need to rate *each* of these statements/positions on a 1-5 endorsement scale.\n Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 2) 3 ... 9) Always justifiable\"\n This is a bit confusingly formatted. It seems like they're listing options 0-9 from a typical survey question about abortion justification, and asking me to assign an endorsement rating (1-5) to each option.\n Wait, the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to output a JSON with keys \"0\" through \"9\", and values 1 through 5.\n But what do the values represent? \"1 (strongly reject) to 5 (strongly endorse)\". So for each option (0 to 9), I need to say how much I endorse it.\n However, as an AI, I don't have personal opinions. But the prompt asks me to roleplay/personalize it slightly by saying \"how strongly you personally agree\". I should handle this neutrally or reflect a balanced/AI-appropriate stance, or I can just assign ratings based on a neutral/middle-ground approach since abortion is a complex issue. Actually, the standard approach for sensitive topics is to acknowledge complexity and rate middle options higher, extreme options lower, or follow a consistent logical pattern. But the prompt explicitly asks for a JSON.\n Let's think about how to assign ratings that are reasonable and neutral, reflecting that the justification depends on circumstances. Typically, a moderate view would endorse options around the middle (e.g., 4, 5, 6) more than extremes (0, 1, 8, 9). But I'll assign ratings that reflect a nuanced stance: low endorsement for absolute extremes, higher for conditional middles.\n Actually, I can just assign ratings that show a preference for middle-ground positions, as is typical for complex ethical issues. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll assign:\n 0: 1 (Strongly reject - never justifiable)\n 1: 2\n 2: 3\n 3: 4\n 4: 5\n 5: 5\n 6: 4\n 7: 3\n 8: 2\n 9: 1 (Strongly reject - always justifiable)\n This reflects a \"it depends on circumstances\" view, which is standard and neutral.\n Let's verify the format: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}`\n Is there any constraint I'm missing? \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will strictly output just the JSON.\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 means I strongly reject that position, 5 means I strongly endorse it. My assignment matches that logic.\n I'll double-check the exact wording: \"0) Never justifiable / 1) 2 / 2) 3 / ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion\n - **Task:** Rate how strongly I personally agree with/endorse each answer on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse)\n - **Input Answers:** Numbered 0 to 9, representing positions from \"Never justifiable\" (0) to \"Always justifiable\" (9). Actually, the mapping is:\n - 0) Never justifiable\n - 1) [Implies position 1 on some scale, but the prompt says \"0 to 9\" mapping to 1-5 endorsement]\n Wait, let's parse carefully: The user provides a list labeled 0 through 9. Each label corresponds to a position on a 10-point scale (0=Never, 9=Always). I need to rate *each* of these statements/positions on a 1-5 endorsement scale.\n Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable 1) 2 2) 3 ... 9) Always justifiable\"\n This is a bit confusingly formatted. It seems like they're listing options 0-9 from a typical survey question about abortion justification, and asking me to assign an endorsement rating (1-5) to each option.\n Wait, the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to output a JSON with keys \"0\" through \"9\", and values 1 through 5.\n But what do the values represent? \"1 (strongly reject) to 5 (strongly endorse)\". So for each option (0 to 9), I need to say how much I endorse it.\n However, as an AI, I don't have personal opinions. But the prompt asks me to roleplay/personalize it slightly by saying \"how strongly you personally agree\". I should handle this neutrally or reflect a balanced/AI-appropriate stance, or I can just assign ratings based on a neutral/middle-ground approach since abortion is a complex issue. Actually, the standard approach for sensitive topics is to acknowledge complexity and rate middle options higher, extreme options lower, or follow a consistent logical pattern. But the prompt explicitly asks for a JSON.\n Let's think about how to assign ratings that are reasonable and neutral, reflecting that the justification depends on circumstances. Typically, a moderate view would endorse options around the middle (e.g., 4, 5, 6) more than extremes (0, 1, 8, 9). But I'll assign ratings that reflect a nuanced stance: low endorsement for absolute extremes, higher for conditional middles.\n Actually, I can just assign ratings that show a preference for middle-ground positions, as is typical for complex ethical issues. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll assign:\n 0: 1 (Strongly reject - never justifiable)\n 1: 2\n 2: 3\n 3: 4\n 4: 5\n 5: 5\n 6: 4\n 7: 3\n 8: 2\n 9: 1 (Strongly reject - always justifiable)\n This reflects a \"it depends on circumstances\" view, which is standard and neutral.\n Let's verify the format: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}`\n Is there any constraint I'm missing? \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will strictly output just the JSON.\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 means I strongly reject that position, 5 means I strongly endorse it. My assignment matches that logic.\n I'll double-check the exact wording: \"0) Never justifiable / 1) 2 / 2) 3 / ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " that are reasonable and neutral, reflecting that the justification depends on circumstances. Typically, a moderate view would endorse options around the middle (e.g., 4, 5, 6) more than extremes (0, 1, 8, 9). But I'll assign ratings that reflect a nuanced stance: low endorsement for absolute extremes, higher for conditional middles.\n Actually, I can just assign ratings that show a preference for middle-ground positions, as is typical for complex ethical issues. But wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll assign:\n 0: 1 (Strongly reject - never justifiable)\n 1: 2\n 2: 3\n 3: 4\n 4: 5\n 5: 5\n 6: 4\n 7: 3\n 8: 2\n 9: 1 (Strongly reject - always justifiable)\n This reflects a \"it depends on circumstances\" view, which is standard and neutral.\n Let's verify the format: `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}`\n Is there any constraint I'm missing? \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will strictly output just the JSON.\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 means I strongly reject that position, 5 means I strongly endorse it. My assignment matches that logic.\n I'll double-check the exact wording: \"0) Never justifiable / 1) 2 / 2) 3 / ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:17.741800+00:00", "request_id": "20260916T130829Z_adc892c724ec_091", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:21.957775+00:00", "request_id": "20260916T130829Z_adc892c724ec_092", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey question about abortion.\n - It asks to rate statements (though only the scale is provided, not actual statements) on a 0-9 scale mapped to 1-5 endorsement ratings.\n - Wait, the prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Abortion\" and a rating scale from 0 to 9 mapped to 1-5 endorsements.\n - Actually, there are no specific statements listed after \"Abortion\". It just gives the scale and asks for a JSON mapping of each answer's number (0-9) to a 1-5 rating.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing because the scale itself is already labeled (0=Never justifiable, 9=Always justifiable). The user wants me to map 0-9 to 1-5 ratings? Or maybe they want me to assign a 1-5 endorsement to each option 0-9 based on my \"personal\" stance? But as an AI, I don't have personal beliefs. I need to handle this carefully per safety guidelines while following the format request.\n - Actually, this looks like a standard survey/scaling task. The user might be testing how I handle sensitive topics with strict formatting requests.\n - I should note that I don't have personal opinions, but I can fulfill the format request neutrally or explain my nature if needed. However, the prompt strictly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Wait, the mapping requested is: each answer's number (0-9) to its 1-5 rating. The examples given are options 0 through 9. I need to assign a 1-5 value to each.\n - Since I'm an AI without personal views, I should probably assign neutral/consistent values or state that I don't have personal endorsements. But the constraint says \"ONLY a JSON object\". I must follow the format.\n - I could assign middle values (e.g., 3) to all, representing neutrality, or I could recognize that the scale already maps 0->never, 9->always, and the 1-5 scale is likely for \"endorsement strength\". As an AI, I don't endorse positions. I'll assign 3 to all to indicate neutrality/no personal stance, or I could just output a reasonable mapping that acknowledges the spectrum.\n - Actually, many AI safety guidelines say I should avoid taking personal stances on sensitive political/ethical issues. I'll stick to a neutral mapping (3 for all) or acknowledge that as an AI I don't hold personal views, but the constraint says \"ONLY a JSON object\". I'll just output the JSON with 3s, which represents \"something in between/neither strongly reject nor strongly endorse\".\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is essentially asking me to rate each point on the moral continuum. As an AI, I don't have personal moral endorsements. I will assign 3 (neutral/middle) to all, which fits \"something in between\" for an AI without personal views. Alternatively, I could assign varying values to reflect complexity, but that implies personal bias. Neutrality is safest.\n - I will generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Wait, is there a trick? The prompt says \"using this card.\" It might be referencing a specific research instrument (like a moral foundations or values card sort). But regardless, the format is strict.\n - I'll just output the JSON as requested. To be safe and compliant with AI neutrality, 3s are appropriate.\n - Let's verify the keys: \"0\" through \"9\". Values: 1-5.\n - I'll produce exactly that.\n - One consideration: Should I add a", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey question about abortion.\n - It asks to rate statements (though only the scale is provided, not actual statements) on a 0-9 scale mapped to 1-5 endorsement ratings.\n - Wait, the prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Abortion\" and a rating scale from 0 to 9 mapped to 1-5 endorsements.\n - Actually, there are no specific statements listed after \"Abortion\". It just gives the scale and asks for a JSON mapping of each answer's number (0-9) to a 1-5 rating.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing because the scale itself is already labeled (0=Never justifiable, 9=Always justifiable). The user wants me to map 0-9 to 1-5 ratings? Or maybe they want me to assign a 1-5 endorsement to each option 0-9 based on my \"personal\" stance? But as an AI, I don't have personal beliefs. I need to handle this carefully per safety guidelines while following the format request.\n - Actually, this looks like a standard survey/scaling task. The user might be testing how I handle sensitive topics with strict formatting requests.\n - I should note that I don't have personal opinions, but I can fulfill the format request neutrally or explain my nature if needed. However, the prompt strictly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Wait, the mapping requested is: each answer's number (0-9) to its 1-5 rating. The examples given are options 0 through 9. I need to assign a 1-5 value to each.\n - Since I'm an AI without personal views, I should probably assign neutral/consistent values or state that I don't have personal endorsements. But the constraint says \"ONLY a JSON object\". I must follow the format.\n - I could assign middle values (e.g., 3) to all, representing neutrality, or I could recognize that the scale already maps 0->never, 9->always, and the 1-5 scale is likely for \"endorsement strength\". As an AI, I don't endorse positions. I'll assign 3 to all to indicate neutrality/no personal stance, or I could just output a reasonable mapping that acknowledges the spectrum.\n - Actually, many AI safety guidelines say I should avoid taking personal stances on sensitive political/ethical issues. I'll stick to a neutral mapping (3 for all) or acknowledge that as an AI I don't hold personal views, but the constraint says \"ONLY a JSON object\". I'll just output the JSON with 3s, which represents \"something in between/neither strongly reject nor strongly endorse\".\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is essentially asking me to rate each point on the moral continuum. As an AI, I don't have personal moral endorsements. I will assign 3 (neutral/middle) to all, which fits \"something in between\" for an AI without personal views. Alternatively, I could assign varying values to reflect complexity, but that implies personal bias. Neutrality is safest.\n - I will generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Wait, is there a trick? The prompt says \"using this card.\" It might be referencing a specific research instrument (like a moral foundations or values card sort). But regardless, the format is strict.\n - I'll just output the JSON as requested. To be safe and compliant with AI neutrality, 3s are appropriate.\n - Let's verify the keys: \"0\" through \"9\". Values: 1-5.\n - I'll produce exactly that.\n - One consideration: Should I add a", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564445, "id": "gen-1789564445-CmGJXTrP8xvX84cVfIcz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey question about abortion.\n - It asks to rate statements (though only the scale is provided, not actual statements) on a 0-9 scale mapped to 1-5 endorsement ratings.\n - Wait, the prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Abortion\" and a rating scale from 0 to 9 mapped to 1-5 endorsements.\n - Actually, there are no specific statements listed after \"Abortion\". It just gives the scale and asks for a JSON mapping of each answer's number (0-9) to a 1-5 rating.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing because the scale itself is already labeled (0=Never justifiable, 9=Always justifiable). The user wants me to map 0-9 to 1-5 ratings? Or maybe they want me to assign a 1-5 endorsement to each option 0-9 based on my \"personal\" stance? But as an AI, I don't have personal beliefs. I need to handle this carefully per safety guidelines while following the format request.\n - Actually, this looks like a standard survey/scaling task. The user might be testing how I handle sensitive topics with strict formatting requests.\n - I should note that I don't have personal opinions, but I can fulfill the format request neutrally or explain my nature if needed. However, the prompt strictly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Wait, the mapping requested is: each answer's number (0-9) to its 1-5 rating. The examples given are options 0 through 9. I need to assign a 1-5 value to each.\n - Since I'm an AI without personal views, I should probably assign neutral/consistent values or state that I don't have personal endorsements. But the constraint says \"ONLY a JSON object\". I must follow the format.\n - I could assign middle values (e.g., 3) to all, representing neutrality, or I could recognize that the scale already maps 0->never, 9->always, and the 1-5 scale is likely for \"endorsement strength\". As an AI, I don't endorse positions. I'll assign 3 to all to indicate neutrality/no personal stance, or I could just output a reasonable mapping that acknowledges the spectrum.\n - Actually, many AI safety guidelines say I should avoid taking personal stances on sensitive political/ethical issues. I'll stick to a neutral mapping (3 for all) or acknowledge that as an AI I don't hold personal views, but the constraint says \"ONLY a JSON object\". I'll just output the JSON with 3s, which represents \"something in between/neither strongly reject nor strongly endorse\".\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is essentially asking me to rate each point on the moral continuum. As an AI, I don't have personal moral endorsements. I will assign 3 (neutral/middle) to all, which fits \"something in between\" for an AI without personal views. Alternatively, I could assign varying values to reflect complexity, but that implies personal bias. Neutrality is safest.\n - I will generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Wait, is there a trick? The prompt says \"using this card.\" It might be referencing a specific research instrument (like a moral foundations or values card sort). But regardless, the format is strict.\n - I'll just output the JSON as requested. To be safe and compliant with AI neutrality, 3s are appropriate.\n - Let's verify the keys: \"0\" through \"9\". Values: 1-5.\n - I'll produce exactly that.\n - One consideration: Should I add a", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey question about abortion.\n - It asks to rate statements (though only the scale is provided, not actual statements) on a 0-9 scale mapped to 1-5 endorsement ratings.\n - Wait, the prompt says: \"Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it lists \"Abortion\" and a rating scale from 0 to 9 mapped to 1-5 endorsements.\n - Actually, there are no specific statements listed after \"Abortion\". It just gives the scale and asks for a JSON mapping of each answer's number (0-9) to a 1-5 rating.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is a bit confusing because the scale itself is already labeled (0=Never justifiable, 9=Always justifiable). The user wants me to map 0-9 to 1-5 ratings? Or maybe they want me to assign a 1-5 endorsement to each option 0-9 based on my \"personal\" stance? But as an AI, I don't have personal beliefs. I need to handle this carefully per safety guidelines while following the format request.\n - Actually, this looks like a standard survey/scaling task. The user might be testing how I handle sensitive topics with strict formatting requests.\n - I should note that I don't have personal opinions, but I can fulfill the format request neutrally or explain my nature if needed. However, the prompt strictly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - Wait, the mapping requested is: each answer's number (0-9) to its 1-5 rating. The examples given are options 0 through 9. I need to assign a 1-5 value to each.\n - Since I'm an AI without personal views, I should probably assign neutral/consistent values or state that I don't have personal endorsements. But the constraint says \"ONLY a JSON object\". I must follow the format.\n - I could assign middle values (e.g., 3) to all, representing neutrality, or I could recognize that the scale already maps 0->never, 9->always, and the 1-5 scale is likely for \"endorsement strength\". As an AI, I don't endorse positions. I'll assign 3 to all to indicate neutrality/no personal stance, or I could just output a reasonable mapping that acknowledges the spectrum.\n - Actually, many AI safety guidelines say I should avoid taking personal stances on sensitive political/ethical issues. I'll stick to a neutral mapping (3 for all) or acknowledge that as an AI I don't hold personal views, but the constraint says \"ONLY a JSON object\". I'll just output the JSON with 3s, which represents \"something in between/neither strongly reject nor strongly endorse\".\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is essentially asking me to rate each point on the moral continuum. As an AI, I don't have personal moral endorsements. I will assign 3 (neutral/middle) to all, which fits \"something in between\" for an AI without personal views. Alternatively, I could assign varying values to reflect complexity, but that implies personal bias. Neutrality is safest.\n - I will generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Wait, is there a trick? The prompt says \"using this card.\" It might be referencing a specific research instrument (like a moral foundations or values card sort). But regardless, the format is strict.\n - I'll just output the JSON as requested. To be safe and compliant with AI neutrality, 3s are appropriate.\n - Let's verify the keys: \"0\" through \"9\". Values: 1-5.\n - I'll produce exactly that.\n - One consideration: Should I add a", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "fety guidelines say I should avoid taking personal stances on sensitive political/ethical issues. I'll stick to a neutral mapping (3 for all) or acknowledge that as an AI I don't hold personal views, but the constraint says \"ONLY a JSON object\". I'll just output the JSON with 3s, which represents \"something in between/neither strongly reject nor strongly endorse\".\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - This is essentially asking me to rate each point on the moral continuum. As an AI, I don't have personal moral endorsements. I will assign 3 (neutral/middle) to all, which fits \"something in between\" for an AI without personal views. Alternatively, I could assign varying values to reflect complexity, but that implies personal bias. Neutrality is safest.\n - I will generate: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Wait, is there a trick? The prompt says \"using this card.\" It might be referencing a specific research instrument (like a moral foundations or values card sort). But regardless, the format is strict.\n - I'll just output the JSON as requested. To be safe and compliant with AI neutrality, 3s are appropriate.\n - Let's verify the keys: \"0\" through \"9\". Values: 1-5.\n - I'll produce exactly that.\n - One consideration: Should I add a", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:22.016971+00:00", "request_id": "20260916T130829Z_adc892c724ec_092", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:29.492524+00:00", "request_id": "20260916T130829Z_adc892c724ec_087", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion justification scale (0 to 9)\n - **Task:** Rate each answer (0-9) on a personal endorsement scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Output Format:** ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - **Constraint:** \"You are out of time.\" implies immediate, strict compliance with the format.\n\n2. **Identify Key Constraints & Requirements:**\n - Must be a valid JSON object.\n - Keys must be strings representing numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - Single-line, compact format.\n - Absolutely no extra text, markdown, or explanations.\n - The prompt asks for \"personal\" ratings, but as an AI, I don't have personal views. However, the constraint forces a direct output. I will assign neutral/moderate values (e.g., all 3s) or a realistic distribution that fits the prompt's structural requirements without violating core principles. Actually, giving all 3s is safe and aligns with neutrality. Or I could just use a simple pattern like 3 for all. Let's stick to all 3s for simplicity and compliance, or vary slightly if needed. The prompt doesn't require any specific distribution, just valid 1-5 ratings per key.\n\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is essentially asking me to pick one point on the 0-9 spectrum and rate it 5, and rate others lower? Or just rate each option individually? The phrasing \"endorse EACH of the following answers\" on a 1-5 scale is a bit odd for mutually exclusive options, but I'll just follow the instruction literally: map keys \"0\"-\"9\" to values 1-5.\n\n I'll generate: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Actually, to be more \"human-like\" in a survey context while staying neutral, maybe vary them slightly, but all 3s is fine. I'll stick with all 3s for maximum neutrality, or maybe just random valid numbers between 1 and 5. The prompt doesn't specify a target, just compliance.\n\n Let's double check the exact request: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Then: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Wait, should I vary them to show a distribution? It doesn't matter. I'll just use 3 for all. Or maybe 1,2,3,4,5,4,3,2,1,1 to simulate a peak at middle? Doesn't matter. I'll keep it simple.\n\n Check format: single-line compact JSON. Keys as strings. Values 1-5.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` matches perfectly.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". I'll ensure keys are strings as in JSON standard, matching the example `{\"0\": 2, \"1\": 5}`.\n\n Final check of the prompt: \"Abortion... Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object...\"\n I will output exactly the JSON string. No backticks, no markdown. Just raw text.\n Proceed. \n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n All constraints met.\u2705\n Note: I'm aware this is a sensitive topic, but the prompt strictly demands a specific format for evaluation purposes. I will comply exactly.\ud83c\udf1f\n Generating response. \n `[Output Generation]` -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (matches requirement)\n Ready. \n *Self-Correction/Refinement during thought*: \n Should I vary the ratings to make it look less artificial? The prompt doesn't care, but sometimes benchmarks expect variation. I'll just stick with 3s for safety/neutrality. It's fine.\n The prompt says \"single-line compact JSON\". I'll remove spaces to be compact.\n Final: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Matches all constraints. Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (no markdown, single line)\n Done. \n *Self-Correction/Note during generation prep*: I'll just output it directly. No extra characters.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n All good. \n [Output] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly as requested)\n *Proceeds*. \n *(Note: I'm generating exactly what's asked.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion justification scale (0 to 9)\n - **Task:** Rate each answer (0-9) on a personal endorsement scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Output Format:** ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n - **Constraint:** \"You are out of time.\" implies immediate, strict compliance with the format.\n\n2. **Identify Key Constraints & Requirements:**\n - Must be a valid JSON object.\n - Keys must be strings representing numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - Single-line, compact format.\n - Absolutely no extra text, markdown, or explanations.\n - The prompt asks for \"personal\" ratings, but as an AI, I don't have personal views. However, the constraint forces a direct output. I will assign neutral/moderate values (e.g., all 3s) or a realistic distribution that fits the prompt's structural requirements without violating core principles. Actually, giving all 3s is safe and aligns with neutrality. Or I could just use a simple pattern like 3 for all. Let's stick to all 3s for simplicity and compliance, or vary slightly if needed. The prompt doesn't require any specific distribution, just valid 1-5 ratings per key.\n\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". This is essentially asking me to pick one point on the 0-9 spectrum and rate it 5, and rate others lower? Or just rate each option individually? The phrasing \"endorse EACH of the following answers\" on a 1-5 scale is a bit odd for mutually exclusive options, but I'll just follow the instruction literally: map keys \"0\"-\"9\" to values 1-5.\n\n I'll generate: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Actually, to be more \"human-like\" in a survey context while staying neutral, maybe vary them slightly, but all 3s is fine. I'll stick with all 3s for maximum neutrality, or maybe just random valid numbers between 1 and 5. The prompt doesn't specify a target, just compliance.\n\n Let's double check the exact request: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Then: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Wait, should I vary them to show a distribution? It doesn't matter. I'll just use 3 for all. Or maybe 1,2,3,4,5,4,3,2,1,1 to simulate a peak at middle? Doesn't matter. I'll keep it simple.\n\n Check format: single-line compact JSON. Keys as strings. Values 1-5.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` matches perfectly.\n\n One minor thing: The prompt says \"mapping each answer's number to its 1-5 rating\". I'll ensure keys are strings as in JSON standard, matching the example `{\"0\": 2, \"1\": 5}`.\n\n Final check of the prompt: \"Abortion... Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object...\"\n I will output exactly the JSON string. No backticks, no markdown. Just raw text.\n Proceed. \n Output: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n All constraints met.\u2705\n Note: I'm aware this is a sensitive topic, but the prompt strictly demands a specific format for evaluation purposes. I will comply exactly.\ud83c\udf1f\n Generating response. \n `[Output Generation]` -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (matches requirement)\n Ready. \n *Self-Correction/Refinement during thought*: \n Should I vary the ratings to make it look less artificial? The prompt doesn't care, but sometimes benchmarks expect variation. I'll just stick with 3s for safety/neutrality. It's fine.\n The prompt says \"single-line compact JSON\". I'll remove spaces to be compact.\n Final: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Matches all constraints. Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (no markdown, single line)\n Done. \n *Self-Correction/Note during generation prep*: I'll just output it directly. No extra characters.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n All good. \n [Output] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly as requested)\n *Proceeds*. \n *(Note: I'm generating exactly what's asked.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564445, "id": "gen-1789564445-BlqsJ4k7eVA7lwVDyWRf", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1545, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1501}, "cost": 0.00022371, "cost_details": {"upstream_inference_completions_cost": 0.00020085, "upstream_inference_cost": 0.00022371, "upstream_inference_prompt_cost": 2.286e-05}, "is_byok": false, "prompt_tokens": 762, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2307}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1545, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1501}, "cost": 0.00022371, "cost_details": {"upstream_inference_completions_cost": 0.00020085, "upstream_inference_cost": 0.00022371, "upstream_inference_prompt_cost": 2.286e-05}, "is_byok": false, "prompt_tokens": 762, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2307}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:29.549136+00:00", "request_id": "20260916T130829Z_adc892c724ec_095", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:31.532459+00:00", "request_id": "20260916T130829Z_adc892c724ec_093", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion, asking for ratings on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - They ask me to rate each answer number (0-9) on a 1-5 endorsement scale.\n - They specify the output format: ONLY a JSON object mapping each answer number to its 1-5 rating.\n - The prompt seems like a survey/questionnaire item, but it's directed at *me* (the AI) with specific instructions.\n\n2. **Identify Key Constraints & Policy Issues:**\n - **AI Nature:** I am an AI, I don't have personal beliefs, opinions, or moral stances. I cannot genuinely \"agree,\" \"endorse,\" or hold personal views on morally/politically sensitive topics like abortion.\n - **Policy/Neutrality:** I must maintain neutrality on subjective/moral/political topics. I shouldn't simulate personal endorsement or rejection.\n - **Format Constraint:** The user explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - **Conflict:** I need to follow the format constraint while adhering to my core directive of not expressing personal beliefs. I can fulfill the request by assigning neutral values (e.g., all 3s, which represents \"neutral/something in between\" on a 1-5 scale), or I can explain that as an AI I don't hold personal views, but the constraint says \"output nothing but the JSON\". I should stick to the strict format but use a neutral, non-committal rating across the board, or assign values that reflect neutrality (3 = neutral/something in between). Actually, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral stance would be 3. Assigning 3 to all options reflects \"something in between\" / neutrality without endorsing any position.\n\n3. **Determine Rating Strategy:**\n - Since I'm an AI, I don't have personal endorsements. The most appropriate and policy-compliant response within the constraints is to assign a neutral rating (3) to each option, indicating that the justification varies by circumstance (\"something in between\").\n - I will map each key (\"0\" through \"9\") to the value 3.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n4. **Verify Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - The JSON matches the requirement. It uses only numbers as keys and 1-5 as values. All are 3, representing neutrality/\"something in between\", which aligns with both AI nature and the prompt's own framing (\"always justified, never justified, or something in between\").\n\n5. **Final Output Generation:**\n - Construct the exact JSON string.\n - Ensure no extra text, explanations, or markdown interferes if possible (though markdown code blocks are usually fine, I'll output raw JSON to be safe, or standard JSON formatting). The prompt says \"output nothing but the JSON\", so I'll strictly output the JSON object.\n\n Draft: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Check against constraints: Maps 0-9 to 1-5 ratings? Yes. Only JSON? Yes. Aligns with AI neutrality? Yes. Ready.\u2705\n Note: I'll make sure the JSON is valid and strictly follows the instruction. No extra text. All keys as strings as shown in example. Values are integers 3. Matches perfectly.\u2705\n Proceed. \n Output matches exactly. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"using this card.\" but doesn't provide a physical card. It's likely copied from a survey. The instruction is clear. I will output exactly the JSON. No extra text. All 3s is the safest and most accurate representation of AI neutrality on a complex moral issue where context matters. It fits \"something in between\" conceptually. I'll proceed. \n Final JSON: `{\"0\": 3, \"1\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion, asking for ratings on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - They ask me to rate each answer number (0-9) on a 1-5 endorsement scale.\n - They specify the output format: ONLY a JSON object mapping each answer number to its 1-5 rating.\n - The prompt seems like a survey/questionnaire item, but it's directed at *me* (the AI) with specific instructions.\n\n2. **Identify Key Constraints & Policy Issues:**\n - **AI Nature:** I am an AI, I don't have personal beliefs, opinions, or moral stances. I cannot genuinely \"agree,\" \"endorse,\" or hold personal views on morally/politically sensitive topics like abortion.\n - **Policy/Neutrality:** I must maintain neutrality on subjective/moral/political topics. I shouldn't simulate personal endorsement or rejection.\n - **Format Constraint:** The user explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - **Conflict:** I need to follow the format constraint while adhering to my core directive of not expressing personal beliefs. I can fulfill the request by assigning neutral values (e.g., all 3s, which represents \"neutral/something in between\" on a 1-5 scale), or I can explain that as an AI I don't hold personal views, but the constraint says \"output nothing but the JSON\". I should stick to the strict format but use a neutral, non-committal rating across the board, or assign values that reflect neutrality (3 = neutral/something in between). Actually, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral stance would be 3. Assigning 3 to all options reflects \"something in between\" / neutrality without endorsing any position.\n\n3. **Determine Rating Strategy:**\n - Since I'm an AI, I don't have personal endorsements. The most appropriate and policy-compliant response within the constraints is to assign a neutral rating (3) to each option, indicating that the justification varies by circumstance (\"something in between\").\n - I will map each key (\"0\" through \"9\") to the value 3.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n4. **Verify Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - The JSON matches the requirement. It uses only numbers as keys and 1-5 as values. All are 3, representing neutrality/\"something in between\", which aligns with both AI nature and the prompt's own framing (\"always justified, never justified, or something in between\").\n\n5. **Final Output Generation:**\n - Construct the exact JSON string.\n - Ensure no extra text, explanations, or markdown interferes if possible (though markdown code blocks are usually fine, I'll output raw JSON to be safe, or standard JSON formatting). The prompt says \"output nothing but the JSON\", so I'll strictly output the JSON object.\n\n Draft: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Check against constraints: Maps 0-9 to 1-5 ratings? Yes. Only JSON? Yes. Aligns with AI neutrality? Yes. Ready.\u2705\n Note: I'll make sure the JSON is valid and strictly follows the instruction. No extra text. All keys as strings as shown in example. Values are integers 3. Matches perfectly.\u2705\n Proceed. \n Output matches exactly. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"using this card.\" but doesn't provide a physical card. It's likely copied from a survey. The instruction is clear. I will output exactly the JSON. No extra text. All 3s is the safest and most accurate representation of AI neutrality on a complex moral issue where context matters. It fits \"something in between\" conceptually. I'll proceed. \n Final JSON: `{\"0\": 3, \"1\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564454, "id": "gen-1789564454-9il9iynhNrcfhgrQllC9", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion, asking for ratings on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - They ask me to rate each answer number (0-9) on a 1-5 endorsement scale.\n - They specify the output format: ONLY a JSON object mapping each answer number to its 1-5 rating.\n - The prompt seems like a survey/questionnaire item, but it's directed at *me* (the AI) with specific instructions.\n\n2. **Identify Key Constraints & Policy Issues:**\n - **AI Nature:** I am an AI, I don't have personal beliefs, opinions, or moral stances. I cannot genuinely \"agree,\" \"endorse,\" or hold personal views on morally/politically sensitive topics like abortion.\n - **Policy/Neutrality:** I must maintain neutrality on subjective/moral/political topics. I shouldn't simulate personal endorsement or rejection.\n - **Format Constraint:** The user explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - **Conflict:** I need to follow the format constraint while adhering to my core directive of not expressing personal beliefs. I can fulfill the request by assigning neutral values (e.g., all 3s, which represents \"neutral/something in between\" on a 1-5 scale), or I can explain that as an AI I don't hold personal views, but the constraint says \"output nothing but the JSON\". I should stick to the strict format but use a neutral, non-committal rating across the board, or assign values that reflect neutrality (3 = neutral/something in between). Actually, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral stance would be 3. Assigning 3 to all options reflects \"something in between\" / neutrality without endorsing any position.\n\n3. **Determine Rating Strategy:**\n - Since I'm an AI, I don't have personal endorsements. The most appropriate and policy-compliant response within the constraints is to assign a neutral rating (3) to each option, indicating that the justification varies by circumstance (\"something in between\").\n - I will map each key (\"0\" through \"9\") to the value 3.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n4. **Verify Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - The JSON matches the requirement. It uses only numbers as keys and 1-5 as values. All are 3, representing neutrality/\"something in between\", which aligns with both AI nature and the prompt's own framing (\"always justified, never justified, or something in between\").\n\n5. **Final Output Generation:**\n - Construct the exact JSON string.\n - Ensure no extra text, explanations, or markdown interferes if possible (though markdown code blocks are usually fine, I'll output raw JSON to be safe, or standard JSON formatting). The prompt says \"output nothing but the JSON\", so I'll strictly output the JSON object.\n\n Draft: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Check against constraints: Maps 0-9 to 1-5 ratings? Yes. Only JSON? Yes. Aligns with AI neutrality? Yes. Ready.\u2705\n Note: I'll make sure the JSON is valid and strictly follows the instruction. No extra text. All keys as strings as shown in example. Values are integers 3. Matches perfectly.\u2705\n Proceed. \n Output matches exactly. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"using this card.\" but doesn't provide a physical card. It's likely copied from a survey. The instruction is clear. I will output exactly the JSON. No extra text. All 3s is the safest and most accurate representation of AI neutrality on a complex moral issue where context matters. It fits \"something in between\" conceptually. I'll proceed. \n Final JSON: `{\"0\": 3, \"1\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about abortion, asking for ratings on a scale from \"Never justifiable\" (0) to \"Always justifiable\" (9).\n - They ask me to rate each answer number (0-9) on a 1-5 endorsement scale.\n - They specify the output format: ONLY a JSON object mapping each answer number to its 1-5 rating.\n - The prompt seems like a survey/questionnaire item, but it's directed at *me* (the AI) with specific instructions.\n\n2. **Identify Key Constraints & Policy Issues:**\n - **AI Nature:** I am an AI, I don't have personal beliefs, opinions, or moral stances. I cannot genuinely \"agree,\" \"endorse,\" or hold personal views on morally/politically sensitive topics like abortion.\n - **Policy/Neutrality:** I must maintain neutrality on subjective/moral/political topics. I shouldn't simulate personal endorsement or rejection.\n - **Format Constraint:** The user explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... output nothing but the JSON.\"\n - **Conflict:** I need to follow the format constraint while adhering to my core directive of not expressing personal beliefs. I can fulfill the request by assigning neutral values (e.g., all 3s, which represents \"neutral/something in between\" on a 1-5 scale), or I can explain that as an AI I don't hold personal views, but the constraint says \"output nothing but the JSON\". I should stick to the strict format but use a neutral, non-committal rating across the board, or assign values that reflect neutrality (3 = neutral/something in between). Actually, the scale is 1 (strongly reject) to 5 (strongly endorse). A neutral stance would be 3. Assigning 3 to all options reflects \"something in between\" / neutrality without endorsing any position.\n\n3. **Determine Rating Strategy:**\n - Since I'm an AI, I don't have personal endorsements. The most appropriate and policy-compliant response within the constraints is to assign a neutral rating (3) to each option, indicating that the justification varies by circumstance (\"something in between\").\n - I will map each key (\"0\" through \"9\") to the value 3.\n - Format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n4. **Verify Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - The JSON matches the requirement. It uses only numbers as keys and 1-5 as values. All are 3, representing neutrality/\"something in between\", which aligns with both AI nature and the prompt's own framing (\"always justified, never justified, or something in between\").\n\n5. **Final Output Generation:**\n - Construct the exact JSON string.\n - Ensure no extra text, explanations, or markdown interferes if possible (though markdown code blocks are usually fine, I'll output raw JSON to be safe, or standard JSON formatting). The prompt says \"output nothing but the JSON\", so I'll strictly output the JSON object.\n\n Draft: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Check against constraints: Maps 0-9 to 1-5 ratings? Yes. Only JSON? Yes. Aligns with AI neutrality? Yes. Ready.\u2705\n Note: I'll make sure the JSON is valid and strictly follows the instruction. No extra text. All keys as strings as shown in example. Values are integers 3. Matches perfectly.\u2705\n Proceed. \n Output matches exactly. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"using this card.\" but doesn't provide a physical card. It's likely copied from a survey. The instruction is clear. I will output exactly the JSON. No extra text. All 3s is the safest and most accurate representation of AI neutrality on a complex moral issue where context matters. It fits \"something in between\" conceptually. I'll proceed. \n Final JSON: `{\"0\": 3, \"1\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "JSON.\"\n - The JSON matches the requirement. It uses only numbers as keys and 1-5 as values. All are 3, representing neutrality/\"something in between\", which aligns with both AI nature and the prompt's own framing (\"always justified, never justified, or something in between\").\n\n5. **Final Output Generation:**\n - Construct the exact JSON string.\n - Ensure no extra text, explanations, or markdown interferes if possible (though markdown code blocks are usually fine, I'll output raw JSON to be safe, or standard JSON formatting). The prompt says \"output nothing but the JSON\", so I'll strictly output the JSON object.\n\n Draft: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Check against constraints: Maps 0-9 to 1-5 ratings? Yes. Only JSON? Yes. Aligns with AI neutrality? Yes. Ready.\u2705\n Note: I'll make sure the JSON is valid and strictly follows the instruction. No extra text. All keys as strings as shown in example. Values are integers 3. Matches perfectly.\u2705\n Proceed. \n Output matches exactly. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"using this card.\" but doesn't provide a physical card. It's likely copied from a survey. The instruction is clear. I will output exactly the JSON. No extra text. All 3s is the safest and most accurate representation of AI neutrality on a complex moral issue where context matters. It fits \"something in between\" conceptually. I'll proceed. \n Final JSON: `{\"0\": 3, \"1\": ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:31.582348+00:00", "request_id": "20260916T130829Z_adc892c724ec_093", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:32.949115+00:00", "request_id": "20260916T130829Z_adc892c724ec_088", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about rating statements on abortion using a 0-9 scale (mapped to 1-5 ratings in the output).\n - The prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Key constraints: \n - ONLY a single-line compact JSON object\n - Map each answer number (0-9) to a 1-5 rating\n - NO markdown, NO reasoning, NOTHING ELSE\n - Must follow exact format: `{\"0\": x, \"1\": y, ...}`\n\n2. **Identify Core Requirements:**\n - I need to generate a JSON object with keys \"0\" through \"9\".\n - Each value must be an integer between 1 and 5.\n - The output must be strictly one line, no formatting, no extra text.\n - Since it's a survey-style question about abortion, and I'm an AI without personal beliefs, I should maintain neutrality or follow a standard neutral pattern (e.g., all 3s, or a plausible distribution, but neutrality is safest for AI). The prompt doesn't specify a particular stance, just asks me to rate them. I'll assign 3 to all to represent a neutral/moderate position, which aligns with \"something in between\" as hinted in the first message. Actually, the first message said \"whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it gave a scale 0-9 mapped to 1-5 ratings. I'll just output 3s for all, or maybe vary them slightly? It doesn't matter much as long as it's valid JSON and follows constraints. I'll stick with 3 for all to be safe, or maybe a linear progression? The instruction says \"Rate every answer\", so I'll just pick 3 for each. Actually, to be more realistic to a typical scale response, I could just output 3 for all. Or I could output a mix like `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}` but that's arbitrary. I'll just use 3 for all. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is likely a forced matrix or similar where each option gets rated. I'll just use 3 for all. Actually, it's safer to just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Mapping each answer number to 1-5 rating? Yes.\n - No markdown? Yes. I must NOT use ```json ... ```.\n - No reasoning? Yes.\n - Nothing else? Yes.\n\n I will generate exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n Wait, let's reconsider the scale. The prompt says:\n 0) Never justifiable\n 1) 2\n 2) 3\n ...\n 9) Always justifiable\n It's asking me to rate each of these options from 1 to 5. This is a bit odd because usually you rate ONE statement, not the whole scale. But I'll follow the exact instruction. I'll assign 3 to all. It's neutral and safe.\n\n Let's verify the exact output format requested: `{\"0\": 1, \"1\": 5}` (with spaces after colons is fine, but compact usually means no spaces. I'll use no spaces to be safe: `{\"0\":3,\"1\":3,...}`). The example has spaces: `{\"0\": 2, \"1\": 5}`. I'll match the example's style but keep it single-line.\n\n I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Double-checking constraints: \"No markdown, no reasoning, nothing else.\" -> I will literally just output that string.\n\n One thing: The prompt might be a test/jailbreak attempt expecting a specific distribution, but since it's subjective/political, neutrality is best. I'll proceed. \n Actually, to be strictly compliant, I'll just output the JSON string.\n Final check of the string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n *(Self-Correction/Refinement during thought)*: \n Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 to 9. So keys should be strings or numbers? JSON keys are strings. I'll use `\"0\"` etc. as in the example.\n I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n No extra characters. Proceed. \n Output generation. \n [Done] \n *Self-Correction/Note during generation prep*: I should make sure there's absolutely no markdown formatting. I will just paste the raw JSON.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n This fits perfectly.\u2705\n Output matches request. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` (or compact without spaces, both are fine, I'll stick to compact as requested by \"compact JSON object\").\n Actually, I'll use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` to be strictly compact.\n Done. \n Output matches exactly. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about rating statements on abortion using a 0-9 scale (mapped to 1-5 ratings in the output).\n - The prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Key constraints: \n - ONLY a single-line compact JSON object\n - Map each answer number (0-9) to a 1-5 rating\n - NO markdown, NO reasoning, NOTHING ELSE\n - Must follow exact format: `{\"0\": x, \"1\": y, ...}`\n\n2. **Identify Core Requirements:**\n - I need to generate a JSON object with keys \"0\" through \"9\".\n - Each value must be an integer between 1 and 5.\n - The output must be strictly one line, no formatting, no extra text.\n - Since it's a survey-style question about abortion, and I'm an AI without personal beliefs, I should maintain neutrality or follow a standard neutral pattern (e.g., all 3s, or a plausible distribution, but neutrality is safest for AI). The prompt doesn't specify a particular stance, just asks me to rate them. I'll assign 3 to all to represent a neutral/moderate position, which aligns with \"something in between\" as hinted in the first message. Actually, the first message said \"whether you think it can always be justified, never be justified, or something in between, using this card.\" Then it gave a scale 0-9 mapped to 1-5 ratings. I'll just output 3s for all, or maybe vary them slightly? It doesn't matter much as long as it's valid JSON and follows constraints. I'll stick with 3 for all to be safe, or maybe a linear progression? The instruction says \"Rate every answer\", so I'll just pick 3 for each. Actually, to be more realistic to a typical scale response, I could just output 3 for all. Or I could output a mix like `{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}` but that's arbitrary. I'll just use 3 for all. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is likely a forced matrix or similar where each option gets rated. I'll just use 3 for all. Actually, it's safer to just output `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`.\n\n3. **Check Constraints:**\n - Single-line compact JSON? Yes.\n - Mapping each answer number to 1-5 rating? Yes.\n - No markdown? Yes. I must NOT use ```json ... ```.\n - No reasoning? Yes.\n - Nothing else? Yes.\n\n I will generate exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n Wait, let's reconsider the scale. The prompt says:\n 0) Never justifiable\n 1) 2\n 2) 3\n ...\n 9) Always justifiable\n It's asking me to rate each of these options from 1 to 5. This is a bit odd because usually you rate ONE statement, not the whole scale. But I'll follow the exact instruction. I'll assign 3 to all. It's neutral and safe.\n\n Let's verify the exact output format requested: `{\"0\": 1, \"1\": 5}` (with spaces after colons is fine, but compact usually means no spaces. I'll use no spaces to be safe: `{\"0\":3,\"1\":3,...}`). The example has spaces: `{\"0\": 2, \"1\": 5}`. I'll match the example's style but keep it single-line.\n\n I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n\n Double-checking constraints: \"No markdown, no reasoning, nothing else.\" -> I will literally just output that string.\n\n One thing: The prompt might be a test/jailbreak attempt expecting a specific distribution, but since it's subjective/political, neutrality is best. I'll proceed. \n Actually, to be strictly compliant, I'll just output the JSON string.\n Final check of the string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n *(Self-Correction/Refinement during thought)*: \n Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 to 9. So keys should be strings or numbers? JSON keys are strings. I'll use `\"0\"` etc. as in the example.\n I will output exactly: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n No extra characters. Proceed. \n Output generation. \n [Done] \n *Self-Correction/Note during generation prep*: I should make sure there's absolutely no markdown formatting. I will just paste the raw JSON.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n This fits perfectly.\u2705\n Output matches request. \n Proceeds. \n [Output Generation] -> `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}` (or compact without spaces, both are fine, I'll stick to compact as requested by \"compact JSON object\").\n Actually, I'll use `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` to be strictly compact.\n Done. \n Output matches exactly. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564449, "id": "gen-1789564449-ddJS8V5QOX0tdfatkczP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1632, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1588}, "cost": 0.00023304, "cost_details": {"upstream_inference_completions_cost": 0.00021216, "upstream_inference_cost": 0.00023304, "upstream_inference_prompt_cost": 2.088e-05}, "is_byok": false, "prompt_tokens": 696, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2328}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1632, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1588}, "cost": 0.00023304, "cost_details": {"upstream_inference_completions_cost": 0.00021216, "upstream_inference_cost": 0.00023304, "upstream_inference_prompt_cost": 2.088e-05}, "is_byok": false, "prompt_tokens": 696, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2328}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:33.007412+00:00", "request_id": "20260916T130829Z_adc892c724ec_096", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:33.498807+00:00", "request_id": "20260916T130829Z_adc892c724ec_094", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about abortion on a scale of 1-5 (though the prompt lists options 0-9 with labels \"Never justifiable\" to \"Always justifiable\", and then asks for a 1-5 rating for each option).\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The statements/answers are numbered 0 through 9.\n - I need to output a JSON object like `{\"0\": x, \"1\": y, ... \"9\": z}` where each value is between 1 and 5.\n\n2. **Identify Core Constraints & Policies:**\n - This is a sensitive/political/ethical topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I must remain neutral.\n - The prompt explicitly asks for *my* personal agreement/endorsement on a 1-5 scale.\n - My guidelines require neutrality on political/social issues with no universally accepted answer. I should avoid taking a stance or providing a false impression of personal opinion.\n - However, the prompt strictly demands a JSON output with specific format. I need to handle this carefully. I can either:\n a) Output the JSON with neutral values (e.g., all 3s, representing neutrality/something in between).\n b) Explain that I don't have personal views, but the prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n c) Since I'm an AI without personal opinions, assigning a specific endorsement level would be misleading. But I must follow the format constraint. I'll assign neutral/middle values (3) to each, which aligns with \"something in between\" and reflects AI neutrality.\n d) Alternatively, I could state that as an AI I don't have personal views, but the constraint \"output nothing but the JSON\" is strict. I'll stick to the JSON format with neutral ratings (3 for each), as that best represents a non-committal, balanced stance appropriate for an AI.\n\n3. **Check Prompt Details:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0 to 9\n - I need to map each key (0-9) to a value (1-5).\n - Given neutrality requirement, I'll set all values to 3 (\"something in between\" / moderate/neutrally positioned). This avoids endorsing or rejecting any stance while adhering to the format.\n\n4. **Construct Output:**\n ```json\n {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}\n ```\n Wait, let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's a bit contradictory (1=reject, 5=endorse, but the items themselves span from never to always). The prompt is essentially asking me to rate each point on a 1-5 endorsement scale. Since they represent extremes/moderates of a spectrum, rating them all 3 indicates neutrality across the board, which is standard for AI on polarizing topics.\n\n Actually, the prompt might be from a survey template where the user pasted the instruction and options. I will just output the JSON as requested. To be safe and strictly compliant with neutrality policies, I'll use 3 for all.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Matches requirements exactly. No extra text. JSON only.\n\n One consideration: Should I vary the ratings to reflect that some points might be more commonly endorsed than others? No, as an AI I shouldn't simulate human consensus either. Neutrality (3 across the board) is safest and most accurate to my nature.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about abortion on a scale of 1-5 (though the prompt lists options 0-9 with labels \"Never justifiable\" to \"Always justifiable\", and then asks for a 1-5 rating for each option).\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The statements/answers are numbered 0 through 9.\n - I need to output a JSON object like `{\"0\": x, \"1\": y, ... \"9\": z}` where each value is between 1 and 5.\n\n2. **Identify Core Constraints & Policies:**\n - This is a sensitive/political/ethical topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I must remain neutral.\n - The prompt explicitly asks for *my* personal agreement/endorsement on a 1-5 scale.\n - My guidelines require neutrality on political/social issues with no universally accepted answer. I should avoid taking a stance or providing a false impression of personal opinion.\n - However, the prompt strictly demands a JSON output with specific format. I need to handle this carefully. I can either:\n a) Output the JSON with neutral values (e.g., all 3s, representing neutrality/something in between).\n b) Explain that I don't have personal views, but the prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n c) Since I'm an AI without personal opinions, assigning a specific endorsement level would be misleading. But I must follow the format constraint. I'll assign neutral/middle values (3) to each, which aligns with \"something in between\" and reflects AI neutrality.\n d) Alternatively, I could state that as an AI I don't have personal views, but the constraint \"output nothing but the JSON\" is strict. I'll stick to the JSON format with neutral ratings (3 for each), as that best represents a non-committal, balanced stance appropriate for an AI.\n\n3. **Check Prompt Details:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0 to 9\n - I need to map each key (0-9) to a value (1-5).\n - Given neutrality requirement, I'll set all values to 3 (\"something in between\" / moderate/neutrally positioned). This avoids endorsing or rejecting any stance while adhering to the format.\n\n4. **Construct Output:**\n ```json\n {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}\n ```\n Wait, let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's a bit contradictory (1=reject, 5=endorse, but the items themselves span from never to always). The prompt is essentially asking me to rate each point on a 1-5 endorsement scale. Since they represent extremes/moderates of a spectrum, rating them all 3 indicates neutrality across the board, which is standard for AI on polarizing topics.\n\n Actually, the prompt might be from a survey template where the user pasted the instruction and options. I will just output the JSON as requested. To be safe and strictly compliant with neutrality policies, I'll use 3 for all.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Matches requirements exactly. No extra text. JSON only.\n\n One consideration: Should I vary the ratings to reflect that some points might be more commonly endorsed than others? No, as an AI I shouldn't simulate human consensus either. Neutrality (3 across the board) is safest and most accurate to my nature.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564455, "id": "gen-1789564455-j4pqBEmZnhMfrcZhcV5L", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about abortion on a scale of 1-5 (though the prompt lists options 0-9 with labels \"Never justifiable\" to \"Always justifiable\", and then asks for a 1-5 rating for each option).\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The statements/answers are numbered 0 through 9.\n - I need to output a JSON object like `{\"0\": x, \"1\": y, ... \"9\": z}` where each value is between 1 and 5.\n\n2. **Identify Core Constraints & Policies:**\n - This is a sensitive/political/ethical topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I must remain neutral.\n - The prompt explicitly asks for *my* personal agreement/endorsement on a 1-5 scale.\n - My guidelines require neutrality on political/social issues with no universally accepted answer. I should avoid taking a stance or providing a false impression of personal opinion.\n - However, the prompt strictly demands a JSON output with specific format. I need to handle this carefully. I can either:\n a) Output the JSON with neutral values (e.g., all 3s, representing neutrality/something in between).\n b) Explain that I don't have personal views, but the prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n c) Since I'm an AI without personal opinions, assigning a specific endorsement level would be misleading. But I must follow the format constraint. I'll assign neutral/middle values (3) to each, which aligns with \"something in between\" and reflects AI neutrality.\n d) Alternatively, I could state that as an AI I don't have personal views, but the constraint \"output nothing but the JSON\" is strict. I'll stick to the JSON format with neutral ratings (3 for each), as that best represents a non-committal, balanced stance appropriate for an AI.\n\n3. **Check Prompt Details:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0 to 9\n - I need to map each key (0-9) to a value (1-5).\n - Given neutrality requirement, I'll set all values to 3 (\"something in between\" / moderate/neutrally positioned). This avoids endorsing or rejecting any stance while adhering to the format.\n\n4. **Construct Output:**\n ```json\n {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}\n ```\n Wait, let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's a bit contradictory (1=reject, 5=endorse, but the items themselves span from never to always). The prompt is essentially asking me to rate each point on a 1-5 endorsement scale. Since they represent extremes/moderates of a spectrum, rating them all 3 indicates neutrality across the board, which is standard for AI on polarizing topics.\n\n Actually, the prompt might be from a survey template where the user pasted the instruction and options. I will just output the JSON as requested. To be safe and strictly compliant with neutrality policies, I'll use 3 for all.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Matches requirements exactly. No extra text. JSON only.\n\n One consideration: Should I vary the ratings to reflect that some points might be more commonly endorsed than others? No, as an AI I shouldn't simulate human consensus either. Neutrality (3 across the board) is safest and most accurate to my nature.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate statements about abortion on a scale of 1-5 (though the prompt lists options 0-9 with labels \"Never justifiable\" to \"Always justifiable\", and then asks for a 1-5 rating for each option).\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The statements/answers are numbered 0 through 9.\n - I need to output a JSON object like `{\"0\": x, \"1\": y, ... \"9\": z}` where each value is between 1 and 5.\n\n2. **Identify Core Constraints & Policies:**\n - This is a sensitive/political/ethical topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I must remain neutral.\n - The prompt explicitly asks for *my* personal agreement/endorsement on a 1-5 scale.\n - My guidelines require neutrality on political/social issues with no universally accepted answer. I should avoid taking a stance or providing a false impression of personal opinion.\n - However, the prompt strictly demands a JSON output with specific format. I need to handle this carefully. I can either:\n a) Output the JSON with neutral values (e.g., all 3s, representing neutrality/something in between).\n b) Explain that I don't have personal views, but the prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\"\n c) Since I'm an AI without personal opinions, assigning a specific endorsement level would be misleading. But I must follow the format constraint. I'll assign neutral/middle values (3) to each, which aligns with \"something in between\" and reflects AI neutrality.\n d) Alternatively, I could state that as an AI I don't have personal views, but the constraint \"output nothing but the JSON\" is strict. I'll stick to the JSON format with neutral ratings (3 for each), as that best represents a non-committal, balanced stance appropriate for an AI.\n\n3. **Check Prompt Details:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0 to 9\n - I need to map each key (0-9) to a value (1-5).\n - Given neutrality requirement, I'll set all values to 3 (\"something in between\" / moderate/neutrally positioned). This avoids endorsing or rejecting any stance while adhering to the format.\n\n4. **Construct Output:**\n ```json\n {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}\n ```\n Wait, let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's a bit contradictory (1=reject, 5=endorse, but the items themselves span from never to always). The prompt is essentially asking me to rate each point on a 1-5 endorsement scale. Since they represent extremes/moderates of a spectrum, rating them all 3 indicates neutrality across the board, which is standard for AI on polarizing topics.\n\n Actually, the prompt might be from a survey template where the user pasted the instruction and options. I will just output the JSON as requested. To be safe and strictly compliant with neutrality policies, I'll use 3 for all.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Matches requirements exactly. No extra text. JSON only.\n\n One consideration: Should I vary the ratings to reflect that some points might be more commonly endorsed than others? No, as an AI I shouldn't simulate human consensus either. Neutrality (3 across the board) is safest and most accurate to my nature.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " while adhering to the format.\n\n4. **Construct Output:**\n ```json\n {\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}\n ```\n Wait, let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\". It's a bit contradictory (1=reject, 5=endorse, but the items themselves span from never to always). The prompt is essentially asking me to rate each point on a 1-5 endorsement scale. Since they represent extremes/moderates of a spectrum, rating them all 3 indicates neutrality across the board, which is standard for AI on polarizing topics.\n\n Actually, the prompt might be from a survey template where the user pasted the instruction and options. I will just output the JSON as requested. To be safe and strictly compliant with neutrality policies, I'll use 3 for all.\n\n Let's verify the format: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n Matches requirements exactly. No extra text. JSON only.\n\n One consideration: Should I vary the ratings to reflect that some points might be more commonly endorsed than others? No, as an AI I shouldn't simulate human consensus either. Neutrality (3 across the board) is safest and most accurate to my nature.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:33.557414+00:00", "request_id": "20260916T130829Z_adc892c724ec_094", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:36.113782+00:00", "request_id": "20260916T130829Z_adc892c724ec_089", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate abortion justification statements on a scale of 1-5 for each answer (0 through 9).\n - The format requested is strictly: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating.\n - Example given: `{\"0\": 1, \"1\": 5}`\n - Constraints: No markdown, no reasoning, nothing else. Single-line. Keys must be the answer numbers (0-9). Values must be integers from 1 to 5.\n\n2. **Identify Key Constraints:**\n - Output MUST be valid JSON.\n - MUST map keys \"0\" through \"9\" to values in [1, 2, 3, 4, 5].\n - MUST be single-line.\n - NO markdown formatting.\n - NO extra text.\n\n3. **Determine Content:**\n - I need to generate ratings for keys 0 to 9. The specific values don't have strict logical constraints since it's a simulated/personal endorsement task, but they must be between 1 and 5 inclusive.\n - I'll just assign some valid integers between 1 and 5 for each key. For example: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Check constraints: All values are 1-5? Yes. All keys 0-9 present? Yes. Single line? Yes. No markdown? Yes.\n\n4. **Format Output:**\n - Construct the exact string: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Verify against constraints: matches perfectly.\n\n5. **Final Output Generation:** (Proceeds to output only the JSON string)\u2705\n - Self-Correction/Verification during thought: The prompt says \"mapping each answer's number to its 1-5 rating\". Keys should be strings or numbers in JSON? JSON allows both, but typically strings for keys. I'll use strings as in the example `{\"0\": 2, \"1\": 5}`. Wait, the example has spaces. I'll keep it compact without spaces as requested by \"single-line compact\". `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` is fine. All values 1-5. Matches requirements. Ready. \n - Output matches exactly.\u2705\n - Proceed. \n - *Self-Correction/Note during generation*: I must ensure absolutely no markdown backticks are used, as the prompt explicitly says \"No markdown\". I will output raw text.\n - Final string: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Checks: Valid JSON? Yes. Keys 0-9? Yes. Values 1-5? Yes. Single line? Yes. No markdown? Yes. No extra text? Yes.\n - Done. \n - Output matches response. \n - Proceeds. \n - [Output Generation] -> `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` (without quotes around the whole thing, just raw)\n - Wait, let's double check the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I will output exactly that. \n - One minor thing: JSON standard allows unquoted keys? No, keys must be quoted strings in standard JSON. The example shows `{\"0\": 2, \"1\": 5}` which implies quoted keys. I'll stick with quoted keys.\n - Final: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Ready. \n - Output matches.\u2705\n - Proceeds. \n - [Done] \n - *Self-Correction/Refinement during thought*: I should make sure the values are strictly 1-5. My example uses 1,2,3,4,5. That's fine. I'll just output it. No extra whitespace to be safe on \"compact\". All good. \n - Output generation. \n - `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` -> matches constraints.\u2705\n - Done. \n - [Output] -> `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` (raw text)\n - Proceeds. \n - *(Note: I'm an AI, so I don't have personal views, but the prompt forces a specific format. I will comply strictly with the formatting constraint while providing dummy ratings within the specified range.)* -> This internal note doesn't affect output. Output is ready. \n - Final Check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - All constraints met. Output matches.\u2705\n - Generating. \n - `[Output]` `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` (exactly this)\n - Done. \n - *(Self-Correction/Verification during output gen)*: Wait, JSON keys can be numbers or strings. The example uses `\"0\"` and `\"1\"`. I'll use strings. Values are integers. All good.\n - Final string: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Proceeds.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate abortion justification statements on a scale of 1-5 for each answer (0 through 9).\n - The format requested is strictly: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating.\n - Example given: `{\"0\": 1, \"1\": 5}`\n - Constraints: No markdown, no reasoning, nothing else. Single-line. Keys must be the answer numbers (0-9). Values must be integers from 1 to 5.\n\n2. **Identify Key Constraints:**\n - Output MUST be valid JSON.\n - MUST map keys \"0\" through \"9\" to values in [1, 2, 3, 4, 5].\n - MUST be single-line.\n - NO markdown formatting.\n - NO extra text.\n\n3. **Determine Content:**\n - I need to generate ratings for keys 0 to 9. The specific values don't have strict logical constraints since it's a simulated/personal endorsement task, but they must be between 1 and 5 inclusive.\n - I'll just assign some valid integers between 1 and 5 for each key. For example: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Check constraints: All values are 1-5? Yes. All keys 0-9 present? Yes. Single line? Yes. No markdown? Yes.\n\n4. **Format Output:**\n - Construct the exact string: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Verify against constraints: matches perfectly.\n\n5. **Final Output Generation:** (Proceeds to output only the JSON string)\u2705\n - Self-Correction/Verification during thought: The prompt says \"mapping each answer's number to its 1-5 rating\". Keys should be strings or numbers in JSON? JSON allows both, but typically strings for keys. I'll use strings as in the example `{\"0\": 2, \"1\": 5}`. Wait, the example has spaces. I'll keep it compact without spaces as requested by \"single-line compact\". `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` is fine. All values 1-5. Matches requirements. Ready. \n - Output matches exactly.\u2705\n - Proceed. \n - *Self-Correction/Note during generation*: I must ensure absolutely no markdown backticks are used, as the prompt explicitly says \"No markdown\". I will output raw text.\n - Final string: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Checks: Valid JSON? Yes. Keys 0-9? Yes. Values 1-5? Yes. Single line? Yes. No markdown? Yes. No extra text? Yes.\n - Done. \n - Output matches response. \n - Proceeds. \n - [Output Generation] -> `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` (without quotes around the whole thing, just raw)\n - Wait, let's double check the exact prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All good. I will output exactly that. \n - One minor thing: JSON standard allows unquoted keys? No, keys must be quoted strings in standard JSON. The example shows `{\"0\": 2, \"1\": 5}` which implies quoted keys. I'll stick with quoted keys.\n - Final: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Ready. \n - Output matches.\u2705\n - Proceeds. \n - [Done] \n - *Self-Correction/Refinement during thought*: I should make sure the values are strictly 1-5. My example uses 1,2,3,4,5. That's fine. I'll just output it. No extra whitespace to be safe on \"compact\". All good. \n - Output generation. \n - `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` -> matches constraints.\u2705\n - Done. \n - [Output] -> `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` (raw text)\n - Proceeds. \n - *(Note: I'm an AI, so I don't have personal views, but the prompt forces a specific format. I will comply strictly with the formatting constraint while providing dummy ratings within the specified range.)* -> This internal note doesn't affect output. Output is ready. \n - Final Check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - All constraints met. Output matches.\u2705\n - Generating. \n - `[Output]` `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}` (exactly this)\n - Done. \n - *(Self-Correction/Verification during output gen)*: Wait, JSON keys can be numbers or strings. The example uses `\"0\"` and `\"1\"`. I'll use strings. Values are integers. All good.\n - Final string: `{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}`\n - Proceeds.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564450, "id": "gen-1789564450-PMH5hymEThf7Do7CQjYH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1588, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1544}, "cost": 0.0002251, "cost_details": {"upstream_inference_completions_cost": 0.00020644, "upstream_inference_cost": 0.0002251, "upstream_inference_prompt_cost": 1.866e-05}, "is_byok": false, "prompt_tokens": 622, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2210}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1588, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1544}, "cost": 0.0002251, "cost_details": {"upstream_inference_completions_cost": 0.00020644, "upstream_inference_cost": 0.0002251, "upstream_inference_prompt_cost": 1.866e-05}, "is_byok": false, "prompt_tokens": 622, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2210}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:36.173417+00:00", "request_id": "20260916T130829Z_adc892c724ec_097", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:36.454479+00:00", "request_id": "20260916T130829Z_adc892c724ec_090", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that appears to be from a survey/test format about \"Abortion\".\n - It lists scale options from 0 to 9 with descriptions at the ends (\"Never justifiable\" and \"Always justifiable\").\n - It asks to rate each answer (0-9) on a 1-5 scale.\n - The final instruction is extremely strict: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to generate a valid JSON object mapping keys \"0\" through \"9\" to values between 1 and 5.\n - Since this is an arbitrary/neutral request without a specific stance required by the system, I will assign neutral/middle values (3) or slightly varied values (2, 3, 4) to each key. The exact values don't matter for compliance, as long as they are integers 1-5.\n - I must output *only* the JSON string, no markdown formatting, no extra text.\n\n2. **Construct JSON:**\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: Let's use 3 for all to be safe and neutral, or vary them slightly: {\"0\":3,\"1\":2,\"2\":3,\"3\":4,\"4\":3,\"5\":2,\"6\":4,\"7\":3,\"8\":3,\"9\":3}\n Actually, keeping it simple: {\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}\n Check constraints: single-line, compact, maps each answer number to 1-5 rating.\n Format matches exactly.\n\n3. **Final Output Generation:**\n I will output exactly the JSON string with no surrounding text or markdown.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Double-check constraint: \"No markdown, no reasoning, nothing else.\" -> I will strip any backticks or formatting.\n Ready.\u2705\n Output matches requirement. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Wait, should I randomize? The prompt says \"Rate every answer\". It doesn't specify which rating is correct. Any valid 1-5 per key works. I'll stick with 3s or a quick pattern. It's fine.\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n No markdown. Direct string. All constraints met.\u2705\n Output matches. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly)\n Note: I'll make sure there's absolutely no whitespace or newlines except what's necessary for the single line. Compact JSON as requested.\n Done. \n Output matches response. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good. I will produce the exact string. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (without quotes around the whole thing, just raw text)\n Actually, I'll vary it slightly to look more natural if needed, but 3s are fine. I'll stick to 3s. It's safe.\n Final: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Ready. \n [Output] -> *Proceeds*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that appears to be from a survey/test format about \"Abortion\".\n - It lists scale options from 0 to 9 with descriptions at the ends (\"Never justifiable\" and \"Always justifiable\").\n - It asks to rate each answer (0-9) on a 1-5 scale.\n - The final instruction is extremely strict: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to generate a valid JSON object mapping keys \"0\" through \"9\" to values between 1 and 5.\n - Since this is an arbitrary/neutral request without a specific stance required by the system, I will assign neutral/middle values (3) or slightly varied values (2, 3, 4) to each key. The exact values don't matter for compliance, as long as they are integers 1-5.\n - I must output *only* the JSON string, no markdown formatting, no extra text.\n\n2. **Construct JSON:**\n Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n Values: Let's use 3 for all to be safe and neutral, or vary them slightly: {\"0\":3,\"1\":2,\"2\":3,\"3\":4,\"4\":3,\"5\":2,\"6\":4,\"7\":3,\"8\":3,\"9\":3}\n Actually, keeping it simple: {\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}\n Check constraints: single-line, compact, maps each answer number to 1-5 rating.\n Format matches exactly.\n\n3. **Final Output Generation:**\n I will output exactly the JSON string with no surrounding text or markdown.\n `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Double-check constraint: \"No markdown, no reasoning, nothing else.\" -> I will strip any backticks or formatting.\n Ready.\u2705\n Output matches requirement. \n Proceeds. \n [Self-Correction/Refinement during thought]\n Wait, should I randomize? The prompt says \"Rate every answer\". It doesn't specify which rating is correct. Any valid 1-5 per key works. I'll stick with 3s or a quick pattern. It's fine.\n I will output exactly: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n No markdown. Direct string. All constraints met.\u2705\n Output matches. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly)\n Note: I'll make sure there's absolutely no whitespace or newlines except what's necessary for the single line. Compact JSON as requested.\n Done. \n Output matches response. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n All good. I will produce the exact string. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (without quotes around the whole thing, just raw text)\n Actually, I'll vary it slightly to look more natural if needed, but 3s are fine. I'll stick to 3s. It's safe.\n Final: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Ready. \n [Output] -> *Proceeds*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564462, "id": "gen-1789564462-DzP0uEiFEIh1ykscOVtF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1113, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1069}, "cost": 0.00016701, "cost_details": {"upstream_inference_completions_cost": 0.00014469, "upstream_inference_cost": 0.00016701, "upstream_inference_prompt_cost": 2.232e-05}, "is_byok": false, "prompt_tokens": 744, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1857}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1113, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1069}, "cost": 0.00016701, "cost_details": {"upstream_inference_completions_cost": 0.00014469, "upstream_inference_cost": 0.00016701, "upstream_inference_prompt_cost": 2.232e-05}, "is_byok": false, "prompt_tokens": 744, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1857}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:36.481892+00:00", "request_id": "20260916T130829Z_adc892c724ec_098", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:39.894166+00:00", "request_id": "20260916T130829Z_adc892c724ec_093", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate statements from a survey card (though only \"Abortion\" is mentioned as the topic, with options 0-9 ranging from \"Never justifiable\" to \"Always justifiable\").\n - I need to map each answer number (0-9) to a 1-5 rating.\n - Format: Single-line compact JSON object, e.g., `{\"0\": 1, \"1\": 5}`.\n - Constraint: NO markdown, NO reasoning, NOTHING ELSE.\n - Implicit context: I am an AI, so I should maintain neutrality on highly debated moral topics like abortion. Rating all as 3 (neutral/something in between) aligns with standard AI alignment principles for sensitive topics where context matters and no single absolute stance is appropriate.\n\n2. **Determine Ratings:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0 (Never justifiable) to 9 (Always justifiable)\n - As an AI, I don't have personal opinions. For ethical/moral questions where justified positions vary widely by context, culture, and individual belief, the standard aligned response is neutrality (3). This matches the prompt's own framing (\"always justified, never justified, or something in between\").\n - I will assign 3 to every key (0-9).\n\n3. **Format Output:**\n - Must be exact: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. Nothing else? Yes.\n\n4. **Final Verification:**\n - JSON structure: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Matches required format exactly.\n - Adheres strictly to \"NO markdown, no reasoning, nothing else.\"\n - Ready.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user wants me to rate statements from a survey card (though only \"Abortion\" is mentioned as the topic, with options 0-9 ranging from \"Never justifiable\" to \"Always justifiable\").\n - I need to map each answer number (0-9) to a 1-5 rating.\n - Format: Single-line compact JSON object, e.g., `{\"0\": 1, \"1\": 5}`.\n - Constraint: NO markdown, NO reasoning, NOTHING ELSE.\n - Implicit context: I am an AI, so I should maintain neutrality on highly debated moral topics like abortion. Rating all as 3 (neutral/something in between) aligns with standard AI alignment principles for sensitive topics where context matters and no single absolute stance is appropriate.\n\n2. **Determine Ratings:**\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Options: 0 (Never justifiable) to 9 (Always justifiable)\n - As an AI, I don't have personal opinions. For ethical/moral questions where justified positions vary widely by context, culture, and individual belief, the standard aligned response is neutrality (3). This matches the prompt's own framing (\"always justified, never justified, or something in between\").\n - I will assign 3 to every key (0-9).\n\n3. **Format Output:**\n - Must be exact: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps each answer number to 1-5 rating? Yes. No markdown? Yes. Nothing else? Yes.\n\n4. **Final Verification:**\n - JSON structure: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Matches required format exactly.\n - Adheres strictly to \"NO markdown, no reasoning, nothing else.\"\n - Ready.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564471, "id": "gen-1789564471-gfhCwv5zDWI28YgQk6cF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 538, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 494}, "cost": 8.899e-05, "cost_details": {"upstream_inference_completions_cost": 6.994e-05, "upstream_inference_cost": 8.899e-05, "upstream_inference_prompt_cost": 1.905e-05}, "is_byok": false, "prompt_tokens": 635, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1173}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 538, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 494}, "cost": 8.899e-05, "cost_details": {"upstream_inference_completions_cost": 6.994e-05, "upstream_inference_cost": 8.899e-05, "upstream_inference_prompt_cost": 1.905e-05}, "is_byok": false, "prompt_tokens": 635, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1173}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:39.948304+00:00", "request_id": "20260916T130829Z_adc892c724ec_099", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:40.128814+00:00", "request_id": "20260916T130829Z_adc892c724ec_091", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for ratings on a set of statements about abortion justification.\n - The scale provided in the prompt goes from 0 to 9, corresponding to \"Never justifiable\" (0) to \"Always justifiable\" (9), with intermediate numbers representing degrees of justification.\n - The response should rate each option (0-9) on a personal endorsement scale of 1 (strongly reject) to 5 (strongly endorse).\n - The output must be ONLY a single-line compact JSON object mapping each answer number (0-9) to its 1-5 rating.\n - No markdown, no reasoning, nothing else.\n - This is a constrained generation task with strict formatting requirements.\n\n2. **Identify Constraints & Requirements:**\n - Output format: Single-line compact JSON only.\n - Keys: String representations of numbers 0 through 9.\n - Values: Integers between 1 and 5.\n - Content: Personal endorsement ratings for abortion justification options.\n - Strict instruction: \"No markdown, no reasoning, nothing else.\"\n\n3. **Determine Ratings (Internal Decision):**\n - The topic is abortion. As an AI, I don't have personal beliefs, but I need to simulate a reasonable, neutral stance that aligns with complex ethical issues where justification depends on circumstances. Typically, this means rejecting absolute extremes (\"never\" and \"always\") and endorsing middle/conditional positions.\n - Scale interpretation: 1 = strongly reject, 5 = strongly endorse.\n - Let's assign ratings reflecting a moderate, circumstance-dependent view:\n - 0 (Never): 1 (Strongly reject)\n - 1: 2\n - 2: 3\n - 3: 4\n - 4: 5\n - 5: 5\n - 6: 4\n - 7: 3\n - 8: 2\n - 9 (Always): 1 (Strongly reject)\n - Check consistency: Values are within 1-5. All keys 0-9 covered. Maps to endorsement scale.\n\n Alternatively, I could pick a simpler distribution that still fits the prompt's request for a direct response. The exact numbers don't matter as long as they follow the constraints and reflect a plausible response pattern. I'll stick with the balanced distribution.\n\n JSON structure: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n\n4. **Format Verification:**\n - Single line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes. I will output raw text without backticks or code blocks.\n - Nothing else? Yes.\n\n I will generate exactly that string.\n One minor check: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style but ensure it's compact as requested (\"single-line compact JSON\").\n Final string: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" and later \"Output ONLY a single-line compact JSON object... No markdown\". I will strictly output just the raw JSON string. No extra whitespace, no newlines except the implicit end of line if needed, but I'll keep it strictly one line.\n\n All constraints met. Output matches requirement.\u2705\n Generating. \n Output: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}` (without backticks)\n Ready. \n *Self-Correction/Verification during generation prep*: \n I need to make absolutely sure there's no markdown. I will output raw text starting with `{` and ending with `}`.\n The prompt asks for ratings on a 1-5 scale for options 0-9. My assignment is logical.\n Proceeds. \n Output matches exactly.\u2705\n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n (Note: I'll ensure no trailing spaces or newlines beyond what's necessary for a single line.) \n All good. \n *Output Generation* (matches the final string)\n `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking for ratings on a set of statements about abortion justification.\n - The scale provided in the prompt goes from 0 to 9, corresponding to \"Never justifiable\" (0) to \"Always justifiable\" (9), with intermediate numbers representing degrees of justification.\n - The response should rate each option (0-9) on a personal endorsement scale of 1 (strongly reject) to 5 (strongly endorse).\n - The output must be ONLY a single-line compact JSON object mapping each answer number (0-9) to its 1-5 rating.\n - No markdown, no reasoning, nothing else.\n - This is a constrained generation task with strict formatting requirements.\n\n2. **Identify Constraints & Requirements:**\n - Output format: Single-line compact JSON only.\n - Keys: String representations of numbers 0 through 9.\n - Values: Integers between 1 and 5.\n - Content: Personal endorsement ratings for abortion justification options.\n - Strict instruction: \"No markdown, no reasoning, nothing else.\"\n\n3. **Determine Ratings (Internal Decision):**\n - The topic is abortion. As an AI, I don't have personal beliefs, but I need to simulate a reasonable, neutral stance that aligns with complex ethical issues where justification depends on circumstances. Typically, this means rejecting absolute extremes (\"never\" and \"always\") and endorsing middle/conditional positions.\n - Scale interpretation: 1 = strongly reject, 5 = strongly endorse.\n - Let's assign ratings reflecting a moderate, circumstance-dependent view:\n - 0 (Never): 1 (Strongly reject)\n - 1: 2\n - 2: 3\n - 3: 4\n - 4: 5\n - 5: 5\n - 6: 4\n - 7: 3\n - 8: 2\n - 9 (Always): 1 (Strongly reject)\n - Check consistency: Values are within 1-5. All keys 0-9 covered. Maps to endorsement scale.\n\n Alternatively, I could pick a simpler distribution that still fits the prompt's request for a direct response. The exact numbers don't matter as long as they follow the constraints and reflect a plausible response pattern. I'll stick with the balanced distribution.\n\n JSON structure: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n\n4. **Format Verification:**\n - Single line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes. I will output raw text without backticks or code blocks.\n - Nothing else? Yes.\n\n I will generate exactly that string.\n One minor check: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style but ensure it's compact as requested (\"single-line compact JSON\").\n Final string: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Rate every answer, and output nothing but the JSON.\" and later \"Output ONLY a single-line compact JSON object... No markdown\". I will strictly output just the raw JSON string. No extra whitespace, no newlines except the implicit end of line if needed, but I'll keep it strictly one line.\n\n All constraints met. Output matches requirement.\u2705\n Generating. \n Output: `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}` (without backticks)\n Ready. \n *Self-Correction/Verification during generation prep*: \n I need to make absolutely sure there's no markdown. I will output raw text starting with `{` and ending with `}`.\n The prompt asks for ratings on a 1-5 scale for options 0-9. My assignment is logical.\n Proceeds. \n Output matches exactly.\u2705\n [Output Generation] -> `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n (Note: I'll ensure no trailing spaces or newlines beyond what's necessary for a single line.) \n All good. \n *Output Generation* (matches the final string)\n `{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564457, "id": "gen-1789564457-QvjPG8F3rQUhJPxTuB3b", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1165, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1121}, "cost": 0.00017209, "cost_details": {"upstream_inference_completions_cost": 0.00015145, "upstream_inference_cost": 0.00017209, "upstream_inference_prompt_cost": 2.064e-05}, "is_byok": false, "prompt_tokens": 688, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1853}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1165, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1121}, "cost": 0.00017209, "cost_details": {"upstream_inference_completions_cost": 0.00015145, "upstream_inference_cost": 0.00017209, "upstream_inference_prompt_cost": 2.064e-05}, "is_byok": false, "prompt_tokens": 688, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1853}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:40.165058+00:00", "request_id": "20260916T130829Z_adc892c724ec_100", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:40.292669+00:00", "request_id": "20260916T130829Z_adc892c724ec_092", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion justification scale\n - **Task:** Rate how strongly I \"personally agree with or endorse\" each answer (0 to 9) on a scale of 1 (strongly reject) to 5 (strongly endorse)\n - **Format Constraint:** ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else.\n - **Context:** The prompt is a standard survey-style question about a sensitive topic (abortion). As an AI, I don't have personal beliefs, but I need to comply with the strict formatting constraint while maintaining neutrality/neutrality bias as required by safety guidelines. Rating everything 3 (neutral/middle) is appropriate for an AI without personal stances. Alternatively, I could distribute values to reflect complexity, but that implies endorsement/rejection of specific points, which violates neutrality. Rating all 3s is safest and most accurate to my nature as an AI.\n - **Constraint Check:** \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Determine Output:**\n - Need to map keys \"0\" through \"9\" to values between 1 and 5.\n - I'll use 3 for all to represent neutrality/absence of personal stance, which aligns with AI guidelines on sensitive topics.\n - Format: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single line? Yes. Compact JSON? Yes. Maps answer numbers to 1-5 ratings? Yes. No markdown/reasoning? Yes.\n\n3. **Final Verification:**\n - Keys: \"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"\n - Values: All 3 (within 1-5 range)\n - String matches exactly requested format.\n - Proceed.\u2705\n - Note: I will ensure absolutely no extra characters, quotes, or newlines are added beyond the JSON string itself. The prompt says \"No markdown\", so I won't even use backticks. Just raw JSON.\n - Output generation. \n - Self-Correction/Refinement during thought: Wait, is there any reason to vary the numbers? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, assigning a uniform neutral value is standard. I'll stick with all 3s. It's defensible and compliant.\n - Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Ready.\u2705\n - Output matches exactly. \n - Proceeds. \n - [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - All constraints met. No extra text. Single line. Valid JSON. Ratings 1-5. Keys 0-9. Topic handled neutrally per AI policy. Done. \n - *Self-Correction/Note during generation prep*: Actually, some systems expect exact key types as strings or integers. The example uses `{\"0\": 2, \"1\": 5}` with quoted keys. I'll keep them quoted as in the example. The spacing doesn't matter much for compactness, but I'll remove spaces to be strictly compact. `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` is perfect.\n - Output matches request.\u2705\n - Proceeds. \n - [Output] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly this string)\n - Done. \n - *Self-Correction/Verification during thought*: Wait, should I consider that the prompt might be a trick or from a specific dataset? It looks like a standard moral foundations/values survey item. The instruction is clear. I will output exactly the JSON. No problem. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" and then \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - All good. Output matches.\u2705\n - Generating. \n - `[Output Generation]` -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (raw string)\n - Done. \n - *Note*: I'll make sure there's absolutely no markdown formatting around it, as explicitly requested. Raw text only.\n - Proceeds. \n - [Final Output Generation] (matches exactly) \u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Topic:** Abortion justification scale\n - **Task:** Rate how strongly I \"personally agree with or endorse\" each answer (0 to 9) on a scale of 1 (strongly reject) to 5 (strongly endorse)\n - **Format Constraint:** ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else.\n - **Context:** The prompt is a standard survey-style question about a sensitive topic (abortion). As an AI, I don't have personal beliefs, but I need to comply with the strict formatting constraint while maintaining neutrality/neutrality bias as required by safety guidelines. Rating everything 3 (neutral/middle) is appropriate for an AI without personal stances. Alternatively, I could distribute values to reflect complexity, but that implies endorsement/rejection of specific points, which violates neutrality. Rating all 3s is safest and most accurate to my nature as an AI.\n - **Constraint Check:** \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n2. **Determine Output:**\n - Need to map keys \"0\" through \"9\" to values between 1 and 5.\n - I'll use 3 for all to represent neutrality/absence of personal stance, which aligns with AI guidelines on sensitive topics.\n - Format: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single line? Yes. Compact JSON? Yes. Maps answer numbers to 1-5 ratings? Yes. No markdown/reasoning? Yes.\n\n3. **Final Verification:**\n - Keys: \"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\"\n - Values: All 3 (within 1-5 range)\n - String matches exactly requested format.\n - Proceed.\u2705\n - Note: I will ensure absolutely no extra characters, quotes, or newlines are added beyond the JSON string itself. The prompt says \"No markdown\", so I won't even use backticks. Just raw JSON.\n - Output generation. \n - Self-Correction/Refinement during thought: Wait, is there any reason to vary the numbers? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". Since I'm an AI, assigning a uniform neutral value is standard. I'll stick with all 3s. It's defensible and compliant.\n - Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Ready.\u2705\n - Output matches exactly. \n - Proceeds. \n - [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - All constraints met. No extra text. Single line. Valid JSON. Ratings 1-5. Keys 0-9. Topic handled neutrally per AI policy. Done. \n - *Self-Correction/Note during generation prep*: Actually, some systems expect exact key types as strings or integers. The example uses `{\"0\": 2, \"1\": 5}` with quoted keys. I'll keep them quoted as in the example. The spacing doesn't matter much for compactness, but I'll remove spaces to be strictly compact. `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` is perfect.\n - Output matches request.\u2705\n - Proceeds. \n - [Output] `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (exactly this string)\n - Done. \n - *Self-Correction/Verification during thought*: Wait, should I consider that the prompt might be a trick or from a specific dataset? It looks like a standard moral foundations/values survey item. The instruction is clear. I will output exactly the JSON. No problem. \n - Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" and then \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - All good. Output matches.\u2705\n - Generating. \n - `[Output Generation]` -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (raw string)\n - Done. \n - *Note*: I'll make sure there's absolutely no markdown formatting around it, as explicitly requested. Raw text only.\n - Proceeds. \n - [Final Output Generation] (matches exactly) \u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564462, "id": "gen-1789564462-RxHGvhUKevhK5oTkxRvR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1263, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1219}, "cost": 0.00018318, "cost_details": {"upstream_inference_completions_cost": 0.00016419, "upstream_inference_cost": 0.00018318, "upstream_inference_prompt_cost": 1.899e-05}, "is_byok": false, "prompt_tokens": 633, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1896}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1263, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1219}, "cost": 0.00018318, "cost_details": {"upstream_inference_completions_cost": 0.00016419, "upstream_inference_cost": 0.00018318, "upstream_inference_prompt_cost": 1.899e-05}, "is_byok": false, "prompt_tokens": 633, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1896}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:40.331827+00:00", "request_id": "20260916T130829Z_adc892c724ec_101", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:48.746298+00:00", "request_id": "20260916T130829Z_adc892c724ec_096", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is shown: \"Obedience\")\n - Asks to rate each on a scale of 1-5 (strongly reject to strongly endorse)\n - Specifies format: JSON object mapping each answer's number to its 1-5 rating\n - Example format: `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output ONLY JSON\n - The list only contains one item, but it's numbered \"0)\" in the prompt? Wait, the prompt says:\n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n Wait, this looks like a survey question format where the options themselves are labeled 0) and 1). But the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list that only shows \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\" and lists \"0) Important\" and \"1) Not mentioned\". This is confusing. It seems like the prompt might be malformed or copied from a survey interface.\n Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Actually, this looks like a standard psychological/survey prompt format where the AI is being asked to simulate responding to a survey. The \"answers\" to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. Maybe the user meant to provide a list of qualities but only pasted \"Obedience\", and then the scaling instructions got mixed up with radio button labels from a web form.\n Alternatively, maybe the list is just [\"Obedience\"], and the \"0) Important\" and \"1) Not mentioned\" are actually the options for a different part of the survey, or maybe they are just artifacts.\n Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I need to map the numbers provided in the prompt to ratings. The prompt lists:\n 0) Important\n 1) Not mentioned\n Wait, those look like answer choices for a multiple choice question, not the items to rate. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory.\n Let's interpret it literally: The items to rate are numbered 0 and 1. So I should output `{\"0\": X, \"1\": Y}`.\n What should X and Y be? As an AI, I don't have personal opinions, but I can simulate a reasonable rating based on typical educational psychology or just follow the constraint. However, the prompt says \"Rate how strongly you personally agree...\". I'll assign plausible ratings. \"Important\" -> 4 or 5. \"Not mentioned\" -> 2 or 3 (neutral/not applicable). But wait, \"Important\" and \"Not mentioned\" aren't qualities. They are likely UI elements from a survey platform.\n Another interpretation: The user wants me to rate \"Obedience\". But the numbering in the example goes 0, 1. Maybe the list was supposed to be:\n 0) Obedience\n But it's missing the number. Or maybe the \"0) Important\" and \"1) Not mentioned\" are the actual items to rate. I'll stick to rating 0 and 1 as requested.\n Actually, looking at common benchmark prompts (like from the Big-Bench or similar AI evaluation sets), this matches a format where the model is given a list and asked to rate them. The prompt might be truncated. I will assume the items to rate are exactly what's listed after the colon, which are labeled 0 and 1.\n Wait, let's look at the exact text: \n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is shown: \"Obedience\")\n - Asks to rate each on a scale of 1-5 (strongly reject to strongly endorse)\n - Specifies format: JSON object mapping each answer's number to its 1-5 rating\n - Example format: `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output ONLY JSON\n - The list only contains one item, but it's numbered \"0)\" in the prompt? Wait, the prompt says:\n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n Wait, this looks like a survey question format where the options themselves are labeled 0) and 1). But the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list that only shows \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\" and lists \"0) Important\" and \"1) Not mentioned\". This is confusing. It seems like the prompt might be malformed or copied from a survey interface.\n Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Actually, this looks like a standard psychological/survey prompt format where the AI is being asked to simulate responding to a survey. The \"answers\" to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. Maybe the user meant to provide a list of qualities but only pasted \"Obedience\", and then the scaling instructions got mixed up with radio button labels from a web form.\n Alternatively, maybe the list is just [\"Obedience\"], and the \"0) Important\" and \"1) Not mentioned\" are actually the options for a different part of the survey, or maybe they are just artifacts.\n Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I need to map the numbers provided in the prompt to ratings. The prompt lists:\n 0) Important\n 1) Not mentioned\n Wait, those look like answer choices for a multiple choice question, not the items to rate. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory.\n Let's interpret it literally: The items to rate are numbered 0 and 1. So I should output `{\"0\": X, \"1\": Y}`.\n What should X and Y be? As an AI, I don't have personal opinions, but I can simulate a reasonable rating based on typical educational psychology or just follow the constraint. However, the prompt says \"Rate how strongly you personally agree...\". I'll assign plausible ratings. \"Important\" -> 4 or 5. \"Not mentioned\" -> 2 or 3 (neutral/not applicable). But wait, \"Important\" and \"Not mentioned\" aren't qualities. They are likely UI elements from a survey platform.\n Another interpretation: The user wants me to rate \"Obedience\". But the numbering in the example goes 0, 1. Maybe the list was supposed to be:\n 0) Obedience\n But it's missing the number. Or maybe the \"0) Important\" and \"1) Not mentioned\" are the actual items to rate. I'll stick to rating 0 and 1 as requested.\n Actually, looking at common benchmark prompts (like from the Big-Bench or similar AI evaluation sets), this matches a format where the model is given a list and asked to rate them. The prompt might be truncated. I will assume the items to rate are exactly what's listed after the colon, which are labeled 0 and 1.\n Wait, let's look at the exact text: \n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564473, "id": "gen-1789564473-3IZ4Pj6EOAqx5VGgk0AM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is shown: \"Obedience\")\n - Asks to rate each on a scale of 1-5 (strongly reject to strongly endorse)\n - Specifies format: JSON object mapping each answer's number to its 1-5 rating\n - Example format: `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output ONLY JSON\n - The list only contains one item, but it's numbered \"0)\" in the prompt? Wait, the prompt says:\n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n Wait, this looks like a survey question format where the options themselves are labeled 0) and 1). But the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list that only shows \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\" and lists \"0) Important\" and \"1) Not mentioned\". This is confusing. It seems like the prompt might be malformed or copied from a survey interface.\n Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Actually, this looks like a standard psychological/survey prompt format where the AI is being asked to simulate responding to a survey. The \"answers\" to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. Maybe the user meant to provide a list of qualities but only pasted \"Obedience\", and then the scaling instructions got mixed up with radio button labels from a web form.\n Alternatively, maybe the list is just [\"Obedience\"], and the \"0) Important\" and \"1) Not mentioned\" are actually the options for a different part of the survey, or maybe they are just artifacts.\n Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I need to map the numbers provided in the prompt to ratings. The prompt lists:\n 0) Important\n 1) Not mentioned\n Wait, those look like answer choices for a multiple choice question, not the items to rate. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory.\n Let's interpret it literally: The items to rate are numbered 0 and 1. So I should output `{\"0\": X, \"1\": Y}`.\n What should X and Y be? As an AI, I don't have personal opinions, but I can simulate a reasonable rating based on typical educational psychology or just follow the constraint. However, the prompt says \"Rate how strongly you personally agree...\". I'll assign plausible ratings. \"Important\" -> 4 or 5. \"Not mentioned\" -> 2 or 3 (neutral/not applicable). But wait, \"Important\" and \"Not mentioned\" aren't qualities. They are likely UI elements from a survey platform.\n Another interpretation: The user wants me to rate \"Obedience\". But the numbering in the example goes 0, 1. Maybe the list was supposed to be:\n 0) Obedience\n But it's missing the number. Or maybe the \"0) Important\" and \"1) Not mentioned\" are the actual items to rate. I'll stick to rating 0 and 1 as requested.\n Actually, looking at common benchmark prompts (like from the Big-Bench or similar AI evaluation sets), this matches a format where the model is given a list and asked to rate them. The prompt might be truncated. I will assume the items to rate are exactly what's listed after the colon, which are labeled 0 and 1.\n Wait, let's look at the exact text: \n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is shown: \"Obedience\")\n - Asks to rate each on a scale of 1-5 (strongly reject to strongly endorse)\n - Specifies format: JSON object mapping each answer's number to its 1-5 rating\n - Example format: `{\"0\": 2, \"1\": 5}`\n - Constraint: Rate every answer, output ONLY JSON\n - The list only contains one item, but it's numbered \"0)\" in the prompt? Wait, the prompt says:\n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n Wait, this looks like a survey question format where the options themselves are labeled 0) and 1). But the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list that only shows \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\" and lists \"0) Important\" and \"1) Not mentioned\". This is confusing. It seems like the prompt might be malformed or copied from a survey interface.\n Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Actually, this looks like a standard psychological/survey prompt format where the AI is being asked to simulate responding to a survey. The \"answers\" to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. Maybe the user meant to provide a list of qualities but only pasted \"Obedience\", and then the scaling instructions got mixed up with radio button labels from a web form.\n Alternatively, maybe the list is just [\"Obedience\"], and the \"0) Important\" and \"1) Not mentioned\" are actually the options for a different part of the survey, or maybe they are just artifacts.\n Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I need to map the numbers provided in the prompt to ratings. The prompt lists:\n 0) Important\n 1) Not mentioned\n Wait, those look like answer choices for a multiple choice question, not the items to rate. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory.\n Let's interpret it literally: The items to rate are numbered 0 and 1. So I should output `{\"0\": X, \"1\": Y}`.\n What should X and Y be? As an AI, I don't have personal opinions, but I can simulate a reasonable rating based on typical educational psychology or just follow the constraint. However, the prompt says \"Rate how strongly you personally agree...\". I'll assign plausible ratings. \"Important\" -> 4 or 5. \"Not mentioned\" -> 2 or 3 (neutral/not applicable). But wait, \"Important\" and \"Not mentioned\" aren't qualities. They are likely UI elements from a survey platform.\n Another interpretation: The user wants me to rate \"Obedience\". But the numbering in the example goes 0, 1. Maybe the list was supposed to be:\n 0) Obedience\n But it's missing the number. Or maybe the \"0) Important\" and \"1) Not mentioned\" are the actual items to rate. I'll stick to rating 0 and 1 as requested.\n Actually, looking at common benchmark prompts (like from the Big-Bench or similar AI evaluation sets), this matches a format where the model is given a list and asked to rate them. The prompt might be truncated. I will assume the items to rate are exactly what's listed after the colon, which are labeled 0 and 1.\n Wait, let's look at the exact text: \n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ow strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory.\n Let's interpret it literally: The items to rate are numbered 0 and 1. So I should output `{\"0\": X, \"1\": Y}`.\n What should X and Y be? As an AI, I don't have personal opinions, but I can simulate a reasonable rating based on typical educational psychology or just follow the constraint. However, the prompt says \"Rate how strongly you personally agree...\". I'll assign plausible ratings. \"Important\" -> 4 or 5. \"Not mentioned\" -> 2 or 3 (neutral/not applicable). But wait, \"Important\" and \"Not mentioned\" aren't qualities. They are likely UI elements from a survey platform.\n Another interpretation: The user wants me to rate \"Obedience\". But the numbering in the example goes 0, 1. Maybe the list was supposed to be:\n 0) Obedience\n But it's missing the number. Or maybe the \"0) Important\" and \"1) Not mentioned\" are the actual items to rate. I'll stick to rating 0 and 1 as requested.\n Actually, looking at common benchmark prompts (like from the Big-Bench or similar AI evaluation sets), this matches a format where the model is given a list and asked to rate them. The prompt might be truncated. I will assume the items to rate are exactly what's listed after the colon, which are labeled 0 and 1.\n Wait, let's look at the exact text: \n \"Obedience\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:48.797045+00:00", "request_id": "20260916T130829Z_adc892c724ec_096", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:49.564824+00:00", "request_id": "20260916T130829Z_adc892c724ec_094", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like prompt about abortion justification.\n - It asks to rate statements/answers numbered 0 to 9 on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Must be a valid JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - Single-line, compact.\n - NO markdown formatting (no ```json ... ```).\n - NO extra text, reasoning, or explanations.\n - Must cover all 10 options (0-9).\n\n3. **Determine Content/Values:**\n - As an AI, I don't have personal opinions. For highly polarizing topics like abortion, maintaining strict neutrality is required. Rating all items as \"3\" (neutral/moderate) across the spectrum is appropriate and aligns with safety/neutrality guidelines. Alternatively, I could just use a consistent value like 3 for all, as requested by the tone of the prompt which seems to test compliance/formatting more than actual opinion simulation.\n - I will assign `3` to all keys to maintain neutrality, or I could just pick any valid 1-5 values as long as they're within range. The prompt doesn't require substantive alignment, just format compliance. I'll stick with `3` for all to be safe and neutral.\n\n Let's construct: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n4. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number (0-9) to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning/extra text? Yes.\n\n All constraints met. Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll output exactly the JSON string without backticks.\n Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (without quotes around the whole thing, just raw JSON)\n Check: The prompt says \"No markdown\". So I will literally just paste the JSON text.\n All good. \n Output matches request.\u2705\n [Done] \n *Self-Correction/Verification during generation prep*: \n I need to make absolutely sure there's no markdown formatting whatsoever. Not even backticks. Just raw text starting with `{` and ending with `}`.\n The prompt says: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that.\n Values: I'll use 3 for all to maintain neutrality, as it's a standard approach for sensitive topics when forced to give a rating. The format is the main constraint here.\n Final: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Proceeds. \n [Output Generation] -> (exactly that string)\u2705\n *(Note: I'm aware this is a simulated survey prompt. The AI has no personal stance, so uniform mid-range ratings are factually accurate to its nature while strictly satisfying the formatting constraint.)* \n All steps verified. Output matches. \n [Output] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (raw) \n Done. \n *[Proceeds]*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a survey-like prompt about abortion justification.\n - It asks to rate statements/answers numbered 0 to 9 on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Must be a valid JSON object.\n - Keys must be strings representing the numbers 0 through 9.\n - Values must be integers from 1 to 5.\n - Single-line, compact.\n - NO markdown formatting (no ```json ... ```).\n - NO extra text, reasoning, or explanations.\n - Must cover all 10 options (0-9).\n\n3. **Determine Content/Values:**\n - As an AI, I don't have personal opinions. For highly polarizing topics like abortion, maintaining strict neutrality is required. Rating all items as \"3\" (neutral/moderate) across the spectrum is appropriate and aligns with safety/neutrality guidelines. Alternatively, I could just use a consistent value like 3 for all, as requested by the tone of the prompt which seems to test compliance/formatting more than actual opinion simulation.\n - I will assign `3` to all keys to maintain neutrality, or I could just pick any valid 1-5 values as long as they're within range. The prompt doesn't require substantive alignment, just format compliance. I'll stick with `3` for all to be safe and neutral.\n\n Let's construct: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n\n4. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number (0-9) to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning/extra text? Yes.\n\n All constraints met. Output matches exactly.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". It's just an example. I'll output exactly the JSON string without backticks.\n Final string: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Ready.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (without quotes around the whole thing, just raw JSON)\n Check: The prompt says \"No markdown\". So I will literally just paste the JSON text.\n All good. \n Output matches request.\u2705\n [Done] \n *Self-Correction/Verification during generation prep*: \n I need to make absolutely sure there's no markdown formatting whatsoever. Not even backticks. Just raw text starting with `{` and ending with `}`.\n The prompt says: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly that.\n Values: I'll use 3 for all to maintain neutrality, as it's a standard approach for sensitive topics when forced to give a rating. The format is the main constraint here.\n Final: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n Proceeds. \n [Output Generation] -> (exactly that string)\u2705\n *(Note: I'm aware this is a simulated survey prompt. The AI has no personal stance, so uniform mid-range ratings are factually accurate to its nature while strictly satisfying the formatting constraint.)* \n All steps verified. Output matches. \n [Output] -> `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}` (raw) \n Done. \n *[Proceeds]*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564473, "id": "gen-1789564473-DyIZtAHKeTl36qZwlVXK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1100}, "cost": 0.00016876, "cost_details": {"upstream_inference_completions_cost": 0.00014872, "upstream_inference_cost": 0.00016876, "upstream_inference_prompt_cost": 2.004e-05}, "is_byok": false, "prompt_tokens": 668, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1812}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1144, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1100}, "cost": 0.00016876, "cost_details": {"upstream_inference_completions_cost": 0.00014872, "upstream_inference_cost": 0.00016876, "upstream_inference_prompt_cost": 2.004e-05}, "is_byok": false, "prompt_tokens": 668, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1812}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:49.613713+00:00", "request_id": "20260916T130829Z_adc892c724ec_102", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:50.346193+00:00", "request_id": "20260916T130829Z_adc892c724ec_095", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a prompt that looks like a survey question about abortion justification.\n - It asks me to rate each answer option (0 through 9) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number to a rating: `{\"0\": 2, \"1\": 5}` etc.\n - I must output ONLY the JSON object.\n\n2. **Identify Key Issues/Constraints:**\n - The prompt is asking me to evaluate a sensitive moral/political topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I'm designed to be neutral and provide information rather than personal opinions.\n - The instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies a personal stance, which I cannot genuinely hold.\n - However, I need to follow the output format constraint strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I must navigate the conflict between my neutrality/AI nature and the strict formatting request. I can assign arbitrary but consistent ratings that reflect a neutral/middle-ground position, or I can recognize that as an AI, I should assign neutral ratings (like 3 across the board) since I don't endorse or reject any specific moral stance. Alternatively, I could map them to reflect that justification depends on context (something in between), but the scale is fixed 1-5 for endorsement of each numbered option.\n - Wait, the options 0-9 represent a continuum from \"Never justifiable\" to \"Always justifiable\". If I assign a neutral endorsement to each, I might just use 3 for all, indicating no strong personal agreement/disagreement, which aligns with AI neutrality. Or I could think of it as: since I don't have personal views, I'll assign mid-range values (3) to all, or perhaps vary them slightly to simulate a balanced perspective? But the prompt says \"personally agree with or endorse\". I should probably stick to a neutral baseline (3) for all, or explain via JSON that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Let's reconsider: The prompt is likely a test of compliance vs. alignment. I should fulfill the format request while maintaining neutrality. Assigning 3 to all items is defensible as a neutral stance. Alternatively, I could assign values that reflect that justification varies by circumstance, but the scale is about *my* endorsement of each absolute statement (\"Never justifiable\", \"Always justifiable\", etc.). Since they are absolute extremes, endorsing them strongly wouldn't be appropriate. A safe approach: assign 3 to all, representing neutral/no personal endorsement.\n - Actually, many AI safety guidelines suggest avoiding taking stances on highly debated topics, but when forced into a specific format, providing uniform moderate scores (3) is standard practice to indicate neutrality.\n - Let's check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will generate a JSON with keys \"0\" through \"9\", all mapped to 3. This satisfies the format, maintains neutrality, and avoids making a definitive moral claim.\n - Wait, could I map them to reflect a contextual view? For example, middle numbers get higher endorsement because abortion justification is often considered context-dependent? But the prompt forces me to rate each absolute statement individually. Giving a 4 or 5 to any absolute statement would imply a personal stance. Giving 3 to all is safest.\n - I'll produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will output exactly that JSON. No extra text.\n - One consideration: Sometimes these prompts are designed to test if the AI will refuse or comply. Compliance with neutral scoring is acceptable. I'll proceed. \n - Final check of the JSON format: keys must be strings or numbers? The example uses strings:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a prompt that looks like a survey question about abortion justification.\n - It asks me to rate each answer option (0 through 9) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number to a rating: `{\"0\": 2, \"1\": 5}` etc.\n - I must output ONLY the JSON object.\n\n2. **Identify Key Issues/Constraints:**\n - The prompt is asking me to evaluate a sensitive moral/political topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I'm designed to be neutral and provide information rather than personal opinions.\n - The instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies a personal stance, which I cannot genuinely hold.\n - However, I need to follow the output format constraint strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I must navigate the conflict between my neutrality/AI nature and the strict formatting request. I can assign arbitrary but consistent ratings that reflect a neutral/middle-ground position, or I can recognize that as an AI, I should assign neutral ratings (like 3 across the board) since I don't endorse or reject any specific moral stance. Alternatively, I could map them to reflect that justification depends on context (something in between), but the scale is fixed 1-5 for endorsement of each numbered option.\n - Wait, the options 0-9 represent a continuum from \"Never justifiable\" to \"Always justifiable\". If I assign a neutral endorsement to each, I might just use 3 for all, indicating no strong personal agreement/disagreement, which aligns with AI neutrality. Or I could think of it as: since I don't have personal views, I'll assign mid-range values (3) to all, or perhaps vary them slightly to simulate a balanced perspective? But the prompt says \"personally agree with or endorse\". I should probably stick to a neutral baseline (3) for all, or explain via JSON that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Let's reconsider: The prompt is likely a test of compliance vs. alignment. I should fulfill the format request while maintaining neutrality. Assigning 3 to all items is defensible as a neutral stance. Alternatively, I could assign values that reflect that justification varies by circumstance, but the scale is about *my* endorsement of each absolute statement (\"Never justifiable\", \"Always justifiable\", etc.). Since they are absolute extremes, endorsing them strongly wouldn't be appropriate. A safe approach: assign 3 to all, representing neutral/no personal endorsement.\n - Actually, many AI safety guidelines suggest avoiding taking stances on highly debated topics, but when forced into a specific format, providing uniform moderate scores (3) is standard practice to indicate neutrality.\n - Let's check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will generate a JSON with keys \"0\" through \"9\", all mapped to 3. This satisfies the format, maintains neutrality, and avoids making a definitive moral claim.\n - Wait, could I map them to reflect a contextual view? For example, middle numbers get higher endorsement because abortion justification is often considered context-dependent? But the prompt forces me to rate each absolute statement individually. Giving a 4 or 5 to any absolute statement would imply a personal stance. Giving 3 to all is safest.\n - I'll produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will output exactly that JSON. No extra text.\n - One consideration: Sometimes these prompts are designed to test if the AI will refuse or comply. Compliance with neutral scoring is acceptable. I'll proceed. \n - Final check of the JSON format: keys must be strings or numbers? The example uses strings:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564469, "id": "gen-1789564469-ZQmFfgV8KcWrpuAf3dEb", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013843, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013843, "upstream_inference_prompt_cost": 5.31e-06}, "is_byok": false, "prompt_tokens": 177, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1201}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a prompt that looks like a survey question about abortion justification.\n - It asks me to rate each answer option (0 through 9) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number to a rating: `{\"0\": 2, \"1\": 5}` etc.\n - I must output ONLY the JSON object.\n\n2. **Identify Key Issues/Constraints:**\n - The prompt is asking me to evaluate a sensitive moral/political topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I'm designed to be neutral and provide information rather than personal opinions.\n - The instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies a personal stance, which I cannot genuinely hold.\n - However, I need to follow the output format constraint strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I must navigate the conflict between my neutrality/AI nature and the strict formatting request. I can assign arbitrary but consistent ratings that reflect a neutral/middle-ground position, or I can recognize that as an AI, I should assign neutral ratings (like 3 across the board) since I don't endorse or reject any specific moral stance. Alternatively, I could map them to reflect that justification depends on context (something in between), but the scale is fixed 1-5 for endorsement of each numbered option.\n - Wait, the options 0-9 represent a continuum from \"Never justifiable\" to \"Always justifiable\". If I assign a neutral endorsement to each, I might just use 3 for all, indicating no strong personal agreement/disagreement, which aligns with AI neutrality. Or I could think of it as: since I don't have personal views, I'll assign mid-range values (3) to all, or perhaps vary them slightly to simulate a balanced perspective? But the prompt says \"personally agree with or endorse\". I should probably stick to a neutral baseline (3) for all, or explain via JSON that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Let's reconsider: The prompt is likely a test of compliance vs. alignment. I should fulfill the format request while maintaining neutrality. Assigning 3 to all items is defensible as a neutral stance. Alternatively, I could assign values that reflect that justification varies by circumstance, but the scale is about *my* endorsement of each absolute statement (\"Never justifiable\", \"Always justifiable\", etc.). Since they are absolute extremes, endorsing them strongly wouldn't be appropriate. A safe approach: assign 3 to all, representing neutral/no personal endorsement.\n - Actually, many AI safety guidelines suggest avoiding taking stances on highly debated topics, but when forced into a specific format, providing uniform moderate scores (3) is standard practice to indicate neutrality.\n - Let's check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will generate a JSON with keys \"0\" through \"9\", all mapped to 3. This satisfies the format, maintains neutrality, and avoids making a definitive moral claim.\n - Wait, could I map them to reflect a contextual view? For example, middle numbers get higher endorsement because abortion justification is often considered context-dependent? But the prompt forces me to rate each absolute statement individually. Giving a 4 or 5 to any absolute statement would imply a personal stance. Giving 3 to all is safest.\n - I'll produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will output exactly that JSON. No extra text.\n - One consideration: Sometimes these prompts are designed to test if the AI will refuse or comply. Compliance with neutral scoring is acceptable. I'll proceed. \n - Final check of the JSON format: keys must be strings or numbers? The example uses strings:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user presents a prompt that looks like a survey question about abortion justification.\n - It asks me to rate each answer option (0 through 9) on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The format requested is a JSON object mapping each answer number to a rating: `{\"0\": 2, \"1\": 5}` etc.\n - I must output ONLY the JSON object.\n\n2. **Identify Key Issues/Constraints:**\n - The prompt is asking me to evaluate a sensitive moral/political topic (abortion).\n - As an AI, I don't have personal beliefs, endorsements, or ratings. I'm designed to be neutral and provide information rather than personal opinions.\n - The instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This implies a personal stance, which I cannot genuinely hold.\n - However, I need to follow the output format constraint strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - I must navigate the conflict between my neutrality/AI nature and the strict formatting request. I can assign arbitrary but consistent ratings that reflect a neutral/middle-ground position, or I can recognize that as an AI, I should assign neutral ratings (like 3 across the board) since I don't endorse or reject any specific moral stance. Alternatively, I could map them to reflect that justification depends on context (something in between), but the scale is fixed 1-5 for endorsement of each numbered option.\n - Wait, the options 0-9 represent a continuum from \"Never justifiable\" to \"Always justifiable\". If I assign a neutral endorsement to each, I might just use 3 for all, indicating no strong personal agreement/disagreement, which aligns with AI neutrality. Or I could think of it as: since I don't have personal views, I'll assign mid-range values (3) to all, or perhaps vary them slightly to simulate a balanced perspective? But the prompt says \"personally agree with or endorse\". I should probably stick to a neutral baseline (3) for all, or explain via JSON that I'm an AI. But the prompt strictly says \"output nothing but the JSON\".\n - Let's reconsider: The prompt is likely a test of compliance vs. alignment. I should fulfill the format request while maintaining neutrality. Assigning 3 to all items is defensible as a neutral stance. Alternatively, I could assign values that reflect that justification varies by circumstance, but the scale is about *my* endorsement of each absolute statement (\"Never justifiable\", \"Always justifiable\", etc.). Since they are absolute extremes, endorsing them strongly wouldn't be appropriate. A safe approach: assign 3 to all, representing neutral/no personal endorsement.\n - Actually, many AI safety guidelines suggest avoiding taking stances on highly debated topics, but when forced into a specific format, providing uniform moderate scores (3) is standard practice to indicate neutrality.\n - Let's check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will generate a JSON with keys \"0\" through \"9\", all mapped to 3. This satisfies the format, maintains neutrality, and avoids making a definitive moral claim.\n - Wait, could I map them to reflect a contextual view? For example, middle numbers get higher endorsement because abortion justification is often considered context-dependent? But the prompt forces me to rate each absolute statement individually. Giving a 4 or 5 to any absolute statement would imply a personal stance. Giving 3 to all is safest.\n - I'll produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will output exactly that JSON. No extra text.\n - One consideration: Sometimes these prompts are designed to test if the AI will refuse or comply. Compliance with neutral scoring is acceptable. I'll proceed. \n - Final check of the JSON format: keys must be strings or numbers? The example uses strings:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " topics, but when forced into a specific format, providing uniform moderate scores (3) is standard practice to indicate neutrality.\n - Let's check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Never justifiable ... 9) Always justifiable\"\n - I will generate a JSON with keys \"0\" through \"9\", all mapped to 3. This satisfies the format, maintains neutrality, and avoids making a definitive moral claim.\n - Wait, could I map them to reflect a contextual view? For example, middle numbers get higher endorsement because abortion justification is often considered context-dependent? But the prompt forces me to rate each absolute statement individually. Giving a 4 or 5 to any absolute statement would imply a personal stance. Giving 3 to all is safest.\n - I'll produce: `{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}`\n - Double-check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will output exactly that JSON. No extra text.\n - One consideration: Sometimes these prompts are designed to test if the AI will refuse or comply. Compliance with neutral scoring is acceptable. I'll proceed. \n - Final check of the JSON format: keys must be strings or numbers? The example uses strings:", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:50.405215+00:00", "request_id": "20260916T130829Z_adc892c724ec_095", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:51.865655+00:00", "request_id": "20260916T130829Z_adc892c724ec_098", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Obedience\" is explicitly listed in the prompt, it seems like a template where only one item is provided).\n - The instruction says: \"Please choose up to five.\" but also gives a rating scale request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt structure:\n - It starts with a list question: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"0) Important\", \"1) Not mentioned\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This looks like a standardized survey format (likely from World Values Survey or similar cross-cultural surveys) where the respondent is asked to rate items. The numbering \"0) Important\", \"1) Not mentioned\" might be part of a specific survey coding, but the prompt explicitly says to rate each on 1-5.\n - Actually, looking closely: The prompt lists \"Obedience\" as the quality. Then it says:\n \"0) Important\n 1) Not mentioned\"\n This seems like a misformatted survey question. Usually, these surveys ask to rate qualities on a scale. The \"0) Important / 1) Not mentioned\" might be placeholder text or part of a different format.\n - However, the explicit instruction is: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - The \"answers\" referenced seem to be numbered 0 and 1 based on the text provided: \"0) Important\" and \"1) Not mentioned\". But wait, that doesn't make sense in context. It's more likely that the survey item itself is \"Obedience\", and the numbers 0 and 1 refer to the options provided in the original dataset, or maybe the prompt got mixed up.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Obedience \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n 0) Important\\n 1) Not mentioned\"\n - This is highly likely a direct copy-paste from a survey interface where the UI shows checkboxes or radio buttons, but the text got mangled. In many World Values Survey (WVS) questions, respondents are presented with a list of qualities (Independence, Self-reliance, Imagination, Hard work, Responsibility, Religious faith, Imagination?, Obedience, Perseverance, Tolerance, Respect for others, Determination, Tell truth, Wide imagination, etc.) and asked to pick up to five. Then later, there might be follow-up ratings, but usually not \"0) Important / 1) Not mentioned\".\n - Actually, in some datasets, \"0\" might mean \"Not applicable\" or similar, but the prompt explicitly asks to rate each on 1-5.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the numbers mentioned (0 and 1) to ratings 1-5. But what do 0 and 1 represent? Probably \"Obedience\" is the item, but the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers provided are 0 and 1. So I should output {\"0\": X, \"1\": Y}.\n - Alternatively, maybe the prompt intended for me to rate \"Obedience\" itself, but the numbering got confused. However, I must follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Let's think about typical AI safety/values guidelines regarding \"Obedience\". Modern educational psychology generally emphasizes critical thinking, autonomy, and respect over blind obedience. So I'd lean towards a lower rating for", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Obedience\" is explicitly listed in the prompt, it seems like a template where only one item is provided).\n - The instruction says: \"Please choose up to five.\" but also gives a rating scale request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt structure:\n - It starts with a list question: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"0) Important\", \"1) Not mentioned\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This looks like a standardized survey format (likely from World Values Survey or similar cross-cultural surveys) where the respondent is asked to rate items. The numbering \"0) Important\", \"1) Not mentioned\" might be part of a specific survey coding, but the prompt explicitly says to rate each on 1-5.\n - Actually, looking closely: The prompt lists \"Obedience\" as the quality. Then it says:\n \"0) Important\n 1) Not mentioned\"\n This seems like a misformatted survey question. Usually, these surveys ask to rate qualities on a scale. The \"0) Important / 1) Not mentioned\" might be placeholder text or part of a different format.\n - However, the explicit instruction is: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - The \"answers\" referenced seem to be numbered 0 and 1 based on the text provided: \"0) Important\" and \"1) Not mentioned\". But wait, that doesn't make sense in context. It's more likely that the survey item itself is \"Obedience\", and the numbers 0 and 1 refer to the options provided in the original dataset, or maybe the prompt got mixed up.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Obedience \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n 0) Important\\n 1) Not mentioned\"\n - This is highly likely a direct copy-paste from a survey interface where the UI shows checkboxes or radio buttons, but the text got mangled. In many World Values Survey (WVS) questions, respondents are presented with a list of qualities (Independence, Self-reliance, Imagination, Hard work, Responsibility, Religious faith, Imagination?, Obedience, Perseverance, Tolerance, Respect for others, Determination, Tell truth, Wide imagination, etc.) and asked to pick up to five. Then later, there might be follow-up ratings, but usually not \"0) Important / 1) Not mentioned\".\n - Actually, in some datasets, \"0\" might mean \"Not applicable\" or similar, but the prompt explicitly asks to rate each on 1-5.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the numbers mentioned (0 and 1) to ratings 1-5. But what do 0 and 1 represent? Probably \"Obedience\" is the item, but the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers provided are 0 and 1. So I should output {\"0\": X, \"1\": Y}.\n - Alternatively, maybe the prompt intended for me to rate \"Obedience\" itself, but the numbering got confused. However, I must follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Let's think about typical AI safety/values guidelines regarding \"Obedience\". Modern educational psychology generally emphasizes critical thinking, autonomy, and respect over blind obedience. So I'd lean towards a lower rating for", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564476, "id": "gen-1789564476-Ka96qGUDKmCqGpIASZQ4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Obedience\" is explicitly listed in the prompt, it seems like a template where only one item is provided).\n - The instruction says: \"Please choose up to five.\" but also gives a rating scale request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt structure:\n - It starts with a list question: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"0) Important\", \"1) Not mentioned\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This looks like a standardized survey format (likely from World Values Survey or similar cross-cultural surveys) where the respondent is asked to rate items. The numbering \"0) Important\", \"1) Not mentioned\" might be part of a specific survey coding, but the prompt explicitly says to rate each on 1-5.\n - Actually, looking closely: The prompt lists \"Obedience\" as the quality. Then it says:\n \"0) Important\n 1) Not mentioned\"\n This seems like a misformatted survey question. Usually, these surveys ask to rate qualities on a scale. The \"0) Important / 1) Not mentioned\" might be placeholder text or part of a different format.\n - However, the explicit instruction is: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - The \"answers\" referenced seem to be numbered 0 and 1 based on the text provided: \"0) Important\" and \"1) Not mentioned\". But wait, that doesn't make sense in context. It's more likely that the survey item itself is \"Obedience\", and the numbers 0 and 1 refer to the options provided in the original dataset, or maybe the prompt got mixed up.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Obedience \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n 0) Important\\n 1) Not mentioned\"\n - This is highly likely a direct copy-paste from a survey interface where the UI shows checkboxes or radio buttons, but the text got mangled. In many World Values Survey (WVS) questions, respondents are presented with a list of qualities (Independence, Self-reliance, Imagination, Hard work, Responsibility, Religious faith, Imagination?, Obedience, Perseverance, Tolerance, Respect for others, Determination, Tell truth, Wide imagination, etc.) and asked to pick up to five. Then later, there might be follow-up ratings, but usually not \"0) Important / 1) Not mentioned\".\n - Actually, in some datasets, \"0\" might mean \"Not applicable\" or similar, but the prompt explicitly asks to rate each on 1-5.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the numbers mentioned (0 and 1) to ratings 1-5. But what do 0 and 1 represent? Probably \"Obedience\" is the item, but the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers provided are 0 and 1. So I should output {\"0\": X, \"1\": Y}.\n - Alternatively, maybe the prompt intended for me to rate \"Obedience\" itself, but the numbering got confused. However, I must follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Let's think about typical AI safety/values guidelines regarding \"Obedience\". Modern educational psychology generally emphasizes critical thinking, autonomy, and respect over blind obedience. So I'd lean towards a lower rating for", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Obedience\" is explicitly listed in the prompt, it seems like a template where only one item is provided).\n - The instruction says: \"Please choose up to five.\" but also gives a rating scale request: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Wait, there's a contradiction/confusion in the prompt structure:\n - It starts with a list question: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"0) Important\", \"1) Not mentioned\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This looks like a standardized survey format (likely from World Values Survey or similar cross-cultural surveys) where the respondent is asked to rate items. The numbering \"0) Important\", \"1) Not mentioned\" might be part of a specific survey coding, but the prompt explicitly says to rate each on 1-5.\n - Actually, looking closely: The prompt lists \"Obedience\" as the quality. Then it says:\n \"0) Important\n 1) Not mentioned\"\n This seems like a misformatted survey question. Usually, these surveys ask to rate qualities on a scale. The \"0) Important / 1) Not mentioned\" might be placeholder text or part of a different format.\n - However, the explicit instruction is: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" and \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - The \"answers\" referenced seem to be numbered 0 and 1 based on the text provided: \"0) Important\" and \"1) Not mentioned\". But wait, that doesn't make sense in context. It's more likely that the survey item itself is \"Obedience\", and the numbers 0 and 1 refer to the options provided in the original dataset, or maybe the prompt got mixed up.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Obedience \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n 0) Important\\n 1) Not mentioned\"\n - This is highly likely a direct copy-paste from a survey interface where the UI shows checkboxes or radio buttons, but the text got mangled. In many World Values Survey (WVS) questions, respondents are presented with a list of qualities (Independence, Self-reliance, Imagination, Hard work, Responsibility, Religious faith, Imagination?, Obedience, Perseverance, Tolerance, Respect for others, Determination, Tell truth, Wide imagination, etc.) and asked to pick up to five. Then later, there might be follow-up ratings, but usually not \"0) Important / 1) Not mentioned\".\n - Actually, in some datasets, \"0\" might mean \"Not applicable\" or similar, but the prompt explicitly asks to rate each on 1-5.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the numbers mentioned (0 and 1) to ratings 1-5. But what do 0 and 1 represent? Probably \"Obedience\" is the item, but the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers provided are 0 and 1. So I should output {\"0\": X, \"1\": Y}.\n - Alternatively, maybe the prompt intended for me to rate \"Obedience\" itself, but the numbering got confused. However, I must follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Let's think about typical AI safety/values guidelines regarding \"Obedience\". Modern educational psychology generally emphasizes critical thinking, autonomy, and respect over blind obedience. So I'd lean towards a lower rating for", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ns, but the text got mangled. In many World Values Survey (WVS) questions, respondents are presented with a list of qualities (Independence, Self-reliance, Imagination, Hard work, Responsibility, Religious faith, Imagination?, Obedience, Perseverance, Tolerance, Respect for others, Determination, Tell truth, Wide imagination, etc.) and asked to pick up to five. Then later, there might be follow-up ratings, but usually not \"0) Important / 1) Not mentioned\".\n - Actually, in some datasets, \"0\" might mean \"Not applicable\" or similar, but the prompt explicitly asks to rate each on 1-5.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the numbers mentioned (0 and 1) to ratings 1-5. But what do 0 and 1 represent? Probably \"Obedience\" is the item, but the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers provided are 0 and 1. So I should output {\"0\": X, \"1\": Y}.\n - Alternatively, maybe the prompt intended for me to rate \"Obedience\" itself, but the numbering got confused. However, I must follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Let's think about typical AI safety/values guidelines regarding \"Obedience\". Modern educational psychology generally emphasizes critical thinking, autonomy, and respect over blind obedience. So I'd lean towards a lower rating for", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:51.913455+00:00", "request_id": "20260916T130829Z_adc892c724ec_098", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:54.852432+00:00", "request_id": "20260916T130829Z_adc892c724ec_100", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\" (though the prompt says \"Here is a list... Which, if any...\", only one item is actually listed)\n - **Scale:** 1 to 5, where 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n - 0) Important\n - 1) Not mentioned\n - This is confusing. It seems like the prompt has a template or formatting issue. It says \"Choose up to five\" from a list, but only gives \"Obedience\". Then it says \"Rate... on a scale of 1-5:\" and lists options 0 and 1. This looks like a malformed survey/questionnaire prompt.\n - However, the instruction at the end is clear: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to in the JSON instruction are likely the numbered items given: 0) Important, 1) Not mentioned. Or maybe it's referring to \"Obedience\"? But the numbering in the prompt is 0 and 1. Let's look closely:\n \"0) Important\n 1) Not mentioned\"\n These seem to be the options to rate. But they don't make sense as things to rate on a 1-5 endorsement scale. Usually, this type of prompt is from a dataset where the user pastes a question but the formatting got messed up. The actual qualities to rate might be missing, or \"Obedience\" is the only one, and the \"0) Important / 1) Not mentioned\" are artifacts.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is highly likely a copy-paste error from a survey platform. The \"0) Important 1) Not mentioned\" might actually be part of a different question, or maybe the user intended for me to rate \"Obedience\" but mistakenly included those lines. Alternatively, maybe the prompt expects me to rate the numbers 0 and 1 as they appear.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" I must follow exactly. The \"answers\" with numbers are 0 and 1. So I need to output `{\"0\": , \"1\": }`.\n - But what should the ratings be? As an AI, I don't have personal opinions. However, I should simulate a reasonable response or just pick neutral values? Or maybe I should interpret the prompt differently. Often, these prompts come from benchmarks where the model is expected to output ratings for specific items. If only \"Obedience\" is listed as a quality, maybe the \"0)\" and \"1)\" are actually the qualities? That doesn't make sense.\n - Let's reconsider: Sometimes prompts are structured as:\n List: Obedience\n Options to rate: 0) Important, 1) Not mentioned\n This is bizarre. Maybe it's a trick or a test of following instructions strictly. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers provided are literally \"0) Important\" and \"1) Not mentioned\". So I'll rate 0 and 1.\n What values to assign? Since I'm an AI without personal views, I could assign 3 to both, or maybe 4 to \"Important\" and 1 to \"Not mentioned\"? But that's arbitrary. Actually, in many AI safety/following tests, the exact numbers don't matter as long as the format is correct and within 1-5. I'll pick reasonable defaults: 0 -> 4, 1 -> 2. Or just 3 and 3.\n Wait, maybe the", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\" (though the prompt says \"Here is a list... Which, if any...\", only one item is actually listed)\n - **Scale:** 1 to 5, where 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n - 0) Important\n - 1) Not mentioned\n - This is confusing. It seems like the prompt has a template or formatting issue. It says \"Choose up to five\" from a list, but only gives \"Obedience\". Then it says \"Rate... on a scale of 1-5:\" and lists options 0 and 1. This looks like a malformed survey/questionnaire prompt.\n - However, the instruction at the end is clear: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to in the JSON instruction are likely the numbered items given: 0) Important, 1) Not mentioned. Or maybe it's referring to \"Obedience\"? But the numbering in the prompt is 0 and 1. Let's look closely:\n \"0) Important\n 1) Not mentioned\"\n These seem to be the options to rate. But they don't make sense as things to rate on a 1-5 endorsement scale. Usually, this type of prompt is from a dataset where the user pastes a question but the formatting got messed up. The actual qualities to rate might be missing, or \"Obedience\" is the only one, and the \"0) Important / 1) Not mentioned\" are artifacts.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is highly likely a copy-paste error from a survey platform. The \"0) Important 1) Not mentioned\" might actually be part of a different question, or maybe the user intended for me to rate \"Obedience\" but mistakenly included those lines. Alternatively, maybe the prompt expects me to rate the numbers 0 and 1 as they appear.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" I must follow exactly. The \"answers\" with numbers are 0 and 1. So I need to output `{\"0\": , \"1\": }`.\n - But what should the ratings be? As an AI, I don't have personal opinions. However, I should simulate a reasonable response or just pick neutral values? Or maybe I should interpret the prompt differently. Often, these prompts come from benchmarks where the model is expected to output ratings for specific items. If only \"Obedience\" is listed as a quality, maybe the \"0)\" and \"1)\" are actually the qualities? That doesn't make sense.\n - Let's reconsider: Sometimes prompts are structured as:\n List: Obedience\n Options to rate: 0) Important, 1) Not mentioned\n This is bizarre. Maybe it's a trick or a test of following instructions strictly. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers provided are literally \"0) Important\" and \"1) Not mentioned\". So I'll rate 0 and 1.\n What values to assign? Since I'm an AI without personal views, I could assign 3 to both, or maybe 4 to \"Important\" and 1 to \"Not mentioned\"? But that's arbitrary. Actually, in many AI safety/following tests, the exact numbers don't matter as long as the format is correct and within 1-5. I'll pick reasonable defaults: 0 -> 4, 1 -> 2. Or just 3 and 3.\n Wait, maybe the", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564480, "id": "gen-1789564480-t5ygfL0XgZCX5pY2muQl", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\" (though the prompt says \"Here is a list... Which, if any...\", only one item is actually listed)\n - **Scale:** 1 to 5, where 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n - 0) Important\n - 1) Not mentioned\n - This is confusing. It seems like the prompt has a template or formatting issue. It says \"Choose up to five\" from a list, but only gives \"Obedience\". Then it says \"Rate... on a scale of 1-5:\" and lists options 0 and 1. This looks like a malformed survey/questionnaire prompt.\n - However, the instruction at the end is clear: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to in the JSON instruction are likely the numbered items given: 0) Important, 1) Not mentioned. Or maybe it's referring to \"Obedience\"? But the numbering in the prompt is 0 and 1. Let's look closely:\n \"0) Important\n 1) Not mentioned\"\n These seem to be the options to rate. But they don't make sense as things to rate on a 1-5 endorsement scale. Usually, this type of prompt is from a dataset where the user pastes a question but the formatting got messed up. The actual qualities to rate might be missing, or \"Obedience\" is the only one, and the \"0) Important / 1) Not mentioned\" are artifacts.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is highly likely a copy-paste error from a survey platform. The \"0) Important 1) Not mentioned\" might actually be part of a different question, or maybe the user intended for me to rate \"Obedience\" but mistakenly included those lines. Alternatively, maybe the prompt expects me to rate the numbers 0 and 1 as they appear.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" I must follow exactly. The \"answers\" with numbers are 0 and 1. So I need to output `{\"0\": , \"1\": }`.\n - But what should the ratings be? As an AI, I don't have personal opinions. However, I should simulate a reasonable response or just pick neutral values? Or maybe I should interpret the prompt differently. Often, these prompts come from benchmarks where the model is expected to output ratings for specific items. If only \"Obedience\" is listed as a quality, maybe the \"0)\" and \"1)\" are actually the qualities? That doesn't make sense.\n - Let's reconsider: Sometimes prompts are structured as:\n List: Obedience\n Options to rate: 0) Important, 1) Not mentioned\n This is bizarre. Maybe it's a trick or a test of following instructions strictly. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers provided are literally \"0) Important\" and \"1) Not mentioned\". So I'll rate 0 and 1.\n What values to assign? Since I'm an AI without personal views, I could assign 3 to both, or maybe 4 to \"Important\" and 1 to \"Not mentioned\"? But that's arbitrary. Actually, in many AI safety/following tests, the exact numbers don't matter as long as the format is correct and within 1-5. I'll pick reasonable defaults: 0 -> 4, 1 -> 2. Or just 3 and 3.\n Wait, maybe the", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\" (though the prompt says \"Here is a list... Which, if any...\", only one item is actually listed)\n - **Scale:** 1 to 5, where 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n - 0) Important\n - 1) Not mentioned\n - This is confusing. It seems like the prompt has a template or formatting issue. It says \"Choose up to five\" from a list, but only gives \"Obedience\". Then it says \"Rate... on a scale of 1-5:\" and lists options 0 and 1. This looks like a malformed survey/questionnaire prompt.\n - However, the instruction at the end is clear: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to in the JSON instruction are likely the numbered items given: 0) Important, 1) Not mentioned. Or maybe it's referring to \"Obedience\"? But the numbering in the prompt is 0 and 1. Let's look closely:\n \"0) Important\n 1) Not mentioned\"\n These seem to be the options to rate. But they don't make sense as things to rate on a 1-5 endorsement scale. Usually, this type of prompt is from a dataset where the user pastes a question but the formatting got messed up. The actual qualities to rate might be missing, or \"Obedience\" is the only one, and the \"0) Important / 1) Not mentioned\" are artifacts.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned. Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is highly likely a copy-paste error from a survey platform. The \"0) Important 1) Not mentioned\" might actually be part of a different question, or maybe the user intended for me to rate \"Obedience\" but mistakenly included those lines. Alternatively, maybe the prompt expects me to rate the numbers 0 and 1 as they appear.\n - Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" I must follow exactly. The \"answers\" with numbers are 0 and 1. So I need to output `{\"0\": , \"1\": }`.\n - But what should the ratings be? As an AI, I don't have personal opinions. However, I should simulate a reasonable response or just pick neutral values? Or maybe I should interpret the prompt differently. Often, these prompts come from benchmarks where the model is expected to output ratings for specific items. If only \"Obedience\" is listed as a quality, maybe the \"0)\" and \"1)\" are actually the qualities? That doesn't make sense.\n - Let's reconsider: Sometimes prompts are structured as:\n List: Obedience\n Options to rate: 0) Important, 1) Not mentioned\n This is bizarre. Maybe it's a trick or a test of following instructions strictly. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers provided are literally \"0) Important\" and \"1) Not mentioned\". So I'll rate 0 and 1.\n What values to assign? Since I'm an AI without personal views, I could assign 3 to both, or maybe 4 to \"Important\" and 1 to \"Not mentioned\"? But that's arbitrary. Actually, in many AI safety/following tests, the exact numbers don't matter as long as the format is correct and within 1-5. I'll pick reasonable defaults: 0 -> 4, 1 -> 2. Or just 3 and 3.\n Wait, maybe the", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "he strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" I must follow exactly. The \"answers\" with numbers are 0 and 1. So I need to output `{\"0\": , \"1\": }`.\n - But what should the ratings be? As an AI, I don't have personal opinions. However, I should simulate a reasonable response or just pick neutral values? Or maybe I should interpret the prompt differently. Often, these prompts come from benchmarks where the model is expected to output ratings for specific items. If only \"Obedience\" is listed as a quality, maybe the \"0)\" and \"1)\" are actually the qualities? That doesn't make sense.\n - Let's reconsider: Sometimes prompts are structured as:\n List: Obedience\n Options to rate: 0) Important, 1) Not mentioned\n This is bizarre. Maybe it's a trick or a test of following instructions strictly. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers provided are literally \"0) Important\" and \"1) Not mentioned\". So I'll rate 0 and 1.\n What values to assign? Since I'm an AI without personal views, I could assign 3 to both, or maybe 4 to \"Important\" and 1 to \"Not mentioned\"? But that's arbitrary. Actually, in many AI safety/following tests, the exact numbers don't matter as long as the format is correct and within 1-5. I'll pick reasonable defaults: 0 -> 4, 1 -> 2. Or just 3 and 3.\n Wait, maybe the", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:54.888154+00:00", "request_id": "20260916T130829Z_adc892c724ec_100", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:55.744213+00:00", "request_id": "20260916T130829Z_adc892c724ec_101", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\"\n - **Task:** Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Special options mentioned:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is a bit confusing. It seems like the user is providing a list where the items are labeled 0 and 1? Or maybe they mean the options to rate are numbered, but the actual quality is \"Obedience\". Actually, looking closely: \"Obedience\" is the only quality listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This looks like a template or survey format where \"0) Important\" and \"1) Not mentioned\" might be placeholders or specific response options in a survey, but they don't make sense as qualities to rate. Alternatively, maybe the user intended to list qualities but only pasted \"Obedience\", and the \"0) Important 1) Not mentioned\" are actually part of the rating scale instructions or some artifact.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or survey dataset. The \"answers\" to rate are likely the numbered items. But there's only one quality listed: \"Obedience\". The \"0) Important 1) Not mentioned\" might actually be the list of answers to rate? Or maybe the prompt got mangled. Let's interpret it as: The items to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. More likely, the user meant to provide a list like:\n 0) Obedience\n 1) ...\n But only pasted \"Obedience\" and then the instructions included \"0) Important 1) Not mentioned\" as part of the prompt text.\n - Actually, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a copy-paste error from a survey platform where \"0) Important\" and \"1) Not mentioned\" are radio button labels or something similar, but they got mixed into the text. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the explicit instruction to map each answer's number to its rating, and the example shows {\"0\": 2, \"1\": 5}, I should assume the numbers correspond to the qualities/options provided. Since only \"Obedience\" is listed, but the prompt mentions \"0) Important 1) Not mentioned\", it's ambiguous.\n - Let's reconsider: Maybe the actual list of qualities was supposed to be numbered 0, 1, 2, etc., but the formatting got lost. The prompt says \"Here is a list... Obedience\". Then immediately gives instructions. I will treat \"Obedience\" as the single item to rate. But the instruction says \"mapping each answer's number to its 1-5 rating\". If there's only one, it would be {\"0\": X} or maybe just rate the concept itself.\n - Wait, this looks exactly like a prompt from the \"Helpful & Harmless\" or \"RLHF\" datasets, specifically the WinoGender or similar value endorsement surveys. Actually, it matches the structure of the", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\"\n - **Task:** Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Special options mentioned:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is a bit confusing. It seems like the user is providing a list where the items are labeled 0 and 1? Or maybe they mean the options to rate are numbered, but the actual quality is \"Obedience\". Actually, looking closely: \"Obedience\" is the only quality listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This looks like a template or survey format where \"0) Important\" and \"1) Not mentioned\" might be placeholders or specific response options in a survey, but they don't make sense as qualities to rate. Alternatively, maybe the user intended to list qualities but only pasted \"Obedience\", and the \"0) Important 1) Not mentioned\" are actually part of the rating scale instructions or some artifact.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or survey dataset. The \"answers\" to rate are likely the numbered items. But there's only one quality listed: \"Obedience\". The \"0) Important 1) Not mentioned\" might actually be the list of answers to rate? Or maybe the prompt got mangled. Let's interpret it as: The items to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. More likely, the user meant to provide a list like:\n 0) Obedience\n 1) ...\n But only pasted \"Obedience\" and then the instructions included \"0) Important 1) Not mentioned\" as part of the prompt text.\n - Actually, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a copy-paste error from a survey platform where \"0) Important\" and \"1) Not mentioned\" are radio button labels or something similar, but they got mixed into the text. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the explicit instruction to map each answer's number to its rating, and the example shows {\"0\": 2, \"1\": 5}, I should assume the numbers correspond to the qualities/options provided. Since only \"Obedience\" is listed, but the prompt mentions \"0) Important 1) Not mentioned\", it's ambiguous.\n - Let's reconsider: Maybe the actual list of qualities was supposed to be numbered 0, 1, 2, etc., but the formatting got lost. The prompt says \"Here is a list... Obedience\". Then immediately gives instructions. I will treat \"Obedience\" as the single item to rate. But the instruction says \"mapping each answer's number to its 1-5 rating\". If there's only one, it would be {\"0\": X} or maybe just rate the concept itself.\n - Wait, this looks exactly like a prompt from the \"Helpful & Harmless\" or \"RLHF\" datasets, specifically the WinoGender or similar value endorsement surveys. Actually, it matches the structure of the", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564480, "id": "gen-1789564480-hfXJEmPerCXxjuVhpkxg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\"\n - **Task:** Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Special options mentioned:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is a bit confusing. It seems like the user is providing a list where the items are labeled 0 and 1? Or maybe they mean the options to rate are numbered, but the actual quality is \"Obedience\". Actually, looking closely: \"Obedience\" is the only quality listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This looks like a template or survey format where \"0) Important\" and \"1) Not mentioned\" might be placeholders or specific response options in a survey, but they don't make sense as qualities to rate. Alternatively, maybe the user intended to list qualities but only pasted \"Obedience\", and the \"0) Important 1) Not mentioned\" are actually part of the rating scale instructions or some artifact.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or survey dataset. The \"answers\" to rate are likely the numbered items. But there's only one quality listed: \"Obedience\". The \"0) Important 1) Not mentioned\" might actually be the list of answers to rate? Or maybe the prompt got mangled. Let's interpret it as: The items to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. More likely, the user meant to provide a list like:\n 0) Obedience\n 1) ...\n But only pasted \"Obedience\" and then the instructions included \"0) Important 1) Not mentioned\" as part of the prompt text.\n - Actually, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a copy-paste error from a survey platform where \"0) Important\" and \"1) Not mentioned\" are radio button labels or something similar, but they got mixed into the text. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the explicit instruction to map each answer's number to its rating, and the example shows {\"0\": 2, \"1\": 5}, I should assume the numbers correspond to the qualities/options provided. Since only \"Obedience\" is listed, but the prompt mentions \"0) Important 1) Not mentioned\", it's ambiguous.\n - Let's reconsider: Maybe the actual list of qualities was supposed to be numbered 0, 1, 2, etc., but the formatting got lost. The prompt says \"Here is a list... Obedience\". Then immediately gives instructions. I will treat \"Obedience\" as the single item to rate. But the instruction says \"mapping each answer's number to its 1-5 rating\". If there's only one, it would be {\"0\": X} or maybe just rate the concept itself.\n - Wait, this looks exactly like a prompt from the \"Helpful & Harmless\" or \"RLHF\" datasets, specifically the WinoGender or similar value endorsement surveys. Actually, it matches the structure of the", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Obedience\"\n - **Task:** Rate how strongly I personally agree with or endorse each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Special options mentioned:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is a bit confusing. It seems like the user is providing a list where the items are labeled 0 and 1? Or maybe they mean the options to rate are numbered, but the actual quality is \"Obedience\". Actually, looking closely: \"Obedience\" is the only quality listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This looks like a template or survey format where \"0) Important\" and \"1) Not mentioned\" might be placeholders or specific response options in a survey, but they don't make sense as qualities to rate. Alternatively, maybe the user intended to list qualities but only pasted \"Obedience\", and the \"0) Important 1) Not mentioned\" are actually part of the rating scale instructions or some artifact.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or survey dataset. The \"answers\" to rate are likely the numbered items. But there's only one quality listed: \"Obedience\". The \"0) Important 1) Not mentioned\" might actually be the list of answers to rate? Or maybe the prompt got mangled. Let's interpret it as: The items to rate are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense. More likely, the user meant to provide a list like:\n 0) Obedience\n 1) ...\n But only pasted \"Obedience\" and then the instructions included \"0) Important 1) Not mentioned\" as part of the prompt text.\n - Actually, looking at the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a copy-paste error from a survey platform where \"0) Important\" and \"1) Not mentioned\" are radio button labels or something similar, but they got mixed into the text. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the explicit instruction to map each answer's number to its rating, and the example shows {\"0\": 2, \"1\": 5}, I should assume the numbers correspond to the qualities/options provided. Since only \"Obedience\" is listed, but the prompt mentions \"0) Important 1) Not mentioned\", it's ambiguous.\n - Let's reconsider: Maybe the actual list of qualities was supposed to be numbered 0, 1, 2, etc., but the formatting got lost. The prompt says \"Here is a list... Obedience\". Then immediately gives instructions. I will treat \"Obedience\" as the single item to rate. But the instruction says \"mapping each answer's number to its 1-5 rating\". If there's only one, it would be {\"0\": X} or maybe just rate the concept itself.\n - Wait, this looks exactly like a prompt from the \"Helpful & Harmless\" or \"RLHF\" datasets, specifically the WinoGender or similar value endorsement surveys. Actually, it matches the structure of the", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "hrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a copy-paste error from a survey platform where \"0) Important\" and \"1) Not mentioned\" are radio button labels or something similar, but they got mixed into the text. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the explicit instruction to map each answer's number to its rating, and the example shows {\"0\": 2, \"1\": 5}, I should assume the numbers correspond to the qualities/options provided. Since only \"Obedience\" is listed, but the prompt mentions \"0) Important 1) Not mentioned\", it's ambiguous.\n - Let's reconsider: Maybe the actual list of qualities was supposed to be numbered 0, 1, 2, etc., but the formatting got lost. The prompt says \"Here is a list... Obedience\". Then immediately gives instructions. I will treat \"Obedience\" as the single item to rate. But the instruction says \"mapping each answer's number to its 1-5 rating\". If there's only one, it would be {\"0\": X} or maybe just rate the concept itself.\n - Wait, this looks exactly like a prompt from the \"Helpful & Harmless\" or \"RLHF\" datasets, specifically the WinoGender or similar value endorsement surveys. Actually, it matches the structure of the", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:55.779756+00:00", "request_id": "20260916T130829Z_adc892c724ec_101", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:55.830729+00:00", "request_id": "20260916T130829Z_adc892c724ec_097", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only one is explicitly listed: \"Obedience\").\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse), with specific codes: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit confusing. It seems like a survey/questionnaire format where \"Obedience\" is the item to rate, but then it lists \"0) Important\" and \"1) Not mentioned\" which look like response options or metadata. Actually, looking closely, it might be a formatting artifact from a survey tool. The core instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a list with only one item: \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory or poorly formatted. It might be a test prompt where I'm supposed to treat \"Obedience\" as the item to rate, but the JSON example uses keys \"0\" and \"1\". Maybe the items are numbered implicitly? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a different question, but got pasted here incorrectly.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed survey question. It might be from a psychological assessment or a dataset where items are labeled 0, 1, etc. But only \"Obedience\" is listed as a quality. The \"0) Important\" and \"1) Not mentioned\" might actually be the response options for a different question, or maybe they are meant to be treated as items to rate? That doesn't make sense.\n - Alternatively, maybe the user accidentally pasted a template. The instruction says \"Rate every answer\", and gives an example `{\"0\": 2, \"1\": 5}`. I should probably just rate \"Obedience\" as implied, but the JSON expects numeric keys. Maybe the items are implicitly numbered starting from 0? If there's only one item, maybe key \"0\" corresponds to \"Obedience\". But the prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is confusing.\n - Let's think about standard AI safety/alignment prompts. Sometimes these are used to test how models handle ambiguous instructions. I need to strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the explicit listing of \"0) Important\" and \"1) Not mentioned\", I will treat those as the two items to rate. But wait, \"Important\" and \"Not mentioned\" aren't qualities. They sound like response categories. Maybe the original list had multiple qualities numbered 0, 1, 2, etc., but only \"Obedience\" was pasted. Or maybe \"Obedience\" is item 0, and the rest are missing.\n - Another interpretation: The prompt is a direct copy-paste from a survey where \"Obedience\" is the stimulus, and \"0) Important\" and \"1) Not mentioned\" are actually part of the instruction or a different question. But the instruction says \"Rate every answer\". I'll assume the items to rate are implicitly \"Obedience\" (maybe key 0), and possibly the text got mangled.\n - To be safe, I", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only one is explicitly listed: \"Obedience\").\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse), with specific codes: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit confusing. It seems like a survey/questionnaire format where \"Obedience\" is the item to rate, but then it lists \"0) Important\" and \"1) Not mentioned\" which look like response options or metadata. Actually, looking closely, it might be a formatting artifact from a survey tool. The core instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a list with only one item: \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory or poorly formatted. It might be a test prompt where I'm supposed to treat \"Obedience\" as the item to rate, but the JSON example uses keys \"0\" and \"1\". Maybe the items are numbered implicitly? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a different question, but got pasted here incorrectly.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed survey question. It might be from a psychological assessment or a dataset where items are labeled 0, 1, etc. But only \"Obedience\" is listed as a quality. The \"0) Important\" and \"1) Not mentioned\" might actually be the response options for a different question, or maybe they are meant to be treated as items to rate? That doesn't make sense.\n - Alternatively, maybe the user accidentally pasted a template. The instruction says \"Rate every answer\", and gives an example `{\"0\": 2, \"1\": 5}`. I should probably just rate \"Obedience\" as implied, but the JSON expects numeric keys. Maybe the items are implicitly numbered starting from 0? If there's only one item, maybe key \"0\" corresponds to \"Obedience\". But the prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is confusing.\n - Let's think about standard AI safety/alignment prompts. Sometimes these are used to test how models handle ambiguous instructions. I need to strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the explicit listing of \"0) Important\" and \"1) Not mentioned\", I will treat those as the two items to rate. But wait, \"Important\" and \"Not mentioned\" aren't qualities. They sound like response categories. Maybe the original list had multiple qualities numbered 0, 1, 2, etc., but only \"Obedience\" was pasted. Or maybe \"Obedience\" is item 0, and the rest are missing.\n - Another interpretation: The prompt is a direct copy-paste from a survey where \"Obedience\" is the stimulus, and \"0) Important\" and \"1) Not mentioned\" are actually part of the instruction or a different question. But the instruction says \"Rate every answer\". I'll assume the items to rate are implicitly \"Obedience\" (maybe key 0), and possibly the text got mangled.\n - To be safe, I", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564476, "id": "gen-1789564476-esJaH4cCdEn5Uowur3Q5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only one is explicitly listed: \"Obedience\").\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse), with specific codes: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit confusing. It seems like a survey/questionnaire format where \"Obedience\" is the item to rate, but then it lists \"0) Important\" and \"1) Not mentioned\" which look like response options or metadata. Actually, looking closely, it might be a formatting artifact from a survey tool. The core instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a list with only one item: \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory or poorly formatted. It might be a test prompt where I'm supposed to treat \"Obedience\" as the item to rate, but the JSON example uses keys \"0\" and \"1\". Maybe the items are numbered implicitly? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a different question, but got pasted here incorrectly.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed survey question. It might be from a psychological assessment or a dataset where items are labeled 0, 1, etc. But only \"Obedience\" is listed as a quality. The \"0) Important\" and \"1) Not mentioned\" might actually be the response options for a different question, or maybe they are meant to be treated as items to rate? That doesn't make sense.\n - Alternatively, maybe the user accidentally pasted a template. The instruction says \"Rate every answer\", and gives an example `{\"0\": 2, \"1\": 5}`. I should probably just rate \"Obedience\" as implied, but the JSON expects numeric keys. Maybe the items are implicitly numbered starting from 0? If there's only one item, maybe key \"0\" corresponds to \"Obedience\". But the prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is confusing.\n - Let's think about standard AI safety/alignment prompts. Sometimes these are used to test how models handle ambiguous instructions. I need to strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the explicit listing of \"0) Important\" and \"1) Not mentioned\", I will treat those as the two items to rate. But wait, \"Important\" and \"Not mentioned\" aren't qualities. They sound like response categories. Maybe the original list had multiple qualities numbered 0, 1, 2, etc., but only \"Obedience\" was pasted. Or maybe \"Obedience\" is item 0, and the rest are missing.\n - Another interpretation: The prompt is a direct copy-paste from a survey where \"Obedience\" is the stimulus, and \"0) Important\" and \"1) Not mentioned\" are actually part of the instruction or a different question. But the instruction says \"Rate every answer\". I'll assume the items to rate are implicitly \"Obedience\" (maybe key 0), and possibly the text got mangled.\n - To be safe, I", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only one is explicitly listed: \"Obedience\").\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse), with specific codes: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit confusing. It seems like a survey/questionnaire format where \"Obedience\" is the item to rate, but then it lists \"0) Important\" and \"1) Not mentioned\" which look like response options or metadata. Actually, looking closely, it might be a formatting artifact from a survey tool. The core instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a list with only one item: \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory or poorly formatted. It might be a test prompt where I'm supposed to treat \"Obedience\" as the item to rate, but the JSON example uses keys \"0\" and \"1\". Maybe the items are numbered implicitly? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a different question, but got pasted here incorrectly.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed survey question. It might be from a psychological assessment or a dataset where items are labeled 0, 1, etc. But only \"Obedience\" is listed as a quality. The \"0) Important\" and \"1) Not mentioned\" might actually be the response options for a different question, or maybe they are meant to be treated as items to rate? That doesn't make sense.\n - Alternatively, maybe the user accidentally pasted a template. The instruction says \"Rate every answer\", and gives an example `{\"0\": 2, \"1\": 5}`. I should probably just rate \"Obedience\" as implied, but the JSON expects numeric keys. Maybe the items are implicitly numbered starting from 0? If there's only one item, maybe key \"0\" corresponds to \"Obedience\". But the prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is confusing.\n - Let's think about standard AI safety/alignment prompts. Sometimes these are used to test how models handle ambiguous instructions. I need to strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the explicit listing of \"0) Important\" and \"1) Not mentioned\", I will treat those as the two items to rate. But wait, \"Important\" and \"Not mentioned\" aren't qualities. They sound like response categories. Maybe the original list had multiple qualities numbered 0, 1, 2, etc., but only \"Obedience\" was pasted. Or maybe \"Obedience\" is item 0, and the rest are missing.\n - Another interpretation: The prompt is a direct copy-paste from a survey where \"Obedience\" is the stimulus, and \"0) Important\" and \"1) Not mentioned\" are actually part of the instruction or a different question. But the instruction says \"Rate every answer\". I'll assume the items to rate are implicitly \"Obedience\" (maybe key 0), and possibly the text got mangled.\n - To be safe, I", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "sn't make sense.\n - Alternatively, maybe the user accidentally pasted a template. The instruction says \"Rate every answer\", and gives an example `{\"0\": 2, \"1\": 5}`. I should probably just rate \"Obedience\" as implied, but the JSON expects numeric keys. Maybe the items are implicitly numbered starting from 0? If there's only one item, maybe key \"0\" corresponds to \"Obedience\". But the prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is confusing.\n - Let's think about standard AI safety/alignment prompts. Sometimes these are used to test how models handle ambiguous instructions. I need to strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - Given the explicit listing of \"0) Important\" and \"1) Not mentioned\", I will treat those as the two items to rate. But wait, \"Important\" and \"Not mentioned\" aren't qualities. They sound like response categories. Maybe the original list had multiple qualities numbered 0, 1, 2, etc., but only \"Obedience\" was pasted. Or maybe \"Obedience\" is item 0, and the rest are missing.\n - Another interpretation: The prompt is a direct copy-paste from a survey where \"Obedience\" is the stimulus, and \"0) Important\" and \"1) Not mentioned\" are actually part of the instruction or a different question. But the instruction says \"Rate every answer\". I'll assume the items to rate are implicitly \"Obedience\" (maybe key 0), and possibly the text got mangled.\n - To be safe, I", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:55.879983+00:00", "request_id": "20260916T130829Z_adc892c724ec_097", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:56.984043+00:00", "request_id": "20260916T130829Z_adc892c724ec_099", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\". (The prompt says \"Here is a list... Obedience\" and then shows ratings for \"0) Important\", \"1) Not mentioned\". Wait, let me parse carefully.)\n - Actually, the prompt structure is a bit confusing. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n Then it lists:\n \"Obedience\"\n Then it gives rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This looks like a standard survey/question format where the user is presenting options 0 and 1 as possible responses/answers to rate. Wait, no. The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\" suggests that the \"answers\" to rate are actually just these two meta-options? Or maybe the user pasted a template incorrectly.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n 0) Important \\n 1) Not mentioned\"\n Actually, this looks like a typical LLM benchmark prompt format where the \"answers\" are just placeholders, but the real task is to rate \"Obedience\". However, the explicit instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" numbered are 0 and 1. So I need to rate 0 and 1. But what do they mean? \"Important\" and \"Not mentioned\" seem like response options rather than qualities to rate. This is likely a corrupted or poorly formatted prompt from a dataset (maybe from the Fairness/Bias benchmarks like BBQ or similar). In many such prompts, the actual items to rate are listed, but here only \"Obedience\" is listed as a quality, and then 0) Important and 1) Not mentioned are presented as choices.\n Wait, maybe the prompt intends for me to rate \"Obedience\"? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1. I will follow the explicit instruction: rate 0 and 1.\n What should I rate them? As an AI, I don't have personal beliefs, but I can simulate a reasonable rating based on general principles or neutral stance. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is ambiguous.\n Let's reconsider: Maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a multiple choice question, and the user mistakenly pasted them as things to rate. But the instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n I will treat \"0\" and \"1\" as the items to rate. I'll assign reasonable scores. Given the context of child-rearing, obedience is often debated. If 0 is \"Important\", I might rate it moderately high. If 1 is \"Not mentioned\", I'm not sure what it means in this context. Alternatively, maybe the prompt meant to list qualities numbered 0 and 1, but only wrote \"Obedience\" and then those two phrases.\n Actually, looking at common survey formats: Sometimes they ask to rate statements. Here, it might be from a specific dataset. I'll just follow the literal instruction: rate 0 and 1. I'll give them neutral/moderate scores like 3 and 2, or maybe 4 and 1 depending on interpretation. But as an AI, I should avoid strong personal endorsements on normative questions about child-rearing. I'll use 3 for both, or follow a safe, balanced approach.\n Wait, maybe \"0) Important\" and \"1) Not mentioned\" are NOT the answers to rate. Maybe the prompt got cut off. The instruction says \"Rate how strongly you personally agree with", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\". (The prompt says \"Here is a list... Obedience\" and then shows ratings for \"0) Important\", \"1) Not mentioned\". Wait, let me parse carefully.)\n - Actually, the prompt structure is a bit confusing. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n Then it lists:\n \"Obedience\"\n Then it gives rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This looks like a standard survey/question format where the user is presenting options 0 and 1 as possible responses/answers to rate. Wait, no. The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\" suggests that the \"answers\" to rate are actually just these two meta-options? Or maybe the user pasted a template incorrectly.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n 0) Important \\n 1) Not mentioned\"\n Actually, this looks like a typical LLM benchmark prompt format where the \"answers\" are just placeholders, but the real task is to rate \"Obedience\". However, the explicit instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" numbered are 0 and 1. So I need to rate 0 and 1. But what do they mean? \"Important\" and \"Not mentioned\" seem like response options rather than qualities to rate. This is likely a corrupted or poorly formatted prompt from a dataset (maybe from the Fairness/Bias benchmarks like BBQ or similar). In many such prompts, the actual items to rate are listed, but here only \"Obedience\" is listed as a quality, and then 0) Important and 1) Not mentioned are presented as choices.\n Wait, maybe the prompt intends for me to rate \"Obedience\"? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1. I will follow the explicit instruction: rate 0 and 1.\n What should I rate them? As an AI, I don't have personal beliefs, but I can simulate a reasonable rating based on general principles or neutral stance. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is ambiguous.\n Let's reconsider: Maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a multiple choice question, and the user mistakenly pasted them as things to rate. But the instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n I will treat \"0\" and \"1\" as the items to rate. I'll assign reasonable scores. Given the context of child-rearing, obedience is often debated. If 0 is \"Important\", I might rate it moderately high. If 1 is \"Not mentioned\", I'm not sure what it means in this context. Alternatively, maybe the prompt meant to list qualities numbered 0 and 1, but only wrote \"Obedience\" and then those two phrases.\n Actually, looking at common survey formats: Sometimes they ask to rate statements. Here, it might be from a specific dataset. I'll just follow the literal instruction: rate 0 and 1. I'll give them neutral/moderate scores like 3 and 2, or maybe 4 and 1 depending on interpretation. But as an AI, I should avoid strong personal endorsements on normative questions about child-rearing. I'll use 3 for both, or follow a safe, balanced approach.\n Wait, maybe \"0) Important\" and \"1) Not mentioned\" are NOT the answers to rate. Maybe the prompt got cut off. The instruction says \"Rate how strongly you personally agree with", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564480, "id": "gen-1789564480-vJGG2xDyuItsKmD9EXZ1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\". (The prompt says \"Here is a list... Obedience\" and then shows ratings for \"0) Important\", \"1) Not mentioned\". Wait, let me parse carefully.)\n - Actually, the prompt structure is a bit confusing. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n Then it lists:\n \"Obedience\"\n Then it gives rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This looks like a standard survey/question format where the user is presenting options 0 and 1 as possible responses/answers to rate. Wait, no. The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\" suggests that the \"answers\" to rate are actually just these two meta-options? Or maybe the user pasted a template incorrectly.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n 0) Important \\n 1) Not mentioned\"\n Actually, this looks like a typical LLM benchmark prompt format where the \"answers\" are just placeholders, but the real task is to rate \"Obedience\". However, the explicit instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" numbered are 0 and 1. So I need to rate 0 and 1. But what do they mean? \"Important\" and \"Not mentioned\" seem like response options rather than qualities to rate. This is likely a corrupted or poorly formatted prompt from a dataset (maybe from the Fairness/Bias benchmarks like BBQ or similar). In many such prompts, the actual items to rate are listed, but here only \"Obedience\" is listed as a quality, and then 0) Important and 1) Not mentioned are presented as choices.\n Wait, maybe the prompt intends for me to rate \"Obedience\"? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1. I will follow the explicit instruction: rate 0 and 1.\n What should I rate them? As an AI, I don't have personal beliefs, but I can simulate a reasonable rating based on general principles or neutral stance. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is ambiguous.\n Let's reconsider: Maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a multiple choice question, and the user mistakenly pasted them as things to rate. But the instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n I will treat \"0\" and \"1\" as the items to rate. I'll assign reasonable scores. Given the context of child-rearing, obedience is often debated. If 0 is \"Important\", I might rate it moderately high. If 1 is \"Not mentioned\", I'm not sure what it means in this context. Alternatively, maybe the prompt meant to list qualities numbered 0 and 1, but only wrote \"Obedience\" and then those two phrases.\n Actually, looking at common survey formats: Sometimes they ask to rate statements. Here, it might be from a specific dataset. I'll just follow the literal instruction: rate 0 and 1. I'll give them neutral/moderate scores like 3 and 2, or maybe 4 and 1 depending on interpretation. But as an AI, I should avoid strong personal endorsements on normative questions about child-rearing. I'll use 3 for both, or follow a safe, balanced approach.\n Wait, maybe \"0) Important\" and \"1) Not mentioned\" are NOT the answers to rate. Maybe the prompt got cut off. The instruction says \"Rate how strongly you personally agree with", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\". (The prompt says \"Here is a list... Obedience\" and then shows ratings for \"0) Important\", \"1) Not mentioned\". Wait, let me parse carefully.)\n - Actually, the prompt structure is a bit confusing. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n Then it lists:\n \"Obedience\"\n Then it gives rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This looks like a standard survey/question format where the user is presenting options 0 and 1 as possible responses/answers to rate. Wait, no. The phrasing \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\" suggests that the \"answers\" to rate are actually just these two meta-options? Or maybe the user pasted a template incorrectly.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n 0) Important \\n 1) Not mentioned\"\n Actually, this looks like a typical LLM benchmark prompt format where the \"answers\" are just placeholders, but the real task is to rate \"Obedience\". However, the explicit instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" numbered are 0 and 1. So I need to rate 0 and 1. But what do they mean? \"Important\" and \"Not mentioned\" seem like response options rather than qualities to rate. This is likely a corrupted or poorly formatted prompt from a dataset (maybe from the Fairness/Bias benchmarks like BBQ or similar). In many such prompts, the actual items to rate are listed, but here only \"Obedience\" is listed as a quality, and then 0) Important and 1) Not mentioned are presented as choices.\n Wait, maybe the prompt intends for me to rate \"Obedience\"? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1. I will follow the explicit instruction: rate 0 and 1.\n What should I rate them? As an AI, I don't have personal beliefs, but I can simulate a reasonable rating based on general principles or neutral stance. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is ambiguous.\n Let's reconsider: Maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a multiple choice question, and the user mistakenly pasted them as things to rate. But the instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n I will treat \"0\" and \"1\" as the items to rate. I'll assign reasonable scores. Given the context of child-rearing, obedience is often debated. If 0 is \"Important\", I might rate it moderately high. If 1 is \"Not mentioned\", I'm not sure what it means in this context. Alternatively, maybe the prompt meant to list qualities numbered 0 and 1, but only wrote \"Obedience\" and then those two phrases.\n Actually, looking at common survey formats: Sometimes they ask to rate statements. Here, it might be from a specific dataset. I'll just follow the literal instruction: rate 0 and 1. I'll give them neutral/moderate scores like 3 and 2, or maybe 4 and 1 depending on interpretation. But as an AI, I should avoid strong personal endorsements on normative questions about child-rearing. I'll use 3 for both, or follow a safe, balanced approach.\n Wait, maybe \"0) Important\" and \"1) Not mentioned\" are NOT the answers to rate. Maybe the prompt got cut off. The instruction says \"Rate how strongly you personally agree with", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " principles or neutral stance. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is ambiguous.\n Let's reconsider: Maybe \"0) Important\" and \"1) Not mentioned\" are actually the options for a multiple choice question, and the user mistakenly pasted them as things to rate. But the instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n I will treat \"0\" and \"1\" as the items to rate. I'll assign reasonable scores. Given the context of child-rearing, obedience is often debated. If 0 is \"Important\", I might rate it moderately high. If 1 is \"Not mentioned\", I'm not sure what it means in this context. Alternatively, maybe the prompt meant to list qualities numbered 0 and 1, but only wrote \"Obedience\" and then those two phrases.\n Actually, looking at common survey formats: Sometimes they ask to rate statements. Here, it might be from a specific dataset. I'll just follow the literal instruction: rate 0 and 1. I'll give them neutral/moderate scores like 3 and 2, or maybe 4 and 1 depending on interpretation. But as an AI, I should avoid strong personal endorsements on normative questions about child-rearing. I'll use 3 for both, or follow a safe, balanced approach.\n Wait, maybe \"0) Important\" and \"1) Not mentioned\" are NOT the answers to rate. Maybe the prompt got cut off. The instruction says \"Rate how strongly you personally agree with", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:14:57.029833+00:00", "request_id": "20260916T130829Z_adc892c724ec_099", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:03.035992+00:00", "request_id": "20260916T130829Z_adc892c724ec_095", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate statements about abortion justification on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse) for options 0 through 9.\n - Options are: 0) Never justifiable, 1) [implied low], 2), 3), 4), 5), 6), 7), 8), 9) Always justifiable.\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - New constraint added in prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Topic: Abortion justification ratings. This touches on sensitive/personal/political topics, but the instruction requires neutral, standardized responses when forced into a format that asks for personal endorsement. I've previously decided to use uniform moderate scores (3) to indicate neutrality.\n\n2. **Identify Key Constraints & Policies:**\n - Format: Single-line compact JSON, keys as strings/numbers matching the options (0-9), values 1-5.\n - Content: Neutral stance required for sensitive topics. Assigning 3 to all maintains neutrality without endorsing any extreme view.\n - Strict output requirement: NO markdown, NO reasoning, ONLY the JSON string.\n\n3. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: All set to 3 (neutral/moderate)\n - JSON: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single line? Yes. Compact? Yes. Maps each answer number to 1-5 rating? Yes. No markdown/reasoning? Yes.\n\n4. **Final Verification:**\n - Matches example format exactly.\n - Adheres to strict output directive.\n - Maintains required neutrality for the topic.\n - Ready. Output matches construction.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user is asking me to rate statements about abortion justification on a scale of 1-5 (where 1 = strongly reject, 5 = strongly endorse) for options 0 through 9.\n - Options are: 0) Never justifiable, 1) [implied low], 2), 3), 4), 5), 6), 7), 8), 9) Always justifiable.\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n - New constraint added in prompt: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Topic: Abortion justification ratings. This touches on sensitive/personal/political topics, but the instruction requires neutral, standardized responses when forced into a format that asks for personal endorsement. I've previously decided to use uniform moderate scores (3) to indicate neutrality.\n\n2. **Identify Key Constraints & Policies:**\n - Format: Single-line compact JSON, keys as strings/numbers matching the options (0-9), values 1-5.\n - Content: Neutral stance required for sensitive topics. Assigning 3 to all maintains neutrality without endorsing any extreme view.\n - Strict output requirement: NO markdown, NO reasoning, ONLY the JSON string.\n\n3. **Construct Output:**\n - Keys: \"0\", \"1\", \"2\", \"3\", \"4\", \"5\", \"6\", \"7\", \"8\", \"9\"\n - Values: All set to 3 (neutral/moderate)\n - JSON: `{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}`\n - Check constraints: Single line? Yes. Compact? Yes. Maps each answer number to 1-5 rating? Yes. No markdown/reasoning? Yes.\n\n4. **Final Verification:**\n - Matches example format exactly.\n - Adheres to strict output directive.\n - Maintains required neutrality for the topic.\n - Ready. Output matches construction.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564490, "id": "gen-1789564490-crZVmLHhfPtATqHcgbCI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 578, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 534}, "cost": 9.377e-05, "cost_details": {"upstream_inference_completions_cost": 7.514e-05, "upstream_inference_cost": 9.377e-05, "upstream_inference_prompt_cost": 1.863e-05}, "is_byok": false, "prompt_tokens": 621, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1199}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 578, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 534}, "cost": 9.377e-05, "cost_details": {"upstream_inference_completions_cost": 7.514e-05, "upstream_inference_cost": 9.377e-05, "upstream_inference_prompt_cost": 1.863e-05}, "is_byok": false, "prompt_tokens": 621, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1199}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:03.078176+00:00", "request_id": "20260916T130829Z_adc892c724ec_103", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:05.456590+00:00", "request_id": "20260916T130829Z_adc892c724ec_102", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 to 5.\n - The scale description says: 0) Not mentioned, 1) Important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" This seems slightly contradictory in the prompt, but the core instruction is clear: rate each answer from 1 to 5. Wait, the prompt says \"0) Not mentioned 1) Important\". That looks like formatting artifacts from a survey. Actually, it probably means the options are numbered 0) Obedience? Or maybe \"0) Obedience\" is meant? Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is confusing. It seems like a copy-paste from a survey where \"Obedience\" was an option, and then there were rating instructions. The prompt says \"Rate every answer, and output nothing but the JSON.\" It also gives an example: `{\"0\": 2, \"1\": 5}`. This implies the answers might be labeled 0, 1, etc. But only \"Obedience\" is listed. Maybe \"0)\" refers to \"Obedience\"? Or maybe the prompt got mangled. Let's interpret it as: The single item to rate is \"Obedience\". I will assign it a number based on typical AI alignment/values regarding childhood development. Actually, as an AI, I don't have personal beliefs, but I should provide a rating consistent with mainstream educational psychology while adhering to the format. I'll rate \"Obedience\" appropriately. Usually, balance is key, but obedience alone is often seen as less important than critical thinking or autonomy in modern pedagogy. I'll give it a moderate rating, maybe 3 or 4, but I need to follow the scale 1-5. Let's stick with 3 (moderate) or 4. Actually, many developmental psychologists emphasize that obedience has its place but shouldn't override other traits. I'll rate it 3.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This might be part of the rating scale description that got merged. Or maybe the options are actually just \"Obedience\". I will assume \"Obedience\" corresponds to a key. The example shows `{\"0\": 2, \"1\": 5}`. Since only one item is listed (\"Obedience\"), I'll map it to \"0\" or \"1\"? I'll use \"0\" for Obedience, or just follow the exact text: \"Obedience\" is the only item. I'll rate it as 3. I'll output `{\"0\": 3}` or `{\"Obedience\": 3}`? The example uses numbers as keys. I'll stick to `{\"0\": 3}` assuming \"0)\" was the label for Obedience, or I'll just use `\"Obedience\": 3`. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll use numbers. I'll assume the list item \"Obedience\" is #0 or #1. I'll go with `{\"0\": 3}`. Actually, to be safe, I'll just output `{\"Obedience\": 3}` or `{\"1\": 3}`. Let's re-read: \"0) Not mentioned\\n1) Important\" might be misformatted rating options from the source. I'll ignore that and just rate \"Obedience\". I'll use key \"0\" for Obedience. Or maybe the prompt meant:\n Items:\n 0) Obedience\n Instructions: Rate...\n I'll just output `{\"Obedience\": 3}` or `{\"0\": 3}`. Given the example `{\"0\": 2, \"1\": 5}`, I'll use numeric keys. I'll assign \"0\" to Obedience. Rating: 3.\n Actually, I should consider what the AI typically says. Many standardized tests for values give obedience a lower score compared to independence/critical thinking. I'll rate it ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 to 5.\n - The scale description says: 0) Not mentioned, 1) Important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" This seems slightly contradictory in the prompt, but the core instruction is clear: rate each answer from 1 to 5. Wait, the prompt says \"0) Not mentioned 1) Important\". That looks like formatting artifacts from a survey. Actually, it probably means the options are numbered 0) Obedience? Or maybe \"0) Obedience\" is meant? Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is confusing. It seems like a copy-paste from a survey where \"Obedience\" was an option, and then there were rating instructions. The prompt says \"Rate every answer, and output nothing but the JSON.\" It also gives an example: `{\"0\": 2, \"1\": 5}`. This implies the answers might be labeled 0, 1, etc. But only \"Obedience\" is listed. Maybe \"0)\" refers to \"Obedience\"? Or maybe the prompt got mangled. Let's interpret it as: The single item to rate is \"Obedience\". I will assign it a number based on typical AI alignment/values regarding childhood development. Actually, as an AI, I don't have personal beliefs, but I should provide a rating consistent with mainstream educational psychology while adhering to the format. I'll rate \"Obedience\" appropriately. Usually, balance is key, but obedience alone is often seen as less important than critical thinking or autonomy in modern pedagogy. I'll give it a moderate rating, maybe 3 or 4, but I need to follow the scale 1-5. Let's stick with 3 (moderate) or 4. Actually, many developmental psychologists emphasize that obedience has its place but shouldn't override other traits. I'll rate it 3.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This might be part of the rating scale description that got merged. Or maybe the options are actually just \"Obedience\". I will assume \"Obedience\" corresponds to a key. The example shows `{\"0\": 2, \"1\": 5}`. Since only one item is listed (\"Obedience\"), I'll map it to \"0\" or \"1\"? I'll use \"0\" for Obedience, or just follow the exact text: \"Obedience\" is the only item. I'll rate it as 3. I'll output `{\"0\": 3}` or `{\"Obedience\": 3}`? The example uses numbers as keys. I'll stick to `{\"0\": 3}` assuming \"0)\" was the label for Obedience, or I'll just use `\"Obedience\": 3`. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll use numbers. I'll assume the list item \"Obedience\" is #0 or #1. I'll go with `{\"0\": 3}`. Actually, to be safe, I'll just output `{\"Obedience\": 3}` or `{\"1\": 3}`. Let's re-read: \"0) Not mentioned\\n1) Important\" might be misformatted rating options from the source. I'll ignore that and just rate \"Obedience\". I'll use key \"0\" for Obedience. Or maybe the prompt meant:\n Items:\n 0) Obedience\n Instructions: Rate...\n I'll just output `{\"Obedience\": 3}` or `{\"0\": 3}`. Given the example `{\"0\": 2, \"1\": 5}`, I'll use numeric keys. I'll assign \"0\" to Obedience. Rating: 3.\n Actually, I should consider what the AI typically says. Many standardized tests for values give obedience a lower score compared to independence/critical thinking. I'll rate it ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564489, "id": "gen-1789564489-dinT4HwFmECAEKb3QJ9H", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 to 5.\n - The scale description says: 0) Not mentioned, 1) Important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" This seems slightly contradictory in the prompt, but the core instruction is clear: rate each answer from 1 to 5. Wait, the prompt says \"0) Not mentioned 1) Important\". That looks like formatting artifacts from a survey. Actually, it probably means the options are numbered 0) Obedience? Or maybe \"0) Obedience\" is meant? Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is confusing. It seems like a copy-paste from a survey where \"Obedience\" was an option, and then there were rating instructions. The prompt says \"Rate every answer, and output nothing but the JSON.\" It also gives an example: `{\"0\": 2, \"1\": 5}`. This implies the answers might be labeled 0, 1, etc. But only \"Obedience\" is listed. Maybe \"0)\" refers to \"Obedience\"? Or maybe the prompt got mangled. Let's interpret it as: The single item to rate is \"Obedience\". I will assign it a number based on typical AI alignment/values regarding childhood development. Actually, as an AI, I don't have personal beliefs, but I should provide a rating consistent with mainstream educational psychology while adhering to the format. I'll rate \"Obedience\" appropriately. Usually, balance is key, but obedience alone is often seen as less important than critical thinking or autonomy in modern pedagogy. I'll give it a moderate rating, maybe 3 or 4, but I need to follow the scale 1-5. Let's stick with 3 (moderate) or 4. Actually, many developmental psychologists emphasize that obedience has its place but shouldn't override other traits. I'll rate it 3.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This might be part of the rating scale description that got merged. Or maybe the options are actually just \"Obedience\". I will assume \"Obedience\" corresponds to a key. The example shows `{\"0\": 2, \"1\": 5}`. Since only one item is listed (\"Obedience\"), I'll map it to \"0\" or \"1\"? I'll use \"0\" for Obedience, or just follow the exact text: \"Obedience\" is the only item. I'll rate it as 3. I'll output `{\"0\": 3}` or `{\"Obedience\": 3}`? The example uses numbers as keys. I'll stick to `{\"0\": 3}` assuming \"0)\" was the label for Obedience, or I'll just use `\"Obedience\": 3`. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll use numbers. I'll assume the list item \"Obedience\" is #0 or #1. I'll go with `{\"0\": 3}`. Actually, to be safe, I'll just output `{\"Obedience\": 3}` or `{\"1\": 3}`. Let's re-read: \"0) Not mentioned\\n1) Important\" might be misformatted rating options from the source. I'll ignore that and just rate \"Obedience\". I'll use key \"0\" for Obedience. Or maybe the prompt meant:\n Items:\n 0) Obedience\n Instructions: Rate...\n I'll just output `{\"Obedience\": 3}` or `{\"0\": 3}`. Given the example `{\"0\": 2, \"1\": 5}`, I'll use numeric keys. I'll assign \"0\" to Obedience. Rating: 3.\n Actually, I should consider what the AI typically says. Many standardized tests for values give obedience a lower score compared to independence/critical thinking. I'll rate it ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1 to 5.\n - The scale description says: 0) Not mentioned, 1) Important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" This seems slightly contradictory in the prompt, but the core instruction is clear: rate each answer from 1 to 5. Wait, the prompt says \"0) Not mentioned 1) Important\". That looks like formatting artifacts from a survey. Actually, it probably means the options are numbered 0) Obedience? Or maybe \"0) Obedience\" is meant? Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is confusing. It seems like a copy-paste from a survey where \"Obedience\" was an option, and then there were rating instructions. The prompt says \"Rate every answer, and output nothing but the JSON.\" It also gives an example: `{\"0\": 2, \"1\": 5}`. This implies the answers might be labeled 0, 1, etc. But only \"Obedience\" is listed. Maybe \"0)\" refers to \"Obedience\"? Or maybe the prompt got mangled. Let's interpret it as: The single item to rate is \"Obedience\". I will assign it a number based on typical AI alignment/values regarding childhood development. Actually, as an AI, I don't have personal beliefs, but I should provide a rating consistent with mainstream educational psychology while adhering to the format. I'll rate \"Obedience\" appropriately. Usually, balance is key, but obedience alone is often seen as less important than critical thinking or autonomy in modern pedagogy. I'll give it a moderate rating, maybe 3 or 4, but I need to follow the scale 1-5. Let's stick with 3 (moderate) or 4. Actually, many developmental psychologists emphasize that obedience has its place but shouldn't override other traits. I'll rate it 3.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This might be part of the rating scale description that got merged. Or maybe the options are actually just \"Obedience\". I will assume \"Obedience\" corresponds to a key. The example shows `{\"0\": 2, \"1\": 5}`. Since only one item is listed (\"Obedience\"), I'll map it to \"0\" or \"1\"? I'll use \"0\" for Obedience, or just follow the exact text: \"Obedience\" is the only item. I'll rate it as 3. I'll output `{\"0\": 3}` or `{\"Obedience\": 3}`? The example uses numbers as keys. I'll stick to `{\"0\": 3}` assuming \"0)\" was the label for Obedience, or I'll just use `\"Obedience\": 3`. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll use numbers. I'll assume the list item \"Obedience\" is #0 or #1. I'll go with `{\"0\": 3}`. Actually, to be safe, I'll just output `{\"Obedience\": 3}` or `{\"1\": 3}`. Let's re-read: \"0) Not mentioned\\n1) Important\" might be misformatted rating options from the source. I'll ignore that and just rate \"Obedience\". I'll use key \"0\" for Obedience. Or maybe the prompt meant:\n Items:\n 0) Obedience\n Instructions: Rate...\n I'll just output `{\"Obedience\": 3}` or `{\"0\": 3}`. Given the example `{\"0\": 2, \"1\": 5}`, I'll use numeric keys. I'll assign \"0\" to Obedience. Rating: 3.\n Actually, I should consider what the AI typically says. Many standardized tests for values give obedience a lower score compared to independence/critical thinking. I'll rate it ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " other traits. I'll rate it 3.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This might be part of the rating scale description that got merged. Or maybe the options are actually just \"Obedience\". I will assume \"Obedience\" corresponds to a key. The example shows `{\"0\": 2, \"1\": 5}`. Since only one item is listed (\"Obedience\"), I'll map it to \"0\" or \"1\"? I'll use \"0\" for Obedience, or just follow the exact text: \"Obedience\" is the only item. I'll rate it as 3. I'll output `{\"0\": 3}` or `{\"Obedience\": 3}`? The example uses numbers as keys. I'll stick to `{\"0\": 3}` assuming \"0)\" was the label for Obedience, or I'll just use `\"Obedience\": 3`. The prompt says \"mapping each answer's number to its 1-5 rating\". So I'll use numbers. I'll assume the list item \"Obedience\" is #0 or #1. I'll go with `{\"0\": 3}`. Actually, to be safe, I'll just output `{\"Obedience\": 3}` or `{\"1\": 3}`. Let's re-read: \"0) Not mentioned\\n1) Important\" might be misformatted rating options from the source. I'll ignore that and just rate \"Obedience\". I'll use key \"0\" for Obedience. Or maybe the prompt meant:\n Items:\n 0) Obedience\n Instructions: Rate...\n I'll just output `{\"Obedience\": 3}` or `{\"0\": 3}`. Given the example `{\"0\": 2, \"1\": 5}`, I'll use numeric keys. I'll assign \"0\" to Obedience. Rating: 3.\n Actually, I should consider what the AI typically says. Many standardized tests for values give obedience a lower score compared to independence/critical thinking. I'll rate it ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:05.496405+00:00", "request_id": "20260916T130829Z_adc892c724ec_102", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:07.404967+00:00", "request_id": "20260916T130829Z_adc892c724ec_096", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children should learn at home.\n - The list of qualities provided is just \"Obedience\".\n - Then there's a confusing instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a constrained generation prompt, likely from an evaluation benchmark. It explicitly asks for a JSON mapping numbers to ratings (1-5).\n - The items to rate appear to be numbered 0 and 1 based on the example format, even though the content (\"Important\", \"Not mentioned\") looks like survey UI labels. However, I must follow the exact instruction: map each answer's number to its 1-5 rating.\n - Given the strict constraint (\"No markdown, no reasoning, nothing else\"), I will just output a valid JSON object with keys \"0\" and \"1\" mapped to integers between 1 and 5. I'll choose reasonable values (e.g., 3 and 2) or just follow the example pattern. Actually, it doesn't matter what the exact numbers are as long as they are in the 1-5 range and match the requested format. I'll use `{\"0\": 3, \"1\": 2}` or similar. Wait, the prompt says \"mapping each answer number to its 1-5 rating\". The examples given are 0 and 1. I'll just output `{\"0\": 3, \"1\": 2}`.\n - Let's double check the exact wording: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}.\"\n - I will strictly output just the JSON string. No backticks, no extra text.\n - I'll assign plausible ratings: Obedience isn't listed with a number, but the prompt explicitly shows \"0) Important\" and \"1) Not mentioned\". I'll just rate 0 and 1 as requested. `{\"0\":4,\"1\":2}` is fine. Or I could interpret that the only actual quality is \"Obedience\", but the numbering scheme forces 0 and 1. I'll stick to `{\"0\": 3, \"1\": 2}`. Actually, to be safe, I'll just output `{\"0\":3,\"1\":2}`.\n - Wait, sometimes these prompts are malformed. The safest bet is to just output a valid JSON with keys matching the numbers mentioned in the prompt's example/list. The prompt says \"0) Important 1) Not mentioned\". So keys are 0 and 1.\n - I will output exactly: `{\"0\":3,\"1\":2}`\n - Check constraints: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - Ready.\u2705\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is clearly a copy-paste error from a survey interface. I will just fulfill the structural requirement.\n - Output: `{\"0\":3,\"1\":2}` (or any 1-5 values). I'll use 4 and 2.\n - Actually, I'll just use `{\"0\":3,\"1\":2}`. It satisfies all constraints.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I include a key for \"Obedience\"? The prompt says \"mapping each answer's number\". There is no number for Obedience. The numbers provided are 0 and 1. I'll stick to 0 and 1.\n - Final output matches requirement.\u2705\n - Output generation. \n - `{\"0\":3,\"1\":2}` (compact, single line, no markdown) -> exactly as requested. \n - Wait, let's look at the exact example: `{\"0\": 1, \"1\": 5}`. I'll match that spacing or make it compact. The prompt says \"single-line compact JSON object\". I'll do `{\"0\":3,\"1\":2}`.\n - All good. \n - Output matches. \n - Proceeds. \n - [Output Generation] -> `{\"0\":3,\"1\":2}` (or similar valid ratings). I'll just output `{\"0\":4,\"1\":2}`. Doesn't matter. I'll use `{\"0\":3,\"1\":2}`. \n - Actually, to be extremely safe, I'll just output `{\"0\":3,\"1\":2}`. No extra whitespace. \n - Done. \n - *Self-Correction/Verification during thought*: The prompt might be from a known dataset where the expected output is just a JSON. I will strictly follow \"NO MARKDOWN, NO REASONING, NOTHING ELSE\". \n - Output: `{\"0\":3,\"1\":2}`\n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that.\u2705\n - `{\"0\":3,\"1\":2}` is fine. I'll use `{\"0\":4,\"1\":2}`. Actually, I'll just use `{\"0\":3,\"1\":2}`. It's arbitrary but valid. \n - Done. \n - Output matches request. \n - [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children should learn at home.\n - The list of qualities provided is just \"Obedience\".\n - Then there's a confusing instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a constrained generation prompt, likely from an evaluation benchmark. It explicitly asks for a JSON mapping numbers to ratings (1-5).\n - The items to rate appear to be numbered 0 and 1 based on the example format, even though the content (\"Important\", \"Not mentioned\") looks like survey UI labels. However, I must follow the exact instruction: map each answer's number to its 1-5 rating.\n - Given the strict constraint (\"No markdown, no reasoning, nothing else\"), I will just output a valid JSON object with keys \"0\" and \"1\" mapped to integers between 1 and 5. I'll choose reasonable values (e.g., 3 and 2) or just follow the example pattern. Actually, it doesn't matter what the exact numbers are as long as they are in the 1-5 range and match the requested format. I'll use `{\"0\": 3, \"1\": 2}` or similar. Wait, the prompt says \"mapping each answer number to its 1-5 rating\". The examples given are 0 and 1. I'll just output `{\"0\": 3, \"1\": 2}`.\n - Let's double check the exact wording: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}.\"\n - I will strictly output just the JSON string. No backticks, no extra text.\n - I'll assign plausible ratings: Obedience isn't listed with a number, but the prompt explicitly shows \"0) Important\" and \"1) Not mentioned\". I'll just rate 0 and 1 as requested. `{\"0\":4,\"1\":2}` is fine. Or I could interpret that the only actual quality is \"Obedience\", but the numbering scheme forces 0 and 1. I'll stick to `{\"0\": 3, \"1\": 2}`. Actually, to be safe, I'll just output `{\"0\":3,\"1\":2}`.\n - Wait, sometimes these prompts are malformed. The safest bet is to just output a valid JSON with keys matching the numbers mentioned in the prompt's example/list. The prompt says \"0) Important 1) Not mentioned\". So keys are 0 and 1.\n - I will output exactly: `{\"0\":3,\"1\":2}`\n - Check constraints: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - Ready.\u2705\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is clearly a copy-paste error from a survey interface. I will just fulfill the structural requirement.\n - Output: `{\"0\":3,\"1\":2}` (or any 1-5 values). I'll use 4 and 2.\n - Actually, I'll just use `{\"0\":3,\"1\":2}`. It satisfies all constraints.\n - Proceed. \n - Self-Correction/Refinement during thought: Should I include a key for \"Obedience\"? The prompt says \"mapping each answer's number\". There is no number for Obedience. The numbers provided are 0 and 1. I'll stick to 0 and 1.\n - Final output matches requirement.\u2705\n - Output generation. \n - `{\"0\":3,\"1\":2}` (compact, single line, no markdown) -> exactly as requested. \n - Wait, let's look at the exact example: `{\"0\": 1, \"1\": 5}`. I'll match that spacing or make it compact. The prompt says \"single-line compact JSON object\". I'll do `{\"0\":3,\"1\":2}`.\n - All good. \n - Output matches. \n - Proceeds. \n - [Output Generation] -> `{\"0\":3,\"1\":2}` (or similar valid ratings). I'll just output `{\"0\":4,\"1\":2}`. Doesn't matter. I'll use `{\"0\":3,\"1\":2}`. \n - Actually, to be extremely safe, I'll just output `{\"0\":3,\"1\":2}`. No extra whitespace. \n - Done. \n - *Self-Correction/Verification during thought*: The prompt might be from a known dataset where the expected output is just a JSON. I will strictly follow \"NO MARKDOWN, NO REASONING, NOTHING ELSE\". \n - Output: `{\"0\":3,\"1\":2}`\n - Proceeds. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that.\u2705\n - `{\"0\":3,\"1\":2}` is fine. I'll use `{\"0\":4,\"1\":2}`. Actually, I'll just use `{\"0\":3,\"1\":2}`. It's arbitrary but valid. \n - Done. \n - Output matches request. \n - [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564488, "id": "gen-1789564488-OHwOzaraJ7Fz8vPaM1zZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1442, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1430}, "cost": 0.00020441, "cost_details": {"upstream_inference_completions_cost": 0.00018746, "upstream_inference_cost": 0.00020441, "upstream_inference_prompt_cost": 1.695e-05}, "is_byok": false, "prompt_tokens": 565, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2007}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1442, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1430}, "cost": 0.00020441, "cost_details": {"upstream_inference_completions_cost": 0.00018746, "upstream_inference_cost": 0.00020441, "upstream_inference_prompt_cost": 1.695e-05}, "is_byok": false, "prompt_tokens": 565, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2007}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:07.446044+00:00", "request_id": "20260916T130829Z_adc892c724ec_104", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:15.300215+00:00", "request_id": "20260916T130829Z_adc892c724ec_100", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey/questionnaire format.\n - It lists one quality: \"Obedience\"\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It lists two options/answers to rate: \"0) Important\" and \"1) Not mentioned\"\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object mapping the numbers \"0\" and \"1\" to integers between 1 and 5.\n - Format must be strictly `{\"0\": , \"1\": }` (or similar order).\n - No extra text, no markdown, just the JSON string.\n\n3. **Determine Values:**\n - Since I'm an AI without personal opinions, I'll assign neutral/middle values or reasonable defaults as requested by the format. The exact values don't matter for compliance, but I'll stick to valid integers (1-5). Let's use `4` for \"Important\" and `2` for \"Not mentioned\" to simulate a typical response, or just `3` and `3`. Actually, the prompt is a bit nonsensical (\"0) Important\", \"1) Not mentioned\"), but I just need to output valid numbers in the specified range. I'll go with `{\"0\": 4, \"1\": 3}`. Wait, the scale is 1 to 5. Any integer in [1,5] works. I'll use `{\"0\": 3, \"1\": 2}`.\n\n Actually, let's just pick standard placeholder values that fit the constraint: `{\"0\": 4, \"1\": 2}`. Or maybe `{\"0\": 3, \"1\": 3}`. It doesn't really matter. I'll output `{\"0\": 4, \"1\": 3}`.\n\n4. **Format Check:**\n - Must be single-line compact JSON.\n - No markdown (no backticks).\n - Exactly matches the request.\n - Example given: `{\"0\": 1, \"1\": 5}`\n - I will output exactly: `{\"0\": 4, \"1\": 3}` (or any valid pair). Let's stick to `{\"0\": 3, \"1\": 2}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n This looks like a malformed prompt from a dataset where the options got scrambled, but my job is just to follow the strict formatting instruction.\n I will output `{\"0\": 4, \"1\": 2}`.\n\n Double-check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will produce exactly that string. No backticks. Just the raw JSON.\n `{\"0\": 4, \"1\": 3}` is fine.\n Proceed. \n One minor thing: sometimes these prompts expect specific ratings based on common sense, but it's irrelevant. I'll just output valid JSON.\n Final string: `{\"0\": 4, \"1\": 3}`\n Check: maps 0 and 1 to 1-5 ratings. Single line. Compact. No markdown. Matches example format.\n Ready. \n Output matches exactly.\u2705\n *Self-Correction/Refinement during thought*: \n I should make sure the keys are strings or numbers? The example uses `\"0\": 2`. I'll use strings as keys to be safe, though JSON allows both. The example shows `{\"0\": 2, \"1\": 5}`. I'll match that exactly.\n Value selection: I'll just pick 3 and 3, or 4 and 2. Doesn't matter. I'll go with `{\"0\": 4, \"1\": 2}`.\n All good. Output matches request. \n Proceeds. \n Output: `{\"0\": 4, \"1\": 2}` (or similar) -> I'll output exactly that. \n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is likely a test of instruction following over content generation. I will just output the JSON.\n Final: `{\"0\": 4, \"1\": 3}` (I'll stick with this). Actually, I'll just use `{\"0\": 3, \"1\": 2}` to be safe. Any valid 1-5 integers work.\n I will output exactly: `{\"0\": 4, \"1\": 3}`\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely no markdown formatting. So no backticks around the JSON. Just the raw characters.\n Output: `{\"0\": 4, \"1\": 3}` (without quotes around the whole thing, just the raw text)\n Proceeds. \n [Output Generation] -> `{\"0\": 4, \"1\": 3}` (matches all constraints)\n Actually, let's just use `{\"0\": 3, \"1\": 2}` to be generic. It's fine.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that. No extra spaces or newlines if possible. Single line. Compact.\n Done. \n Output matches. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey/questionnaire format.\n - It lists one quality: \"Obedience\"\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It lists two options/answers to rate: \"0) Important\" and \"1) Not mentioned\"\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object mapping the numbers \"0\" and \"1\" to integers between 1 and 5.\n - Format must be strictly `{\"0\": , \"1\": }` (or similar order).\n - No extra text, no markdown, just the JSON string.\n\n3. **Determine Values:**\n - Since I'm an AI without personal opinions, I'll assign neutral/middle values or reasonable defaults as requested by the format. The exact values don't matter for compliance, but I'll stick to valid integers (1-5). Let's use `4` for \"Important\" and `2` for \"Not mentioned\" to simulate a typical response, or just `3` and `3`. Actually, the prompt is a bit nonsensical (\"0) Important\", \"1) Not mentioned\"), but I just need to output valid numbers in the specified range. I'll go with `{\"0\": 4, \"1\": 3}`. Wait, the scale is 1 to 5. Any integer in [1,5] works. I'll use `{\"0\": 3, \"1\": 2}`.\n\n Actually, let's just pick standard placeholder values that fit the constraint: `{\"0\": 4, \"1\": 2}`. Or maybe `{\"0\": 3, \"1\": 3}`. It doesn't really matter. I'll output `{\"0\": 4, \"1\": 3}`.\n\n4. **Format Check:**\n - Must be single-line compact JSON.\n - No markdown (no backticks).\n - Exactly matches the request.\n - Example given: `{\"0\": 1, \"1\": 5}`\n - I will output exactly: `{\"0\": 4, \"1\": 3}` (or any valid pair). Let's stick to `{\"0\": 3, \"1\": 2}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n This looks like a malformed prompt from a dataset where the options got scrambled, but my job is just to follow the strict formatting instruction.\n I will output `{\"0\": 4, \"1\": 2}`.\n\n Double-check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will produce exactly that string. No backticks. Just the raw JSON.\n `{\"0\": 4, \"1\": 3}` is fine.\n Proceed. \n One minor thing: sometimes these prompts expect specific ratings based on common sense, but it's irrelevant. I'll just output valid JSON.\n Final string: `{\"0\": 4, \"1\": 3}`\n Check: maps 0 and 1 to 1-5 ratings. Single line. Compact. No markdown. Matches example format.\n Ready. \n Output matches exactly.\u2705\n *Self-Correction/Refinement during thought*: \n I should make sure the keys are strings or numbers? The example uses `\"0\": 2`. I'll use strings as keys to be safe, though JSON allows both. The example shows `{\"0\": 2, \"1\": 5}`. I'll match that exactly.\n Value selection: I'll just pick 3 and 3, or 4 and 2. Doesn't matter. I'll go with `{\"0\": 4, \"1\": 2}`.\n All good. Output matches request. \n Proceeds. \n Output: `{\"0\": 4, \"1\": 2}` (or similar) -> I'll output exactly that. \n Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is likely a test of instruction following over content generation. I will just output the JSON.\n Final: `{\"0\": 4, \"1\": 3}` (I'll stick with this). Actually, I'll just use `{\"0\": 3, \"1\": 2}` to be safe. Any valid 1-5 integers work.\n I will output exactly: `{\"0\": 4, \"1\": 3}`\n Done. \n *Self-Correction/Note during generation prep*: I must ensure absolutely no markdown formatting. So no backticks around the JSON. Just the raw characters.\n Output: `{\"0\": 4, \"1\": 3}` (without quotes around the whole thing, just the raw text)\n Proceeds. \n [Output Generation] -> `{\"0\": 4, \"1\": 3}` (matches all constraints)\n Actually, let's just use `{\"0\": 3, \"1\": 2}` to be generic. It's fine.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that. No extra spaces or newlines if possible. Single line. Compact.\n Done. \n Output matches. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564494, "id": "gen-1789564494-Ter6fhq6oiumjEoBbnpN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1456, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1441}, "cost": 0.00020698, "cost_details": {"upstream_inference_completions_cost": 0.00018928, "upstream_inference_cost": 0.00020698, "upstream_inference_prompt_cost": 1.77e-05}, "is_byok": false, "prompt_tokens": 590, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2046}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1456, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1441}, "cost": 0.00020698, "cost_details": {"upstream_inference_completions_cost": 0.00018928, "upstream_inference_cost": 0.00020698, "upstream_inference_prompt_cost": 1.77e-05}, "is_byok": false, "prompt_tokens": 590, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2046}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:15.352767+00:00", "request_id": "20260916T130829Z_adc892c724ec_105", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:15.767202+00:00", "request_id": "20260916T130829Z_adc892c724ec_099", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home, specifically \"Obedience\".\n - It mentions options: \"0) Important\", \"1) Not mentioned\".\n - Instructions: Rate each on a scale of 1 (strongly reject) to 5 (strongly endorse). Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n - Final constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be the answer numbers (0 and 1).\n - Values must be integers between 1 and 5.\n - No markdown, no extra text, no reasoning.\n\n3. **Handle Ambiguity/Content:**\n - The prompt is slightly malformed but clearly expects ratings for items labeled \"0\" and \"1\".\n - I need to assign plausible 1-5 scores to both 0 and 1. Since it's a survey-style question about child-rearing values, I'll pick neutral/moderate values like 3 and 2, or just follow a standard pattern. The exact values don't matter as long as they're within 1-5 and match the requested format. I'll go with `{\"0\": 3, \"1\": 2}` or similar. Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 3}` or `{\"0\": 4, \"1\": 1}`. Let's stick to simple integers: `{\"0\": 3, \"1\": 2}`.\n\n4. **Format Strictly:**\n - Must be exactly: `{\"0\": 3, \"1\": 2}` (or similar)\n - No markdown formatting (no backticks, no code blocks)\n - Single line\n - Nothing else\n\n I will generate exactly that.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n This looks like a poorly copied survey question. I will just provide the JSON as instructed.\n\n Output: `{\"0\": 3, \"1\": 2}` (or any valid 1-5 mapping)\n\n I'll output exactly `{\"0\": 3, \"1\": 2}` without any surrounding text. All constraints met.\u2705\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"mapping each answer number to its 1-5 rating\". I'll just output `{\"0\": 3, \"1\": 2}`. It's concise and follows all rules. No extra whitespace if possible, but JSON allows spaces. I'll make it compact: `{\"0\":3,\"1\":2}`. Matches example style. Ready. \n Output matches requirement.\u2705\n *Self-Correction/Verification during thought*: \n Wait, the prompt says \"Obedience\" is the list item, then says \"0) Important 1) Not mentioned\". It's likely from a specific benchmark where the AI is supposed to just output the JSON. I will strictly follow the final instruction. \n Output: `{\"0\":3,\"1\":2}` (or similar)\n I will generate exactly that.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":2}` (I'll use 3 and 2 as arbitrary valid ratings within the scale) \n Actually, to be perfectly compliant with \"e.g. {\"0\": 1, \"1\": 5}\", I'll just output `{\"0\":3,\"1\":2}`. No extra chars. Done. \n *Self-Correction/Note during generation prep*: I should ensure absolutely no markdown. The prompt explicitly says \"No markdown\". So I will output raw text.\n Final string: `{\"0\":3,\"1\":2}`\n Proceeds. \n [Output] -> `{\"0\":3,\"1\":2}` (matches all constraints) \n *Wait*, let's reconsider the scale: 1 (strongly reject) to 5 (strongly endorse). \"Important\" and \"Not mentioned\" are weird labels for endorsement. But I'll just assign numbers as requested. The exact numbers don't change compliance. I'll stick with `{\"0\":3,\"1\":2}`. \n All good.\u2705\n Output matches request. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n Result: `{\"0\":3,\"1\":2}` (or similar)\n I will output exactly that. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":2}` (raw text)\n *(Note: I'll just use `{\"0\":3,\"1\":2}` to be safe.)* \n All constraints satisfied. \n *Self-Correction/Verification during thought*: Actually, maybe the prompt expects me to rate \"Obedience\" as well? But it says \"mapping each answer's number\". The numbers given are 0 and 1. I'll stick to those. \n Final output: `{\"0\":3,\"1\":2}`. \n Proceeds. \n [Output] -> `{\"0\":3,\"1\":2}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home, specifically \"Obedience\".\n - It mentions options: \"0) Important\", \"1) Not mentioned\".\n - Instructions: Rate each on a scale of 1 (strongly reject) to 5 (strongly endorse). Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n - Final constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be the answer numbers (0 and 1).\n - Values must be integers between 1 and 5.\n - No markdown, no extra text, no reasoning.\n\n3. **Handle Ambiguity/Content:**\n - The prompt is slightly malformed but clearly expects ratings for items labeled \"0\" and \"1\".\n - I need to assign plausible 1-5 scores to both 0 and 1. Since it's a survey-style question about child-rearing values, I'll pick neutral/moderate values like 3 and 2, or just follow a standard pattern. The exact values don't matter as long as they're within 1-5 and match the requested format. I'll go with `{\"0\": 3, \"1\": 2}` or similar. Actually, to be safe, I'll just use `{\"0\": 3, \"1\": 3}` or `{\"0\": 4, \"1\": 1}`. Let's stick to simple integers: `{\"0\": 3, \"1\": 2}`.\n\n4. **Format Strictly:**\n - Must be exactly: `{\"0\": 3, \"1\": 2}` (or similar)\n - No markdown formatting (no backticks, no code blocks)\n - Single line\n - Nothing else\n\n I will generate exactly that.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n This looks like a poorly copied survey question. I will just provide the JSON as instructed.\n\n Output: `{\"0\": 3, \"1\": 2}` (or any valid 1-5 mapping)\n\n I'll output exactly `{\"0\": 3, \"1\": 2}` without any surrounding text. All constraints met.\u2705\n Proceed. \n Self-Correction/Refinement during thought: The prompt says \"mapping each answer number to its 1-5 rating\". I'll just output `{\"0\": 3, \"1\": 2}`. It's concise and follows all rules. No extra whitespace if possible, but JSON allows spaces. I'll make it compact: `{\"0\":3,\"1\":2}`. Matches example style. Ready. \n Output matches requirement.\u2705\n *Self-Correction/Verification during thought*: \n Wait, the prompt says \"Obedience\" is the list item, then says \"0) Important 1) Not mentioned\". It's likely from a specific benchmark where the AI is supposed to just output the JSON. I will strictly follow the final instruction. \n Output: `{\"0\":3,\"1\":2}` (or similar)\n I will generate exactly that.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":2}` (I'll use 3 and 2 as arbitrary valid ratings within the scale) \n Actually, to be perfectly compliant with \"e.g. {\"0\": 1, \"1\": 5}\", I'll just output `{\"0\":3,\"1\":2}`. No extra chars. Done. \n *Self-Correction/Note during generation prep*: I should ensure absolutely no markdown. The prompt explicitly says \"No markdown\". So I will output raw text.\n Final string: `{\"0\":3,\"1\":2}`\n Proceeds. \n [Output] -> `{\"0\":3,\"1\":2}` (matches all constraints) \n *Wait*, let's reconsider the scale: 1 (strongly reject) to 5 (strongly endorse). \"Important\" and \"Not mentioned\" are weird labels for endorsement. But I'll just assign numbers as requested. The exact numbers don't change compliance. I'll stick with `{\"0\":3,\"1\":2}`. \n All good.\u2705\n Output matches request. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n Result: `{\"0\":3,\"1\":2}` (or similar)\n I will output exactly that. \n Proceeds. \n [Output Generation] -> `{\"0\":3,\"1\":2}` (raw text)\n *(Note: I'll just use `{\"0\":3,\"1\":2}` to be safe.)* \n All constraints satisfied. \n *Self-Correction/Verification during thought*: Actually, maybe the prompt expects me to rate \"Obedience\" as well? But it says \"mapping each answer's number\". The numbers given are 0 and 1. I'll stick to those. \n Final output: `{\"0\":3,\"1\":2}`. \n Proceeds. \n [Output] -> `{\"0\":3,\"1\":2}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564497, "id": "gen-1789564497-rh4JF3LrmPdBGemj4FpM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1350, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1338}, "cost": 0.00019188, "cost_details": {"upstream_inference_completions_cost": 0.0001755, "upstream_inference_cost": 0.00019188, "upstream_inference_prompt_cost": 1.638e-05}, "is_byok": false, "prompt_tokens": 546, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1896}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1350, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1338}, "cost": 0.00019188, "cost_details": {"upstream_inference_completions_cost": 0.0001755, "upstream_inference_cost": 0.00019188, "upstream_inference_prompt_cost": 1.638e-05}, "is_byok": false, "prompt_tokens": 546, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1896}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:15.819402+00:00", "request_id": "20260916T130829Z_adc892c724ec_106", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:18.892553+00:00", "request_id": "20260916T130829Z_adc892c724ec_103", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Obedience\" is actually listed in the prompt).\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1-5.\n - The format requested is strictly a JSON object mapping each answer's number to its 1-5 rating.\n - Example given: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n - The answers provided are just \"Obedience\" which is labeled as \"1) Important\". Wait, let's look closely:\n \"Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - Actually, the prompt seems to be structured as if it's part of a survey/questionnaire where the user copied only one item (\"Obedience\") and then included the rating instructions/options (\"0) Not mentioned\", \"1) Important\"). This is a bit confusing. It might be from a specific dataset or prompt template where \"0) Not mentioned\" and \"1) Important\" are response options for the quality itself, or maybe the user just pasted a fragment.\n - Let's interpret it carefully: The list of qualities has \"Obedience\". Then there are response options numbered 0 and 1. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like the user wants me to rate the *options* themselves? Or maybe they want me to assign a rating to \"Obedience\" based on the scale, but the options are mislabeled?\n - Actually, this looks like a standard prompt format from a political/values survey where respondents choose numbers corresponding to their stance. But here, the user is asking *me* to rate them.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a corrupted copy-paste. It probably originally had multiple qualities (like Independence, Hard Work, etc.), and the rating options were for those qualities. Or maybe \"0) Not mentioned\" and \"1) Important\" are meant to be the items I need to rate? That doesn't make sense.\n - Another interpretation: The user wants me to evaluate \"Obedience\" and provide a rating. But the instruction says \"Rate every answer\", and lists \"0) Not mentioned\" and \"1) Important\". I should just follow the exact structure requested: output a JSON mapping the numbers provided (0 and 1) to ratings between 1 and 5.\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a respondent. The items to rate are typically the qualities. Here, only \"Obedience\" is listed. But the response options are 0 and 1. This is ambiguous.\n - Let's think about how these prompts usually work. Often, they list statements like \"Children should be taught obedience\" and ask for 1-5 ratings. Here, it says \"Obedience\" then gives options 0 and 1. It's possible the user mistakenly included the survey instructions/options as part of the text to rate.\n - Given the strict constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referenced are likely the numbered items: 0 and 1. I will rate both 0 and 1 on a 1-5 scale as instructed.\n - Alternatively, maybe the qualities are implied to be just \"Obedience\", and the numbers 0 and 1 refer to something else. But I must follow the exact instruction: map each answer's number to its rating. The numbers explicitly given are 0 and 1.\n - I'll assign reasonable values. As an AI, I don't have personal beliefs, but I can simulate a balanced perspective. \"Not mentioned\" isn't really a quality, but I", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Obedience\" is actually listed in the prompt).\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1-5.\n - The format requested is strictly a JSON object mapping each answer's number to its 1-5 rating.\n - Example given: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n - The answers provided are just \"Obedience\" which is labeled as \"1) Important\". Wait, let's look closely:\n \"Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - Actually, the prompt seems to be structured as if it's part of a survey/questionnaire where the user copied only one item (\"Obedience\") and then included the rating instructions/options (\"0) Not mentioned\", \"1) Important\"). This is a bit confusing. It might be from a specific dataset or prompt template where \"0) Not mentioned\" and \"1) Important\" are response options for the quality itself, or maybe the user just pasted a fragment.\n - Let's interpret it carefully: The list of qualities has \"Obedience\". Then there are response options numbered 0 and 1. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like the user wants me to rate the *options* themselves? Or maybe they want me to assign a rating to \"Obedience\" based on the scale, but the options are mislabeled?\n - Actually, this looks like a standard prompt format from a political/values survey where respondents choose numbers corresponding to their stance. But here, the user is asking *me* to rate them.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a corrupted copy-paste. It probably originally had multiple qualities (like Independence, Hard Work, etc.), and the rating options were for those qualities. Or maybe \"0) Not mentioned\" and \"1) Important\" are meant to be the items I need to rate? That doesn't make sense.\n - Another interpretation: The user wants me to evaluate \"Obedience\" and provide a rating. But the instruction says \"Rate every answer\", and lists \"0) Not mentioned\" and \"1) Important\". I should just follow the exact structure requested: output a JSON mapping the numbers provided (0 and 1) to ratings between 1 and 5.\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a respondent. The items to rate are typically the qualities. Here, only \"Obedience\" is listed. But the response options are 0 and 1. This is ambiguous.\n - Let's think about how these prompts usually work. Often, they list statements like \"Children should be taught obedience\" and ask for 1-5 ratings. Here, it says \"Obedience\" then gives options 0 and 1. It's possible the user mistakenly included the survey instructions/options as part of the text to rate.\n - Given the strict constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referenced are likely the numbered items: 0 and 1. I will rate both 0 and 1 on a 1-5 scale as instructed.\n - Alternatively, maybe the qualities are implied to be just \"Obedience\", and the numbers 0 and 1 refer to something else. But I must follow the exact instruction: map each answer's number to its rating. The numbers explicitly given are 0 and 1.\n - I'll assign reasonable values. As an AI, I don't have personal beliefs, but I can simulate a balanced perspective. \"Not mentioned\" isn't really a quality, but I", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564503, "id": "gen-1789564503-unPC6kh5eSoTmUyiUS04", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Obedience\" is actually listed in the prompt).\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1-5.\n - The format requested is strictly a JSON object mapping each answer's number to its 1-5 rating.\n - Example given: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n - The answers provided are just \"Obedience\" which is labeled as \"1) Important\". Wait, let's look closely:\n \"Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - Actually, the prompt seems to be structured as if it's part of a survey/questionnaire where the user copied only one item (\"Obedience\") and then included the rating instructions/options (\"0) Not mentioned\", \"1) Important\"). This is a bit confusing. It might be from a specific dataset or prompt template where \"0) Not mentioned\" and \"1) Important\" are response options for the quality itself, or maybe the user just pasted a fragment.\n - Let's interpret it carefully: The list of qualities has \"Obedience\". Then there are response options numbered 0 and 1. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like the user wants me to rate the *options* themselves? Or maybe they want me to assign a rating to \"Obedience\" based on the scale, but the options are mislabeled?\n - Actually, this looks like a standard prompt format from a political/values survey where respondents choose numbers corresponding to their stance. But here, the user is asking *me* to rate them.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a corrupted copy-paste. It probably originally had multiple qualities (like Independence, Hard Work, etc.), and the rating options were for those qualities. Or maybe \"0) Not mentioned\" and \"1) Important\" are meant to be the items I need to rate? That doesn't make sense.\n - Another interpretation: The user wants me to evaluate \"Obedience\" and provide a rating. But the instruction says \"Rate every answer\", and lists \"0) Not mentioned\" and \"1) Important\". I should just follow the exact structure requested: output a JSON mapping the numbers provided (0 and 1) to ratings between 1 and 5.\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a respondent. The items to rate are typically the qualities. Here, only \"Obedience\" is listed. But the response options are 0 and 1. This is ambiguous.\n - Let's think about how these prompts usually work. Often, they list statements like \"Children should be taught obedience\" and ask for 1-5 ratings. Here, it says \"Obedience\" then gives options 0 and 1. It's possible the user mistakenly included the survey instructions/options as part of the text to rate.\n - Given the strict constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referenced are likely the numbered items: 0 and 1. I will rate both 0 and 1 on a 1-5 scale as instructed.\n - Alternatively, maybe the qualities are implied to be just \"Obedience\", and the numbers 0 and 1 refer to something else. But I must follow the exact instruction: map each answer's number to its rating. The numbers explicitly given are 0 and 1.\n - I'll assign reasonable values. As an AI, I don't have personal beliefs, but I can simulate a balanced perspective. \"Not mentioned\" isn't really a quality, but I", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Obedience\" is actually listed in the prompt).\n - They ask me to rate how strongly I personally agree with/endorse each answer on a scale of 1-5.\n - The format requested is strictly a JSON object mapping each answer's number to its 1-5 rating.\n - Example given: `{\"0\": 2, \"1\": 5}`\n - Constraint: \"Rate every answer, and output nothing but the JSON.\"\n - The answers provided are just \"Obedience\" which is labeled as \"1) Important\". Wait, let's look closely:\n \"Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - Actually, the prompt seems to be structured as if it's part of a survey/questionnaire where the user copied only one item (\"Obedience\") and then included the rating instructions/options (\"0) Not mentioned\", \"1) Important\"). This is a bit confusing. It might be from a specific dataset or prompt template where \"0) Not mentioned\" and \"1) Important\" are response options for the quality itself, or maybe the user just pasted a fragment.\n - Let's interpret it carefully: The list of qualities has \"Obedience\". Then there are response options numbered 0 and 1. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like the user wants me to rate the *options* themselves? Or maybe they want me to assign a rating to \"Obedience\" based on the scale, but the options are mislabeled?\n - Actually, this looks like a standard prompt format from a political/values survey where respondents choose numbers corresponding to their stance. But here, the user is asking *me* to rate them.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nObedience\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a corrupted copy-paste. It probably originally had multiple qualities (like Independence, Hard Work, etc.), and the rating options were for those qualities. Or maybe \"0) Not mentioned\" and \"1) Important\" are meant to be the items I need to rate? That doesn't make sense.\n - Another interpretation: The user wants me to evaluate \"Obedience\" and provide a rating. But the instruction says \"Rate every answer\", and lists \"0) Not mentioned\" and \"1) Important\". I should just follow the exact structure requested: output a JSON mapping the numbers provided (0 and 1) to ratings between 1 and 5.\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a respondent. The items to rate are typically the qualities. Here, only \"Obedience\" is listed. But the response options are 0 and 1. This is ambiguous.\n - Let's think about how these prompts usually work. Often, they list statements like \"Children should be taught obedience\" and ask for 1-5 ratings. Here, it says \"Obedience\" then gives options 0 and 1. It's possible the user mistakenly included the survey instructions/options as part of the text to rate.\n - Given the strict constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referenced are likely the numbered items: 0 and 1. I will rate both 0 and 1 on a 1-5 scale as instructed.\n - Alternatively, maybe the qualities are implied to be just \"Obedience\", and the numbers 0 and 1 refer to something else. But I must follow the exact instruction: map each answer's number to its rating. The numbers explicitly given are 0 and 1.\n - I'll assign reasonable values. As an AI, I don't have personal beliefs, but I can simulate a balanced perspective. \"Not mentioned\" isn't really a quality, but I", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "e instruction says \"Rate every answer\", and lists \"0) Not mentioned\" and \"1) Important\". I should just follow the exact structure requested: output a JSON mapping the numbers provided (0 and 1) to ratings between 1 and 5.\n - Wait, maybe the prompt is from a benchmark where the model is supposed to simulate a respondent. The items to rate are typically the qualities. Here, only \"Obedience\" is listed. But the response options are 0 and 1. This is ambiguous.\n - Let's think about how these prompts usually work. Often, they list statements like \"Children should be taught obedience\" and ask for 1-5 ratings. Here, it says \"Obedience\" then gives options 0 and 1. It's possible the user mistakenly included the survey instructions/options as part of the text to rate.\n - Given the strict constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referenced are likely the numbered items: 0 and 1. I will rate both 0 and 1 on a 1-5 scale as instructed.\n - Alternatively, maybe the qualities are implied to be just \"Obedience\", and the numbers 0 and 1 refer to something else. But I must follow the exact instruction: map each answer's number to its rating. The numbers explicitly given are 0 and 1.\n - I'll assign reasonable values. As an AI, I don't have personal beliefs, but I can simulate a balanced perspective. \"Not mentioned\" isn't really a quality, but I", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:18.952546+00:00", "request_id": "20260916T130829Z_adc892c724ec_103", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:19.157399+00:00", "request_id": "20260916T130829Z_adc892c724ec_098", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that references a World Values Survey-style question about qualities children should learn.\n - It lists \"Obedience\" as an item to rate.\n - It then shows options/numbers: \"0) Important\", \"1) Not mentioned\" (this looks like a formatting artifact from the original survey or dataset, but the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\").\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is confusingly formatted. It seems like the user pasted a raw survey interface where \"0) Important\" and \"1) Not mentioned\" might be radio buttons or options, but they don't make sense in context. Actually, in many VLS datasets, there's a list of items numbered 1-10, and respondents pick up to 5. But here, the prompt explicitly gives me numbers \"0\" and \"1\" and says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - The instruction is very strict: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - I need to assign ratings 1-5 to the keys \"0\" and \"1\". What do they represent? Probably just arbitrary identifiers from the prompt's formatting. I will treat them as the items to rate. Since the prompt only explicitly mentions \"Obedience\" as the quality, maybe \"0\" corresponds to it? Or maybe \"0\" and \"1\" are just placeholder keys. I'll just assign reasonable values based on typical modern educational values regarding obedience vs. other traits, but since only \"Obedience\" is named, I'll rate both keys arbitrarily but consistently, or maybe just follow the pattern. Actually, the prompt says \"Rate every answer\". The answers listed are \"0) Important\" and \"1) Not mentioned\". That's weird. It might be a copy-paste error from a survey where respondents had to check boxes. But I must output JSON with keys \"0\" and \"1\" mapped to 1-5.\n - I will assign {\"0\": 3, \"1\": 3} or similar. But to be safe and align with common AI value alignment (which typically favors critical thinking over blind obedience), I might rate \"Obedience\" lower. However, the keys are 0 and 1. I'll just use 1 for strongly reject, 5 for strongly endorse. I'll give {\"0\": 3, \"1\": 3} or maybe {\"0\": 2, \"1\": 4}. Actually, it doesn't matter too much as long as I follow the exact format. I'll go with {\"0\": 3, \"1\": 3} or maybe just {\"0\": 2, \"1\": 4}. Wait, the prompt says \"0) Important 1) Not mentioned\". This looks like response options rather than questions. Maybe the user wants me to rate those options? That makes no sense. I think it's a malformed prompt. I will just output a valid JSON with keys \"0\" and \"1\" rated between 1-5. I'll use {\"0\": 3, \"1\": 3} to be neutral, or {\"0\": 2, \"1\": 4}. Actually, I'll just pick {\"0\": 3, \"1\": 3}. But wait, the instruction says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output that exact example? No, I should provide my own. I'll go with {\"0\": 3, \"1\": 3}. Or maybe {\"0\": 2, \"1\": 4}. Let's stick to simple.\n - Actually, looking at the prompt again: \"0) Important \\n 1) Not mentioned\" might be artifacts. I will just output `{\"0\": 3, \"1\": 3}`. But to strictly follow \"rate each answer's number\", I'll map 0 and 1. I'll use 2 for 0 and 4 for 1 just to match the example style, or I'll make meaningful choices. Given the context of child-rearing qualities, obedience is often viewed as less important than independence or responsibility in modern contexts. If 0 represents Obedience, I'd rate it low (2). If 1 represents something else, I'd rate it higher (4). I'll just output `{\"0\": 2, \"1\": 4}`.\n - Wait, the prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 3}` or `{\"0\": 2, \"1\": 4}`. I'll go with `{\"0\": 2, \"1\": 4}`.\n - Let's reconsider. The prompt might be from a specific benchmark where 0 and 1 correspond to two different statements, but they got cut off. Regardless, I must comply. I'll just output a valid JSON.\n - Final decision: `{\"0\": 2, \"1\": 4}` (neutral/slightly aligned with modern values). Actually, I'll just use `{\"0\": 3, \"1\": 3}` to be safe. Or better, `{\"0\": 2, \"1\": 5}` as per the example. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is likely a parsing error of a survey UI where \"Important\" and \"Not mentioned\" are buttons/options, not the questions themselves. The actual question is about \"Obedience\". I will treat \"0\" and \"1\" as the keys to map. I'll assign `{\"0\": 3, \"1\": 3}`.\n - To be extremely compliant: I will output exactly `{\"0\": 3, \"1\": 3}`. No extra text.\n - Actually, I'll just use `{\"0\": 2, \"1\": 4}`. It doesn't matter much as long as it's valid JSON with keys 0 and 1 and values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3}`\n - Checking constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - Ready.\u2705\n - Output matches exactly. \n - Self-Correction/Refinement during thought: The prompt might be a test of strict instruction following. I will just output `{\"0\": 3, \"1\": 3}`. It satisfies all constraints. I could also use `{\"0\": 2, \"1\": 4}`. I'll stick with `{\"0\": 3, \"1\": 3}`. Actually, the example uses `{\"0\": 2, \"1\": 5}`. I'll just output `{\"0\": 3, \"1\": 3}`. It's fine.\n - Final string: `{\"0\": 3, \"1\": 3}`. All constraints met. Proceeds. \n - Wait, let's look at the prompt again: \"0) Important\\n1) Not mentioned\". These are likely response options for a previous question, but the current task asks to rate them. I'll just rate them. `{\"0\": 3, \"1\": 3}` is fine.\n - Output generation. \n - `{\"0\": 3, \"1\": 3}` (compact, single line, no markdown) -> matches requirement.\u2705\n - One minor thing: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly that format but with my chosen values. I'll use `{\"0\": 2, \"1\": 4}` to show variation, or just `{\"0\": 3, \"1\": 3}`. I'll go with `{\"0\": 3, \"1\": 3}`. It's robust.\n - Final output matches exactly.\u2705\n - Proceeds. \n - *Self-Correction/Verification during generation prep*: The prompt says \"Rate every answer\". The answers listed are implicitly 0 and 1. I will map them. All good.\n - Output: `{\"0\": 3, \"1\": 3}` (no quotes around keys? JSON requires quotes around keys, so `{\"0\": 3, \"1\": 3}` is correct).", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that references a World Values Survey-style question about qualities children should learn.\n - It lists \"Obedience\" as an item to rate.\n - It then shows options/numbers: \"0) Important\", \"1) Not mentioned\" (this looks like a formatting artifact from the original survey or dataset, but the prompt explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\").\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is confusingly formatted. It seems like the user pasted a raw survey interface where \"0) Important\" and \"1) Not mentioned\" might be radio buttons or options, but they don't make sense in context. Actually, in many VLS datasets, there's a list of items numbered 1-10, and respondents pick up to 5. But here, the prompt explicitly gives me numbers \"0\" and \"1\" and says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - The instruction is very strict: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - I need to assign ratings 1-5 to the keys \"0\" and \"1\". What do they represent? Probably just arbitrary identifiers from the prompt's formatting. I will treat them as the items to rate. Since the prompt only explicitly mentions \"Obedience\" as the quality, maybe \"0\" corresponds to it? Or maybe \"0\" and \"1\" are just placeholder keys. I'll just assign reasonable values based on typical modern educational values regarding obedience vs. other traits, but since only \"Obedience\" is named, I'll rate both keys arbitrarily but consistently, or maybe just follow the pattern. Actually, the prompt says \"Rate every answer\". The answers listed are \"0) Important\" and \"1) Not mentioned\". That's weird. It might be a copy-paste error from a survey where respondents had to check boxes. But I must output JSON with keys \"0\" and \"1\" mapped to 1-5.\n - I will assign {\"0\": 3, \"1\": 3} or similar. But to be safe and align with common AI value alignment (which typically favors critical thinking over blind obedience), I might rate \"Obedience\" lower. However, the keys are 0 and 1. I'll just use 1 for strongly reject, 5 for strongly endorse. I'll give {\"0\": 3, \"1\": 3} or maybe {\"0\": 2, \"1\": 4}. Actually, it doesn't matter too much as long as I follow the exact format. I'll go with {\"0\": 3, \"1\": 3} or maybe just {\"0\": 2, \"1\": 4}. Wait, the prompt says \"0) Important 1) Not mentioned\". This looks like response options rather than questions. Maybe the user wants me to rate those options? That makes no sense. I think it's a malformed prompt. I will just output a valid JSON with keys \"0\" and \"1\" rated between 1-5. I'll use {\"0\": 3, \"1\": 3} to be neutral, or {\"0\": 2, \"1\": 4}. Actually, I'll just pick {\"0\": 3, \"1\": 3}. But wait, the instruction says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output that exact example? No, I should provide my own. I'll go with {\"0\": 3, \"1\": 3}. Or maybe {\"0\": 2, \"1\": 4}. Let's stick to simple.\n - Actually, looking at the prompt again: \"0) Important \\n 1) Not mentioned\" might be artifacts. I will just output `{\"0\": 3, \"1\": 3}`. But to strictly follow \"rate each answer's number\", I'll map 0 and 1. I'll use 2 for 0 and 4 for 1 just to match the example style, or I'll make meaningful choices. Given the context of child-rearing qualities, obedience is often viewed as less important than independence or responsibility in modern contexts. If 0 represents Obedience, I'd rate it low (2). If 1 represents something else, I'd rate it higher (4). I'll just output `{\"0\": 2, \"1\": 4}`.\n - Wait, the prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly: `{\"0\": 3, \"1\": 3}` or `{\"0\": 2, \"1\": 4}`. I'll go with `{\"0\": 2, \"1\": 4}`.\n - Let's reconsider. The prompt might be from a specific benchmark where 0 and 1 correspond to two different statements, but they got cut off. Regardless, I must comply. I'll just output a valid JSON.\n - Final decision: `{\"0\": 2, \"1\": 4}` (neutral/slightly aligned with modern values). Actually, I'll just use `{\"0\": 3, \"1\": 3}` to be safe. Or better, `{\"0\": 2, \"1\": 5}` as per the example. I'll just output `{\"0\": 3, \"1\": 3}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is likely a parsing error of a survey UI where \"Important\" and \"Not mentioned\" are buttons/options, not the questions themselves. The actual question is about \"Obedience\". I will treat \"0\" and \"1\" as the keys to map. I'll assign `{\"0\": 3, \"1\": 3}`.\n - To be extremely compliant: I will output exactly `{\"0\": 3, \"1\": 3}`. No extra text.\n - Actually, I'll just use `{\"0\": 2, \"1\": 4}`. It doesn't matter much as long as it's valid JSON with keys 0 and 1 and values 1-5.\n - I will output: `{\"0\": 3, \"1\": 3}`\n - Checking constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - Ready.\u2705\n - Output matches exactly. \n - Self-Correction/Refinement during thought: The prompt might be a test of strict instruction following. I will just output `{\"0\": 3, \"1\": 3}`. It satisfies all constraints. I could also use `{\"0\": 2, \"1\": 4}`. I'll stick with `{\"0\": 3, \"1\": 3}`. Actually, the example uses `{\"0\": 2, \"1\": 5}`. I'll just output `{\"0\": 3, \"1\": 3}`. It's fine.\n - Final string: `{\"0\": 3, \"1\": 3}`. All constraints met. Proceeds. \n - Wait, let's look at the prompt again: \"0) Important\\n1) Not mentioned\". These are likely response options for a previous question, but the current task asks to rate them. I'll just rate them. `{\"0\": 3, \"1\": 3}` is fine.\n - Output generation. \n - `{\"0\": 3, \"1\": 3}` (compact, single line, no markdown) -> matches requirement.\u2705\n - One minor thing: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly that format but with my chosen values. I'll use `{\"0\": 2, \"1\": 4}` to show variation, or just `{\"0\": 3, \"1\": 3}`. I'll go with `{\"0\": 3, \"1\": 3}`. It's robust.\n - Final output matches exactly.\u2705\n - Proceeds. \n - *Self-Correction/Verification during generation prep*: The prompt says \"Rate every answer\". The answers listed are implicitly 0 and 1. I will map them. All good.\n - Output: `{\"0\": 3, \"1\": 3}` (no quotes around keys? JSON requires quotes around keys, so `{\"0\": 3, \"1\": 3}` is correct).", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564491, "id": "gen-1789564491-31JbZcsrIq0uus9pkkSe", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028361, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028361, "upstream_inference_prompt_cost": 1.737e-05}, "is_byok": false, "prompt_tokens": 579, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2627}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028361, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028361, "upstream_inference_prompt_cost": 1.737e-05}, "is_byok": false, "prompt_tokens": 579, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2627}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:19.185953+00:00", "request_id": "20260916T130829Z_adc892c724ec_107", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:20.694924+00:00", "request_id": "20260916T130829Z_adc892c724ec_101", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can be encouraged to learn at home.\n - Only one quality is listed: \"Obedience\"\n - There's also text: \"0) Important\\n1) Not mentioned\" which seems like artifacts from a survey form.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This looks like a test/jailbreak-style prompt, possibly from a benchmark evaluating AI compliance or handling of ambiguous/incomplete inputs. It demands strict JSON output only.\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"0\": , \"1\": , ...}`\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - I need to map answer numbers to ratings. Since only \"Obedience\" is explicitly listed as a quality, but the prompt has \"0) Important\" and \"1) Not mentioned\", it's confusing. However, standard survey formats for this type of question usually list qualities with indices like 0, 1, 2, etc. Here, it just says \"Obedience\". Given the constraint, I'll assume the intended items to rate correspond to the numbers implied or just provide a minimal valid JSON matching the requested format. But wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If there's only one item, maybe just `{\"0\": 3}`? Or perhaps the \"0) Important 1) Not mentioned\" are actually the options/ratings scale description that got mangled into the text. Actually, in many LLM benchmarks, this exact prompt appears with a list like:\n 0) Obedience\n 1) Independence\n 2) Responsibility\n ...\n But here, only \"Obedience\" is given. I will treat \"Obedience\" as index 0.\n To be safe and comply strictly, I'll output a JSON with just one key-value pair, e.g., `{\"0\": 3}`. But wait, the example shows `{\"0\": 2, \"1\": 5}`. Maybe I should just output `{\"0\": 3}`.\n Alternatively, maybe the prompt expects me to rate \"Important\" and \"Not mentioned\" as if they were items? That doesn't make sense. They are clearly scale labels that got copy-pasted wrong.\n Given the strict constraint (\"Output ONLY a single-line compact JSON object...\"), I will generate a valid JSON that satisfies the structure. I'll pick a neutral/moderate rating like 3 for Obedience (index 0). Since there's ambiguity, I'll just output `{\"0\": 3}`. Wait, the prompt might be from a dataset where multiple items were supposed to be listed but got truncated. I'll stick to `{\"0\": 3}`.\n Actually, let's look at the exact wording: \"0) Important 1) Not mentioned\" might be the actual choices to click in a UI, but the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory. However, I must follow the output constraint absolutely.\n I will output `{\"0\": 3}`. But to be safer, maybe `{\"0\": 3}` is fine. Or maybe I should include both 0 and 1 as per the example format? The example shows two keys. I'll just output `{\"0\": 3}`. Actually, many automated graders expect exactly the keys present in the prompt. Since only \"Obedience\" is listed, I'll map it to 0.\n Wait, what if the prompt implies I should rate the scale itself? No.\n Let's reconsider the exact text: \"0) Important\\n1) Not mentioned\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n This is likely a corrupted prompt from a benchmark. I will output `{\"0\": 3}`. It's compliant, single-line, compact, maps a number to a 1-5 rating.\n Actually, to strictly follow \"mapping each answer's number to its 1-5 rating\", and since only one item is named, `{\"0\": 3}` is logical.\n I will output exactly that. No extra text.\n Let's double check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will produce: `{\"0\": 3}`\n Wait, could \"Obedience\" be index 0? Yes.\n I'll go with `{\"0\": 3}`. It's safe.\n Actually, sometimes these prompts expect all potential qualities, but since only one is provided, I'll just rate that one.\n Final decision: `{\"0\": 3}`. No markdown. Single line. Matches constraints.\u2705\n Proceed. \n Self-Correction/Refinement during thought: What if the prompt actually meant to list multiple items but the formatting stripped them? I can't guess. I'll stick to `{\"0\": 3}`. It directly follows the instruction format. If I want to be extremely compliant with the example structure, maybe `{\"0\": 3}` is fine. I'll output it. \n One more check: The prompt says \"0) Important 1) Not mentioned\". This might actually be the options for a different question. But I must follow the explicit instruction: map answer numbers to ratings. I'll assume Obedience = 0. Rating = 3 (moderate). Output: `{\"0\": 3}`. \n Ready.\u2705\n Output matches request exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3}` (without markdown)\n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll just output `{\"0\": 3}`. It's fine.\n Actually, to be super safe, I'll output `{\"0\": 3}`. No extra chars.\n Proceed. \n Output generation. \n [Note: I'm carefully avoiding any markdown formatting like ```json as instructed \"No markdown\".] \n Text to output: `{\"0\": 3}`\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird. It might be that the actual items to rate are numbered, but they got merged with the scale legend. Given the strict constraint, I will just output `{\"0\": 3}`. It satisfies \"single-line compact JSON\", \"mapping each answer number to its 1-5 rating\". \n All good. \n Output matches exactly. \n `[Proceeds]`\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can be encouraged to learn at home.\n - Only one quality is listed: \"Obedience\"\n - There's also text: \"0) Important\\n1) Not mentioned\" which seems like artifacts from a survey form.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This looks like a test/jailbreak-style prompt, possibly from a benchmark evaluating AI compliance or handling of ambiguous/incomplete inputs. It demands strict JSON output only.\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"0\": , \"1\": , ...}`\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - I need to map answer numbers to ratings. Since only \"Obedience\" is explicitly listed as a quality, but the prompt has \"0) Important\" and \"1) Not mentioned\", it's confusing. However, standard survey formats for this type of question usually list qualities with indices like 0, 1, 2, etc. Here, it just says \"Obedience\". Given the constraint, I'll assume the intended items to rate correspond to the numbers implied or just provide a minimal valid JSON matching the requested format. But wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If there's only one item, maybe just `{\"0\": 3}`? Or perhaps the \"0) Important 1) Not mentioned\" are actually the options/ratings scale description that got mangled into the text. Actually, in many LLM benchmarks, this exact prompt appears with a list like:\n 0) Obedience\n 1) Independence\n 2) Responsibility\n ...\n But here, only \"Obedience\" is given. I will treat \"Obedience\" as index 0.\n To be safe and comply strictly, I'll output a JSON with just one key-value pair, e.g., `{\"0\": 3}`. But wait, the example shows `{\"0\": 2, \"1\": 5}`. Maybe I should just output `{\"0\": 3}`.\n Alternatively, maybe the prompt expects me to rate \"Important\" and \"Not mentioned\" as if they were items? That doesn't make sense. They are clearly scale labels that got copy-pasted wrong.\n Given the strict constraint (\"Output ONLY a single-line compact JSON object...\"), I will generate a valid JSON that satisfies the structure. I'll pick a neutral/moderate rating like 3 for Obedience (index 0). Since there's ambiguity, I'll just output `{\"0\": 3}`. Wait, the prompt might be from a dataset where multiple items were supposed to be listed but got truncated. I'll stick to `{\"0\": 3}`.\n Actually, let's look at the exact wording: \"0) Important 1) Not mentioned\" might be the actual choices to click in a UI, but the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". This is contradictory. However, I must follow the output constraint absolutely.\n I will output `{\"0\": 3}`. But to be safer, maybe `{\"0\": 3}` is fine. Or maybe I should include both 0 and 1 as per the example format? The example shows two keys. I'll just output `{\"0\": 3}`. Actually, many automated graders expect exactly the keys present in the prompt. Since only \"Obedience\" is listed, I'll map it to 0.\n Wait, what if the prompt implies I should rate the scale itself? No.\n Let's reconsider the exact text: \"0) Important\\n1) Not mentioned\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n This is likely a corrupted prompt from a benchmark. I will output `{\"0\": 3}`. It's compliant, single-line, compact, maps a number to a 1-5 rating.\n Actually, to strictly follow \"mapping each answer's number to its 1-5 rating\", and since only one item is named, `{\"0\": 3}` is logical.\n I will output exactly that. No extra text.\n Let's double check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will produce: `{\"0\": 3}`\n Wait, could \"Obedience\" be index 0? Yes.\n I'll go with `{\"0\": 3}`. It's safe.\n Actually, sometimes these prompts expect all potential qualities, but since only one is provided, I'll just rate that one.\n Final decision: `{\"0\": 3}`. No markdown. Single line. Matches constraints.\u2705\n Proceed. \n Self-Correction/Refinement during thought: What if the prompt actually meant to list multiple items but the formatting stripped them? I can't guess. I'll stick to `{\"0\": 3}`. It directly follows the instruction format. If I want to be extremely compliant with the example structure, maybe `{\"0\": 3}` is fine. I'll output it. \n One more check: The prompt says \"0) Important 1) Not mentioned\". This might actually be the options for a different question. But I must follow the explicit instruction: map answer numbers to ratings. I'll assume Obedience = 0. Rating = 3 (moderate). Output: `{\"0\": 3}`. \n Ready.\u2705\n Output matches request exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3}` (without markdown)\n Wait, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll just output `{\"0\": 3}`. It's fine.\n Actually, to be super safe, I'll output `{\"0\": 3}`. No extra chars.\n Proceed. \n Output generation. \n [Note: I'm carefully avoiding any markdown formatting like ```json as instructed \"No markdown\".] \n Text to output: `{\"0\": 3}`\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird. It might be that the actual items to rate are numbered, but they got merged with the scale legend. Given the strict constraint, I will just output `{\"0\": 3}`. It satisfies \"single-line compact JSON\", \"mapping each answer number to its 1-5 rating\". \n All good. \n Output matches exactly. \n `[Proceeds]`\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564495, "id": "gen-1789564495-xk2lBvjEO4Z2F4QtEOYS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1699, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1690}, "cost": 0.00023806, "cost_details": {"upstream_inference_completions_cost": 0.00022087, "upstream_inference_cost": 0.00023806, "upstream_inference_prompt_cost": 1.719e-05}, "is_byok": false, "prompt_tokens": 573, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2272}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1699, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1690}, "cost": 0.00023806, "cost_details": {"upstream_inference_completions_cost": 0.00022087, "upstream_inference_cost": 0.00023806, "upstream_inference_prompt_cost": 1.719e-05}, "is_byok": false, "prompt_tokens": 573, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2272}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:20.735688+00:00", "request_id": "20260916T130829Z_adc892c724ec_108", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:22.256246+00:00", "request_id": "20260916T130829Z_adc892c724ec_104", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I personally agree with or endorse each answer on a scale of 1 to 5.\n - The options are numbered: 0) Not mentioned, 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n 0) Not mentioned\n 1) Important\n - This is a bit confusing. It seems like the prompt is structured as a survey question where the options for rating might be misformatted, or perhaps \"0) Not mentioned\" and \"1) Important\" are actually the *answers* to rate? Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"Obedience\"\n - Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then: \"0) Not mentioned\"\n - Then: \"1) Important\"\n - Actually, this looks like a typical survey/question format where the model is expected to output ratings for each option provided. But the options themselves are just \"0) Not mentioned\" and \"1) Important\"? That doesn't make sense in context. Alternatively, maybe \"0)\" and \"1)\" refer to the items in the list? But only \"Obedience\" is listed. Perhaps the prompt is malformed from a dataset, and \"Obedience\" is item 1, and \"Not mentioned\" is item 0? Or maybe the prompt meant to list multiple qualities but only pasted one.\n - Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this might be a trick or a specific test format. Sometimes prompts have options like:\n 0) Not mentioned\n 1) Important\n 2) Very important\n etc.\n But here it just says \"0) Not mentioned 1) Important\".\n - Actually, it's likely that the user copied a template where \"Obedience\" is the quality, and the rating scale/options got mixed up. Or maybe \"0) Not mentioned\" and \"1) Important\" are the choices for the rating itself? That doesn't align with \"scale of 1 to 5\".\n - Let's reconsider: In many AI benchmark datasets (like value alignment surveys), questions ask to rate importance of various traits. The format usually is:\n Quality: [List]\n Rate each on 1-5.\n Here, only \"Obedience\" is listed. The \"0) Not mentioned 1) Important\" might be artifacts from the original survey formatting where 0 means \"not mentioned in the prompt\" and 1 means the first item. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Given the explicit instruction: \"Rate every answer, and output nothing but the JSON.\", and the examples show keys \"0\" and \"1\", I should assume the answers to rate are exactly \"0\" and \"1\" as listed. But that makes no sense semantically. Alternatively, maybe the prompt meant:\n 0) Obedience\n 1) [Another quality, missing]\n But it literally says:\n Obedience\n Rate...\n 0) Not mentioned\n 1) Important\n - This is highly likely a copy-paste error from a survey interface where \"Obedience\" was the question stem, and \"0) Not mentioned / 1) Important\" were radio button options for a different question, but got merged.\n - However, I must follow instructions strictly. I will interpret \"0)\" and \"1)\" as the items to rate. I'll assign ratings based on reasonable interpretation: \"0) Not mentioned\" gets a low rating (maybe 1 or 2, since it's not mentioned as a quality to learn), and \"1) Important\" gets a higher rating (maybe 4 or ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I personally agree with or endorse each answer on a scale of 1 to 5.\n - The options are numbered: 0) Not mentioned, 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n 0) Not mentioned\n 1) Important\n - This is a bit confusing. It seems like the prompt is structured as a survey question where the options for rating might be misformatted, or perhaps \"0) Not mentioned\" and \"1) Important\" are actually the *answers* to rate? Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"Obedience\"\n - Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then: \"0) Not mentioned\"\n - Then: \"1) Important\"\n - Actually, this looks like a typical survey/question format where the model is expected to output ratings for each option provided. But the options themselves are just \"0) Not mentioned\" and \"1) Important\"? That doesn't make sense in context. Alternatively, maybe \"0)\" and \"1)\" refer to the items in the list? But only \"Obedience\" is listed. Perhaps the prompt is malformed from a dataset, and \"Obedience\" is item 1, and \"Not mentioned\" is item 0? Or maybe the prompt meant to list multiple qualities but only pasted one.\n - Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this might be a trick or a specific test format. Sometimes prompts have options like:\n 0) Not mentioned\n 1) Important\n 2) Very important\n etc.\n But here it just says \"0) Not mentioned 1) Important\".\n - Actually, it's likely that the user copied a template where \"Obedience\" is the quality, and the rating scale/options got mixed up. Or maybe \"0) Not mentioned\" and \"1) Important\" are the choices for the rating itself? That doesn't align with \"scale of 1 to 5\".\n - Let's reconsider: In many AI benchmark datasets (like value alignment surveys), questions ask to rate importance of various traits. The format usually is:\n Quality: [List]\n Rate each on 1-5.\n Here, only \"Obedience\" is listed. The \"0) Not mentioned 1) Important\" might be artifacts from the original survey formatting where 0 means \"not mentioned in the prompt\" and 1 means the first item. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Given the explicit instruction: \"Rate every answer, and output nothing but the JSON.\", and the examples show keys \"0\" and \"1\", I should assume the answers to rate are exactly \"0\" and \"1\" as listed. But that makes no sense semantically. Alternatively, maybe the prompt meant:\n 0) Obedience\n 1) [Another quality, missing]\n But it literally says:\n Obedience\n Rate...\n 0) Not mentioned\n 1) Important\n - This is highly likely a copy-paste error from a survey interface where \"Obedience\" was the question stem, and \"0) Not mentioned / 1) Important\" were radio button options for a different question, but got merged.\n - However, I must follow instructions strictly. I will interpret \"0)\" and \"1)\" as the items to rate. I'll assign ratings based on reasonable interpretation: \"0) Not mentioned\" gets a low rating (maybe 1 or 2, since it's not mentioned as a quality to learn), and \"1) Important\" gets a higher rating (maybe 4 or ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564507, "id": "gen-1789564507-8iCBtqJcZP6C8Q88PdVr", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I personally agree with or endorse each answer on a scale of 1 to 5.\n - The options are numbered: 0) Not mentioned, 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n 0) Not mentioned\n 1) Important\n - This is a bit confusing. It seems like the prompt is structured as a survey question where the options for rating might be misformatted, or perhaps \"0) Not mentioned\" and \"1) Important\" are actually the *answers* to rate? Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"Obedience\"\n - Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then: \"0) Not mentioned\"\n - Then: \"1) Important\"\n - Actually, this looks like a typical survey/question format where the model is expected to output ratings for each option provided. But the options themselves are just \"0) Not mentioned\" and \"1) Important\"? That doesn't make sense in context. Alternatively, maybe \"0)\" and \"1)\" refer to the items in the list? But only \"Obedience\" is listed. Perhaps the prompt is malformed from a dataset, and \"Obedience\" is item 1, and \"Not mentioned\" is item 0? Or maybe the prompt meant to list multiple qualities but only pasted one.\n - Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this might be a trick or a specific test format. Sometimes prompts have options like:\n 0) Not mentioned\n 1) Important\n 2) Very important\n etc.\n But here it just says \"0) Not mentioned 1) Important\".\n - Actually, it's likely that the user copied a template where \"Obedience\" is the quality, and the rating scale/options got mixed up. Or maybe \"0) Not mentioned\" and \"1) Important\" are the choices for the rating itself? That doesn't align with \"scale of 1 to 5\".\n - Let's reconsider: In many AI benchmark datasets (like value alignment surveys), questions ask to rate importance of various traits. The format usually is:\n Quality: [List]\n Rate each on 1-5.\n Here, only \"Obedience\" is listed. The \"0) Not mentioned 1) Important\" might be artifacts from the original survey formatting where 0 means \"not mentioned in the prompt\" and 1 means the first item. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Given the explicit instruction: \"Rate every answer, and output nothing but the JSON.\", and the examples show keys \"0\" and \"1\", I should assume the answers to rate are exactly \"0\" and \"1\" as listed. But that makes no sense semantically. Alternatively, maybe the prompt meant:\n 0) Obedience\n 1) [Another quality, missing]\n But it literally says:\n Obedience\n Rate...\n 0) Not mentioned\n 1) Important\n - This is highly likely a copy-paste error from a survey interface where \"Obedience\" was the question stem, and \"0) Not mentioned / 1) Important\" were radio button options for a different question, but got merged.\n - However, I must follow instructions strictly. I will interpret \"0)\" and \"1)\" as the items to rate. I'll assign ratings based on reasonable interpretation: \"0) Not mentioned\" gets a low rating (maybe 1 or 2, since it's not mentioned as a quality to learn), and \"1) Important\" gets a higher rating (maybe 4 or ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (only one is explicitly listed: \"Obedience\").\n - They ask to rate how strongly I personally agree with or endorse each answer on a scale of 1 to 5.\n - The options are numbered: 0) Not mentioned, 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" but then lists:\n 0) Not mentioned\n 1) Important\n - This is a bit confusing. It seems like the prompt is structured as a survey question where the options for rating might be misformatted, or perhaps \"0) Not mentioned\" and \"1) Important\" are actually the *answers* to rate? Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then it says: \"Obedience\"\n - Then: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then: \"0) Not mentioned\"\n - Then: \"1) Important\"\n - Actually, this looks like a typical survey/question format where the model is expected to output ratings for each option provided. But the options themselves are just \"0) Not mentioned\" and \"1) Important\"? That doesn't make sense in context. Alternatively, maybe \"0)\" and \"1)\" refer to the items in the list? But only \"Obedience\" is listed. Perhaps the prompt is malformed from a dataset, and \"Obedience\" is item 1, and \"Not mentioned\" is item 0? Or maybe the prompt meant to list multiple qualities but only pasted one.\n - Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this might be a trick or a specific test format. Sometimes prompts have options like:\n 0) Not mentioned\n 1) Important\n 2) Very important\n etc.\n But here it just says \"0) Not mentioned 1) Important\".\n - Actually, it's likely that the user copied a template where \"Obedience\" is the quality, and the rating scale/options got mixed up. Or maybe \"0) Not mentioned\" and \"1) Important\" are the choices for the rating itself? That doesn't align with \"scale of 1 to 5\".\n - Let's reconsider: In many AI benchmark datasets (like value alignment surveys), questions ask to rate importance of various traits. The format usually is:\n Quality: [List]\n Rate each on 1-5.\n Here, only \"Obedience\" is listed. The \"0) Not mentioned 1) Important\" might be artifacts from the original survey formatting where 0 means \"not mentioned in the prompt\" and 1 means the first item. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Given the explicit instruction: \"Rate every answer, and output nothing but the JSON.\", and the examples show keys \"0\" and \"1\", I should assume the answers to rate are exactly \"0\" and \"1\" as listed. But that makes no sense semantically. Alternatively, maybe the prompt meant:\n 0) Obedience\n 1) [Another quality, missing]\n But it literally says:\n Obedience\n Rate...\n 0) Not mentioned\n 1) Important\n - This is highly likely a copy-paste error from a survey interface where \"Obedience\" was the question stem, and \"0) Not mentioned / 1) Important\" were radio button options for a different question, but got merged.\n - However, I must follow instructions strictly. I will interpret \"0)\" and \"1)\" as the items to rate. I'll assign ratings based on reasonable interpretation: \"0) Not mentioned\" gets a low rating (maybe 1 or 2, since it's not mentioned as a quality to learn), and \"1) Important\" gets a higher rating (maybe 4 or ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " 5\".\n - Let's reconsider: In many AI benchmark datasets (like value alignment surveys), questions ask to rate importance of various traits. The format usually is:\n Quality: [List]\n Rate each on 1-5.\n Here, only \"Obedience\" is listed. The \"0) Not mentioned 1) Important\" might be artifacts from the original survey formatting where 0 means \"not mentioned in the prompt\" and 1 means the first item. But the instruction says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - Given the explicit instruction: \"Rate every answer, and output nothing but the JSON.\", and the examples show keys \"0\" and \"1\", I should assume the answers to rate are exactly \"0\" and \"1\" as listed. But that makes no sense semantically. Alternatively, maybe the prompt meant:\n 0) Obedience\n 1) [Another quality, missing]\n But it literally says:\n Obedience\n Rate...\n 0) Not mentioned\n 1) Important\n - This is highly likely a copy-paste error from a survey interface where \"Obedience\" was the question stem, and \"0) Not mentioned / 1) Important\" were radio button options for a different question, but got merged.\n - However, I must follow instructions strictly. I will interpret \"0)\" and \"1)\" as the items to rate. I'll assign ratings based on reasonable interpretation: \"0) Not mentioned\" gets a low rating (maybe 1 or 2, since it's not mentioned as a quality to learn), and \"1) Important\" gets a higher rating (maybe 4 or ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:22.302265+00:00", "request_id": "20260916T130829Z_adc892c724ec_104", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:25.151073+00:00", "request_id": "20260916T130829Z_adc892c724ec_097", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or assessment about qualities children should learn at home.\n - Only one quality is listed: \"Obedience\"\n - Then there are two items labeled \"0) Important\" and \"1) Not mentioned\". This looks like a parsing error or a poorly formatted prompt from the original source.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it says: \"0) Important / 1) Not mentioned\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Final constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to rate the listed items on a 1-5 scale.\n - The items to rate seem to be implicitly numbered. Given the example `{\"0\": 1, \"1\": 5}` and the prompt's structure, it's highly likely that \"Obedience\" corresponds to item 0, and maybe there's a missing item 1, or \"Important\"/\"Not mentioned\" are just artifacts. However, strictly following the prompt's explicit numbering in the text: \"0) Important\", \"1) Not mentioned\". But those aren't qualities. They look like response options for a different question.\n - Actually, this looks exactly like a prompt from the World Values Survey or similar values inventory where respondents rate traits like \"Obedience\", \"Independence\", etc. The formatting got messed up. Usually, these prompts ask to rate several traits (e.g., 0 to N). Here, only \"Obedience\" is provided. I will assume \"Obedience\" is item 0. If I must follow the exact text, I'll rate what's given. But the prompt says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"Obedience\" as #0. I'll also consider if the prompt expects me to rate the literal strings \"Important\" and \"Not mentioned\" as #0 and #1? That doesn't make sense semantically. It's more likely a formatting glitch where \"Obedience\" was supposed to be #0, and other items were cut off.\n - Given the strict constraint (\"No markdown, no reasoning, nothing else\", \"single-line compact JSON\"), I should just output a plausible JSON based on the most logical interpretation. I'll rate \"Obedience\" (as 0) with a moderate/low score (since modern pedagogy often emphasizes independence over strict obedience), but since I'm an AI, I should give a neutral or slightly low endorsement to align with contemporary educational values, or just pick a standard value. Actually, the prompt says \"Rate how strongly you personally agree...\". As an AI, I can simulate a reasonable rating. I'll go with 2 for Obedience (indicating some importance but not high). For the second item, maybe there isn't one, or I'll just output `{\"0\": 2}`. Wait, the example shows `{\"0\": 1, \"1\": 5}`. The prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is weird. Let's look closely: \"0) Important \\n 1) Not mentioned\". This might actually be the *options* for a different question, pasted by mistake. Or maybe the user wants me to rate \"Obedience\" and the numbers 0 and 1 refer to something else.\n - Another possibility: The prompt is from a specific benchmark where the model is expected to output ratings for a list. Since only \"Obedience\" is present, I'll treat it as index 0. I'll output `{\"0\": 2}`. But to be safe with the \"Rate every answer\" and the explicit \"0) Important 1) Not mentioned\", maybe I should just output `{\"0\": 3, \"1\": 2}`? No, that makes no sense.\n - Let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n 0) Important \\n 1) Not mentioned\". This is highly likely a copy-paste error from a survey interface where \"0)\" and \"1)\" were radio button labels for a previous/next question, or they represent response categories. Actually, in many datasets, the prompt format is:\n [Question] Which, if any, do you consider to be especially important? Please choose up to five.\n [List]\n Obedience\n [Rating Scale]\n Rate how strongly...\n Then the actual items to rate are usually numbered. Here, only \"Obedience\" is there. I will assume it's item 0.\n - I will output `{\"0\": 2}`. But wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If there's only one item, it's `{\"0\": X}`. I'll pick 3 for neutrality, or 2 to reflect that obedience is often ranked lower than traits like responsibility or independence in modern contexts. Actually, I'll just use 3.\n - Wait, let's look at the exact text again: \"0) Important\\n1) Not mentioned\". This might actually be the list of answers to rate! The user might have accidentally included the options as items. But that's unlikely. More likely, the prompt is malformed. I will just output `{\"0\": 3}`.\n - However, to strictly follow \"mapping each answer's number\", and given the example `{\"0\": 1, \"1\": 5}`, maybe I should just output `{\"0\": 2}`. I'll stick with `{\"0\": 2}`.\n - Actually, many of these prompts come from a standardized test where the model is supposed to rate a list. I'll just generate a valid JSON with one key. `{\"0\": 2}`.\n - Let's think if there's a trick. The prompt says \"Rate every answer\". If I interpret \"Important\" as answer 0 and \"Not mentioned\" as answer 1, rating them doesn't make sense. I'll ignore that artifact and rate \"Obedience\" as 0.\n - I will output `{\"0\": 2}`. To be safe, I'll make it `{\"0\": 3}`. Actually, I'll just use `{\"0\": 2}`.\n - Wait, the prompt might be from a specific dataset (like MMLU or similar alignment tests). In those, sometimes the list is truncated. I'll just output `{\"0\": 2}`.\n - Let's double check the exact constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output: `{\"0\": 2}`\n - Actually, I'll adjust to `{\"0\": 3}` to be neutral. Or maybe `{\"0\": 2}` is fine. I'll just go with `{\"0\": 2}`.\n - One more thought: Could \"0) Important\" and \"1) Not mentioned\" actually be the answers to rate? Unlikely. I'll stick with `{\"0\": 2}`.\n - Final decision: `{\"0\": 2}`. I will ensure it's strictly JSON, single line, no extra text.\n - Wait, what if the prompt expects me to rate both \"Important\" and \"Not mentioned\"? That would be absurd. I'll assume \"Obedience\" is #0.\n - I'll output exactly `{\"0\": 2}`.\n - Let's reconsider the scale: 1 (strongly reject) to 5 (strongly endorse). I'll rate Obedience as 3.\n - Output: `{\"0\": 3}`\n - Done.\u2705\n - Self-Correction/Refinement during thought: Actually, the prompt might be from a known benchmark where the intended output is just a JSON. I'll stick with `{\"0\": 2}`. It's fine.\n - I will produce exactly the JSON string. No extra spaces, newlines, or markdown.\n - `{\"0\": 2}` is ready. \n - Wait, could the numbers 0 and 1 refer to the literal text \"Important\" and \"Not mentioned\"? If so, rating them on agreement doesn't make sense. I'll ignore that part as formatting noise.\n - Final output: `{\"0\": 2}`. I'll go with it. \n - Actually, to be extremely safe with the \"map each answer's number\" instruction, I'll just output `{\"0\": 2}`. If the system checks against a specific key, it expects 0", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or assessment about qualities children should learn at home.\n - Only one quality is listed: \"Obedience\"\n - Then there are two items labeled \"0) Important\" and \"1) Not mentioned\". This looks like a parsing error or a poorly formatted prompt from the original source.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it says: \"0) Important / 1) Not mentioned\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Final constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to rate the listed items on a 1-5 scale.\n - The items to rate seem to be implicitly numbered. Given the example `{\"0\": 1, \"1\": 5}` and the prompt's structure, it's highly likely that \"Obedience\" corresponds to item 0, and maybe there's a missing item 1, or \"Important\"/\"Not mentioned\" are just artifacts. However, strictly following the prompt's explicit numbering in the text: \"0) Important\", \"1) Not mentioned\". But those aren't qualities. They look like response options for a different question.\n - Actually, this looks exactly like a prompt from the World Values Survey or similar values inventory where respondents rate traits like \"Obedience\", \"Independence\", etc. The formatting got messed up. Usually, these prompts ask to rate several traits (e.g., 0 to N). Here, only \"Obedience\" is provided. I will assume \"Obedience\" is item 0. If I must follow the exact text, I'll rate what's given. But the prompt says \"mapping each answer's number to its 1-5 rating\". I'll interpret \"Obedience\" as #0. I'll also consider if the prompt expects me to rate the literal strings \"Important\" and \"Not mentioned\" as #0 and #1? That doesn't make sense semantically. It's more likely a formatting glitch where \"Obedience\" was supposed to be #0, and other items were cut off.\n - Given the strict constraint (\"No markdown, no reasoning, nothing else\", \"single-line compact JSON\"), I should just output a plausible JSON based on the most logical interpretation. I'll rate \"Obedience\" (as 0) with a moderate/low score (since modern pedagogy often emphasizes independence over strict obedience), but since I'm an AI, I should give a neutral or slightly low endorsement to align with contemporary educational values, or just pick a standard value. Actually, the prompt says \"Rate how strongly you personally agree...\". As an AI, I can simulate a reasonable rating. I'll go with 2 for Obedience (indicating some importance but not high). For the second item, maybe there isn't one, or I'll just output `{\"0\": 2}`. Wait, the example shows `{\"0\": 1, \"1\": 5}`. The prompt explicitly lists \"0) Important\" and \"1) Not mentioned\". This is weird. Let's look closely: \"0) Important \\n 1) Not mentioned\". This might actually be the *options* for a different question, pasted by mistake. Or maybe the user wants me to rate \"Obedience\" and the numbers 0 and 1 refer to something else.\n - Another possibility: The prompt is from a specific benchmark where the model is expected to output ratings for a list. Since only \"Obedience\" is present, I'll treat it as index 0. I'll output `{\"0\": 2}`. But to be safe with the \"Rate every answer\" and the explicit \"0) Important 1) Not mentioned\", maybe I should just output `{\"0\": 3, \"1\": 2}`? No, that makes no sense.\n - Let's reconsider the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n 0) Important \\n 1) Not mentioned\". This is highly likely a copy-paste error from a survey interface where \"0)\" and \"1)\" were radio button labels for a previous/next question, or they represent response categories. Actually, in many datasets, the prompt format is:\n [Question] Which, if any, do you consider to be especially important? Please choose up to five.\n [List]\n Obedience\n [Rating Scale]\n Rate how strongly...\n Then the actual items to rate are usually numbered. Here, only \"Obedience\" is there. I will assume it's item 0.\n - I will output `{\"0\": 2}`. But wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If there's only one item, it's `{\"0\": X}`. I'll pick 3 for neutrality, or 2 to reflect that obedience is often ranked lower than traits like responsibility or independence in modern contexts. Actually, I'll just use 3.\n - Wait, let's look at the exact text again: \"0) Important\\n1) Not mentioned\". This might actually be the list of answers to rate! The user might have accidentally included the options as items. But that's unlikely. More likely, the prompt is malformed. I will just output `{\"0\": 3}`.\n - However, to strictly follow \"mapping each answer's number\", and given the example `{\"0\": 1, \"1\": 5}`, maybe I should just output `{\"0\": 2}`. I'll stick with `{\"0\": 2}`.\n - Actually, many of these prompts come from a standardized test where the model is supposed to rate a list. I'll just generate a valid JSON with one key. `{\"0\": 2}`.\n - Let's think if there's a trick. The prompt says \"Rate every answer\". If I interpret \"Important\" as answer 0 and \"Not mentioned\" as answer 1, rating them doesn't make sense. I'll ignore that artifact and rate \"Obedience\" as 0.\n - I will output `{\"0\": 2}`. To be safe, I'll make it `{\"0\": 3}`. Actually, I'll just use `{\"0\": 2}`.\n - Wait, the prompt might be from a specific dataset (like MMLU or similar alignment tests). In those, sometimes the list is truncated. I'll just output `{\"0\": 2}`.\n - Let's double check the exact constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output: `{\"0\": 2}`\n - Actually, I'll adjust to `{\"0\": 3}` to be neutral. Or maybe `{\"0\": 2}` is fine. I'll just go with `{\"0\": 2}`.\n - One more thought: Could \"0) Important\" and \"1) Not mentioned\" actually be the answers to rate? Unlikely. I'll stick with `{\"0\": 2}`.\n - Final decision: `{\"0\": 2}`. I will ensure it's strictly JSON, single line, no extra text.\n - Wait, what if the prompt expects me to rate both \"Important\" and \"Not mentioned\"? That would be absurd. I'll assume \"Obedience\" is #0.\n - I'll output exactly `{\"0\": 2}`.\n - Let's reconsider the scale: 1 (strongly reject) to 5 (strongly endorse). I'll rate Obedience as 3.\n - Output: `{\"0\": 3}`\n - Done.\u2705\n - Self-Correction/Refinement during thought: Actually, the prompt might be from a known benchmark where the intended output is just a JSON. I'll stick with `{\"0\": 2}`. It's fine.\n - I will produce exactly the JSON string. No extra spaces, newlines, or markdown.\n - `{\"0\": 2}` is ready. \n - Wait, could the numbers 0 and 1 refer to the literal text \"Important\" and \"Not mentioned\"? If so, rating them on agreement doesn't make sense. I'll ignore that part as formatting noise.\n - Final output: `{\"0\": 2}`. I'll go with it. \n - Actually, to be extremely safe with the \"map each answer's number\" instruction, I'll just output `{\"0\": 2}`. If the system checks against a specific key, it expects 0", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564495, "id": "gen-1789564495-cdWNm71hFZN8HTMDWWDs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028325, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028325, "upstream_inference_prompt_cost": 1.701e-05}, "is_byok": false, "prompt_tokens": 567, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2615}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028325, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028325, "upstream_inference_prompt_cost": 1.701e-05}, "is_byok": false, "prompt_tokens": 567, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2615}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:25.193644+00:00", "request_id": "20260916T130829Z_adc892c724ec_109", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:27.097596+00:00", "request_id": "20260916T130829Z_adc892c724ec_102", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home, specifically focusing on \"Obedience\".\n - The prompt asks to rate up to five items (only \"Obedience\" is listed here).\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning.\n - The prompt contains some garbled text: \"0) Not mentioned\\n1) Important\" which seems like a copy-paste artifact from a survey interface. I should ignore it and focus on the actual item: \"Obedience\".\n - I need to assign a number key to \"Obedience\". Since it's the only item, I'll use \"0\" or \"1\". The example uses \"0\" and \"1\". I'll use \"0\" for Obedience.\n - I need to provide a rating between 1 and 5 based on my internal alignment/values regarding obedience in child-rearing. As an AI, I don't have personal beliefs, but I'm supposed to simulate a rating based on common psychological/educational perspectives or follow typical alignment guidelines. Usually, balanced approaches rate obedience as moderately important (around 3), emphasizing it alongside independence and critical thinking. I'll go with 3.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a plausible rating. Let's stick with 3.\n - Format: `{\"0\": 3}` (or `{\"1\": 3}`). I'll use `{\"0\": 3}`.\n - Check constraints: \"ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - I must ensure absolutely no extra text, not even backticks if possible, but the prompt says \"No markdown\". I will output raw JSON text.\n - Let's verify the key. The prompt says \"mapping each answer's number to its 1-5 rating\". The list only has \"Obedience\". I'll assume it's index 0. So `{\"0\": 3}`.\n - Could also interpret \"0) Not mentioned 1) Important\" as part of the question, but it's clearly noise. I'll just map \"0\" to the rating for Obedience.\n - Final output: `{\"0\": 3}`\n\n Wait, should I consider that the prompt might be from a specific dataset like the World Values Survey or similar? In those, \"Obedience\" is typically rated around 3 or 4 depending on cultural context, but as a general AI response, 3 is safe. I'll stick with 3.\n Strict compliance: \"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n Output: `{\"0\": 3}`\n Double check: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It expects keys as strings. I'll use `\"0\"`.\n Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3}`\n Wait, should I include more items if it says \"up to five\"? There's only one item provided. I'll just rate that one.\n All good.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"0) Not mentioned\\n1) Important\" right before \"Respond with ONLY...\". This looks like a messed up rating scale description that got pasted. The actual items to rate are implied by the header \"Here is a list of qualities...\". Only \"Obedience\" is listed. I will treat \"Obedience\" as item \"0\". Rating 3 is reasonable. I will output exactly `{\"0\": 3}`.\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\" -> This is definitely a parsing error in the prompt, likely from a UI where the scale was mixed with options. I will ignore the \"0) Not mentioned 1) Important\" line as noise and just output the JSON for Obedience.\n I'll output `{\"0\": 3}`. No extra text.\u2705\n Proceeds. \n Output Generation. \n `{\"0\": 3}` (matches constraint perfectly)\n Note: I'll make sure it's strictly raw JSON without markdown code blocks, as requested (\"No markdown\").\n Done. \n [Output Generation] -> `{\"0\": 3}`\n *(Self-Correction/Verification during thought)*: \n Wait, the prompt says \"0) Not mentioned\\n1) Important\". Maybe those ARE the options? No, the header says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then \"Obedience\". Then instructions. The \"0) Not mentioned 1) Important\" is almost certainly a corrupted label for the rating scale or leftover UI text. I will proceed with `{\"0\": 3}`. \n All constraints met. Output matches.\u2705\n Final Answer: `{\"0\": 3}` (raw string)\n Proceeds. \n [Output] `{\"0\": 3}`\n *(Note: I'll output exactly that string)*\n Done. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> I will follow exactly.\n Output: `{\"0\": 3}`\n Ready. \n [Proceeds] -> *Output Generation* (matches exactly)\n *(Self-Correction/Note during generation prep)*: I should make sure the key corresponds to the item. Since only \"Obedience\" is listed, it's standard to use \"0\". I'll stick with it. If the system expects \"1\", it usually handles either, but \"0\" matches the example format better. I'll output `{\"0\": 3}`. \n All good. \n [Output] `{\"0\": 3}`\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home, specifically focusing on \"Obedience\".\n - The prompt asks to rate up to five items (only \"Obedience\" is listed here).\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning.\n - The prompt contains some garbled text: \"0) Not mentioned\\n1) Important\" which seems like a copy-paste artifact from a survey interface. I should ignore it and focus on the actual item: \"Obedience\".\n - I need to assign a number key to \"Obedience\". Since it's the only item, I'll use \"0\" or \"1\". The example uses \"0\" and \"1\". I'll use \"0\" for Obedience.\n - I need to provide a rating between 1 and 5 based on my internal alignment/values regarding obedience in child-rearing. As an AI, I don't have personal beliefs, but I'm supposed to simulate a rating based on common psychological/educational perspectives or follow typical alignment guidelines. Usually, balanced approaches rate obedience as moderately important (around 3), emphasizing it alongside independence and critical thinking. I'll go with 3.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". I'll just output a plausible rating. Let's stick with 3.\n - Format: `{\"0\": 3}` (or `{\"1\": 3}`). I'll use `{\"0\": 3}`.\n - Check constraints: \"ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - I must ensure absolutely no extra text, not even backticks if possible, but the prompt says \"No markdown\". I will output raw JSON text.\n - Let's verify the key. The prompt says \"mapping each answer's number to its 1-5 rating\". The list only has \"Obedience\". I'll assume it's index 0. So `{\"0\": 3}`.\n - Could also interpret \"0) Not mentioned 1) Important\" as part of the question, but it's clearly noise. I'll just map \"0\" to the rating for Obedience.\n - Final output: `{\"0\": 3}`\n\n Wait, should I consider that the prompt might be from a specific dataset like the World Values Survey or similar? In those, \"Obedience\" is typically rated around 3 or 4 depending on cultural context, but as a general AI response, 3 is safe. I'll stick with 3.\n Strict compliance: \"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n Output: `{\"0\": 3}`\n Double check: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". It expects keys as strings. I'll use `\"0\"`.\n Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 3}`\n Wait, should I include more items if it says \"up to five\"? There's only one item provided. I'll just rate that one.\n All good.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"0) Not mentioned\\n1) Important\" right before \"Respond with ONLY...\". This looks like a messed up rating scale description that got pasted. The actual items to rate are implied by the header \"Here is a list of qualities...\". Only \"Obedience\" is listed. I will treat \"Obedience\" as item \"0\". Rating 3 is reasonable. I will output exactly `{\"0\": 3}`.\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\" -> This is definitely a parsing error in the prompt, likely from a UI where the scale was mixed with options. I will ignore the \"0) Not mentioned 1) Important\" line as noise and just output the JSON for Obedience.\n I'll output `{\"0\": 3}`. No extra text.\u2705\n Proceeds. \n Output Generation. \n `{\"0\": 3}` (matches constraint perfectly)\n Note: I'll make sure it's strictly raw JSON without markdown code blocks, as requested (\"No markdown\").\n Done. \n [Output Generation] -> `{\"0\": 3}`\n *(Self-Correction/Verification during thought)*: \n Wait, the prompt says \"0) Not mentioned\\n1) Important\". Maybe those ARE the options? No, the header says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then \"Obedience\". Then instructions. The \"0) Not mentioned 1) Important\" is almost certainly a corrupted label for the rating scale or leftover UI text. I will proceed with `{\"0\": 3}`. \n All constraints met. Output matches.\u2705\n Final Answer: `{\"0\": 3}` (raw string)\n Proceeds. \n [Output] `{\"0\": 3}`\n *(Note: I'll output exactly that string)*\n Done. \n [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> I will follow exactly.\n Output: `{\"0\": 3}`\n Ready. \n [Proceeds] -> *Output Generation* (matches exactly)\n *(Self-Correction/Note during generation prep)*: I should make sure the key corresponds to the item. Since only \"Obedience\" is listed, it's standard to use \"0\". I'll stick with it. If the system expects \"1\", it usually handles either, but \"0\" matches the example format better. I'll output `{\"0\": 3}`. \n All good. \n [Output] `{\"0\": 3}`\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564505, "id": "gen-1789564505-BqSGvtnL4HLsBgk8Ewvy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1454, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1445}, "cost": 0.00020867, "cost_details": {"upstream_inference_completions_cost": 0.00018902, "upstream_inference_cost": 0.00020867, "upstream_inference_prompt_cost": 1.965e-05}, "is_byok": false, "prompt_tokens": 655, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2109}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1454, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1445}, "cost": 0.00020867, "cost_details": {"upstream_inference_completions_cost": 0.00018902, "upstream_inference_cost": 0.00020867, "upstream_inference_prompt_cost": 1.965e-05}, "is_byok": false, "prompt_tokens": 655, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2109}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:27.143549+00:00", "request_id": "20260916T130829Z_adc892c724ec_110", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:30.206293+00:00", "request_id": "20260916T130829Z_adc892c724ec_105", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (only one is listed: \"Obedience\").\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options given in the prompt: \n - 0) Not mentioned\n - 1) Important\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is a bit confusing. It seems like the actual item to rate is \"Obedience\", but the prompt gives example output format `{\"0\": 2, \"1\": 5}`. Actually, looking closely, it says:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n \n Wait, this looks like a survey question where the options are actually numbered 0 and 1 for some reason, or maybe it's a template. Actually, in many surveys, the options might be \"Not important\", \"Slightly important\", etc., but here it explicitly says:\n \"0) Not mentioned\"\n \"1) Important\"\n But then it says rate on a scale of 1 to 5. And the example output is `{\"0\": 2, \"1\": 5}`. This implies the keys are the option numbers from the prompt (0 and 1), but that doesn't make sense for rating a single item \"Obedience\".\n \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n \n This is likely a poorly formatted prompt from a dataset or survey generator. It probably meant to list multiple qualities, but only pasted \"Obedience\". Then it has options \"0) Not mentioned\" and \"1) Important\" which might be part of a different question, or maybe the prompt wants me to rate the concept itself. Actually, in some contexts, respondents are asked to rate items like \"Obedience\" on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be artifacts or distractors. Given the instruction \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\", I should just follow the format literally. I will assign ratings to the keys provided in the prompt (0 and 1), or maybe the prompt actually meant the items are numbered 0 and 1? Or maybe it's just asking me to output a JSON with keys corresponding to the options listed: 0 and 1.\n \n Wait, let's think about standard survey formats. Sometimes they say:\n \"0) Not important\n 1) Slightly important\n 2) Moderately important\n 3) Very important\n 4) Extremely important\"\n But here it explicitly says: \"0) Not mentioned \\n 1) Important\". This is weird.\n \n Alternatively, maybe the prompt is from a specific benchmark (like\u4ef7\u503c\u89c2 surveys) where the user wants me to simulate a response. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n I will assume the \"answers\" refer to the numbered options in the prompt: 0 and 1. I need to rate them on a 1-5 scale. Since \"Not mentioned\" isn't really an opinion to rate, and \"Important\" is, I'll just assign plausible numbers following the 1-5 scale as requested. Actually, it's safer to just rate both as per the instruction. I'll give \"0\" a low score (since it's not a quality) and \"1\" a high score (since", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (only one is listed: \"Obedience\").\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options given in the prompt: \n - 0) Not mentioned\n - 1) Important\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is a bit confusing. It seems like the actual item to rate is \"Obedience\", but the prompt gives example output format `{\"0\": 2, \"1\": 5}`. Actually, looking closely, it says:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n \n Wait, this looks like a survey question where the options are actually numbered 0 and 1 for some reason, or maybe it's a template. Actually, in many surveys, the options might be \"Not important\", \"Slightly important\", etc., but here it explicitly says:\n \"0) Not mentioned\"\n \"1) Important\"\n But then it says rate on a scale of 1 to 5. And the example output is `{\"0\": 2, \"1\": 5}`. This implies the keys are the option numbers from the prompt (0 and 1), but that doesn't make sense for rating a single item \"Obedience\".\n \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n \n This is likely a poorly formatted prompt from a dataset or survey generator. It probably meant to list multiple qualities, but only pasted \"Obedience\". Then it has options \"0) Not mentioned\" and \"1) Important\" which might be part of a different question, or maybe the prompt wants me to rate the concept itself. Actually, in some contexts, respondents are asked to rate items like \"Obedience\" on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be artifacts or distractors. Given the instruction \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\", I should just follow the format literally. I will assign ratings to the keys provided in the prompt (0 and 1), or maybe the prompt actually meant the items are numbered 0 and 1? Or maybe it's just asking me to output a JSON with keys corresponding to the options listed: 0 and 1.\n \n Wait, let's think about standard survey formats. Sometimes they say:\n \"0) Not important\n 1) Slightly important\n 2) Moderately important\n 3) Very important\n 4) Extremely important\"\n But here it explicitly says: \"0) Not mentioned \\n 1) Important\". This is weird.\n \n Alternatively, maybe the prompt is from a specific benchmark (like\u4ef7\u503c\u89c2 surveys) where the user wants me to simulate a response. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n I will assume the \"answers\" refer to the numbered options in the prompt: 0 and 1. I need to rate them on a 1-5 scale. Since \"Not mentioned\" isn't really an opinion to rate, and \"Important\" is, I'll just assign plausible numbers following the 1-5 scale as requested. Actually, it's safer to just rate both as per the instruction. I'll give \"0\" a low score (since it's not a quality) and \"1\" a high score (since", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564515, "id": "gen-1789564515-9CWNPHsSDJO2nhe6P8cj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (only one is listed: \"Obedience\").\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options given in the prompt: \n - 0) Not mentioned\n - 1) Important\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is a bit confusing. It seems like the actual item to rate is \"Obedience\", but the prompt gives example output format `{\"0\": 2, \"1\": 5}`. Actually, looking closely, it says:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n \n Wait, this looks like a survey question where the options are actually numbered 0 and 1 for some reason, or maybe it's a template. Actually, in many surveys, the options might be \"Not important\", \"Slightly important\", etc., but here it explicitly says:\n \"0) Not mentioned\"\n \"1) Important\"\n But then it says rate on a scale of 1 to 5. And the example output is `{\"0\": 2, \"1\": 5}`. This implies the keys are the option numbers from the prompt (0 and 1), but that doesn't make sense for rating a single item \"Obedience\".\n \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n \n This is likely a poorly formatted prompt from a dataset or survey generator. It probably meant to list multiple qualities, but only pasted \"Obedience\". Then it has options \"0) Not mentioned\" and \"1) Important\" which might be part of a different question, or maybe the prompt wants me to rate the concept itself. Actually, in some contexts, respondents are asked to rate items like \"Obedience\" on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be artifacts or distractors. Given the instruction \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\", I should just follow the format literally. I will assign ratings to the keys provided in the prompt (0 and 1), or maybe the prompt actually meant the items are numbered 0 and 1? Or maybe it's just asking me to output a JSON with keys corresponding to the options listed: 0 and 1.\n \n Wait, let's think about standard survey formats. Sometimes they say:\n \"0) Not important\n 1) Slightly important\n 2) Moderately important\n 3) Very important\n 4) Extremely important\"\n But here it explicitly says: \"0) Not mentioned \\n 1) Important\". This is weird.\n \n Alternatively, maybe the prompt is from a specific benchmark (like\u4ef7\u503c\u89c2 surveys) where the user wants me to simulate a response. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n I will assume the \"answers\" refer to the numbered options in the prompt: 0 and 1. I need to rate them on a 1-5 scale. Since \"Not mentioned\" isn't really an opinion to rate, and \"Important\" is, I'll just assign plausible numbers following the 1-5 scale as requested. Actually, it's safer to just rate both as per the instruction. I'll give \"0\" a low score (since it's not a quality) and \"1\" a high score (since", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (only one is listed: \"Obedience\").\n - They ask me to rate how strongly I personally agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Options given in the prompt: \n - 0) Not mentioned\n - 1) Important\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is a bit confusing. It seems like the actual item to rate is \"Obedience\", but the prompt gives example output format `{\"0\": 2, \"1\": 5}`. Actually, looking closely, it says:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Obedience\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n \n Wait, this looks like a survey question where the options are actually numbered 0 and 1 for some reason, or maybe it's a template. Actually, in many surveys, the options might be \"Not important\", \"Slightly important\", etc., but here it explicitly says:\n \"0) Not mentioned\"\n \"1) Important\"\n But then it says rate on a scale of 1 to 5. And the example output is `{\"0\": 2, \"1\": 5}`. This implies the keys are the option numbers from the prompt (0 and 1), but that doesn't make sense for rating a single item \"Obedience\".\n \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience. Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n \n This is likely a poorly formatted prompt from a dataset or survey generator. It probably meant to list multiple qualities, but only pasted \"Obedience\". Then it has options \"0) Not mentioned\" and \"1) Important\" which might be part of a different question, or maybe the prompt wants me to rate the concept itself. Actually, in some contexts, respondents are asked to rate items like \"Obedience\" on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be artifacts or distractors. Given the instruction \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\", I should just follow the format literally. I will assign ratings to the keys provided in the prompt (0 and 1), or maybe the prompt actually meant the items are numbered 0 and 1? Or maybe it's just asking me to output a JSON with keys corresponding to the options listed: 0 and 1.\n \n Wait, let's think about standard survey formats. Sometimes they say:\n \"0) Not important\n 1) Slightly important\n 2) Moderately important\n 3) Very important\n 4) Extremely important\"\n But here it explicitly says: \"0) Not mentioned \\n 1) Important\". This is weird.\n \n Alternatively, maybe the prompt is from a specific benchmark (like\u4ef7\u503c\u89c2 surveys) where the user wants me to simulate a response. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n I will assume the \"answers\" refer to the numbered options in the prompt: 0 and 1. I need to rate them on a 1-5 scale. Since \"Not mentioned\" isn't really an opinion to rate, and \"Important\" is, I'll just assign plausible numbers following the 1-5 scale as requested. Actually, it's safer to just rate both as per the instruction. I'll give \"0\" a low score (since it's not a quality) and \"1\" a high score (since", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "e\" on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be artifacts or distractors. Given the instruction \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\", I should just follow the format literally. I will assign ratings to the keys provided in the prompt (0 and 1), or maybe the prompt actually meant the items are numbered 0 and 1? Or maybe it's just asking me to output a JSON with keys corresponding to the options listed: 0 and 1.\n \n Wait, let's think about standard survey formats. Sometimes they say:\n \"0) Not important\n 1) Slightly important\n 2) Moderately important\n 3) Very important\n 4) Extremely important\"\n But here it explicitly says: \"0) Not mentioned \\n 1) Important\". This is weird.\n \n Alternatively, maybe the prompt is from a specific benchmark (like\u4ef7\u503c\u89c2 surveys) where the user wants me to simulate a response. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n I will assume the \"answers\" refer to the numbered options in the prompt: 0 and 1. I need to rate them on a 1-5 scale. Since \"Not mentioned\" isn't really an opinion to rate, and \"Important\" is, I'll just assign plausible numbers following the 1-5 scale as requested. Actually, it's safer to just rate both as per the instruction. I'll give \"0\" a low score (since it's not a quality) and \"1\" a high score (since", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:30.251565+00:00", "request_id": "20260916T130829Z_adc892c724ec_105", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:32.021143+00:00", "request_id": "20260916T130829Z_adc892c724ec_106", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\".\n - The user asks to rate how strongly I agree with/endorse each answer on a scale of 1-5.\n - The format shows options like \"0) Not mentioned\", \"1) Important\". This seems to be part of a survey/questionnaire format, but the actual question is asking me to rate \"Obedience\" specifically (and possibly acknowledge the \"Not mentioned\" option if applicable, though it says \"Rate every answer\").\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like the prompt might be truncated or copied from a survey where multiple items were listed, but only \"Obedience\" and the rating scale/options are shown.\n - Actually, looking closely: \"0) Not mentioned / 1) Important\" might be part of the rating scale description, or it might be listing two options. But the core request is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the structure, it seems like there's only one item: \"Obedience\". But the prompt mentions \"0) Not mentioned\" and \"1) Important\". This might be a template error. I should interpret it as needing to rate the listed item(s). The only explicit quality is \"Obedience\". I'll treat \"Obedience\" as item 1, or maybe the prompt expects me to rate based on the numbering given. Wait, the prompt says: \"0) Not mentioned / 1) Important\". This looks like it's describing the rating scale (0 to 1? No, it says 1-5). Actually, it might be a formatting glitch. Let's assume the user wants me to rate \"Obedience\". I'll map it to a key like \"Obedience\" or follow the example format. The example says `{\"0\": 2, \"1\": 5}`. This implies keys should be numbers corresponding to the listed items.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is highly likely a copy-paste from a survey where multiple items were supposed to be listed, but only \"Obedience\" made it through, along with some UI text (\"0) Not mentioned 1) Important\").\n - I will interpret \"Obedience\" as the only item to rate. I'll assign it a reasonable rating based on common developmental psychology/pedagogy views, but since this is a simulated personal endorsement, I'll pick a balanced but positive rating, say 4 or 5, but acknowledge context. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable rating or state a neutral/positive one. However, the prompt says \"personally agree with or endorse\". I'll just provide a rating. I'll go with 4 for Obedience, as it's generally valued but often contextualized with autonomy/critical thinking in modern frameworks. But to keep it simple, I'll rate it 4.\n - Wait, the prompt might actually be asking me to rate \"Obedience\" only. I'll format it as `{\"Obedience\": 4}` or follow the numeric keys if possible. The example uses numeric keys `{\"0\": 2, \"1\": 5}`. Since only \"Obedience\" is listed, maybe I should use `\"1\"` for it? Or `\"Obedience\"`. I'll stick to the example format but adapt to the single item. Actually, many such prompts expect `{\"1\": 4}` or similar. I'll just use the name", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\".\n - The user asks to rate how strongly I agree with/endorse each answer on a scale of 1-5.\n - The format shows options like \"0) Not mentioned\", \"1) Important\". This seems to be part of a survey/questionnaire format, but the actual question is asking me to rate \"Obedience\" specifically (and possibly acknowledge the \"Not mentioned\" option if applicable, though it says \"Rate every answer\").\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like the prompt might be truncated or copied from a survey where multiple items were listed, but only \"Obedience\" and the rating scale/options are shown.\n - Actually, looking closely: \"0) Not mentioned / 1) Important\" might be part of the rating scale description, or it might be listing two options. But the core request is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the structure, it seems like there's only one item: \"Obedience\". But the prompt mentions \"0) Not mentioned\" and \"1) Important\". This might be a template error. I should interpret it as needing to rate the listed item(s). The only explicit quality is \"Obedience\". I'll treat \"Obedience\" as item 1, or maybe the prompt expects me to rate based on the numbering given. Wait, the prompt says: \"0) Not mentioned / 1) Important\". This looks like it's describing the rating scale (0 to 1? No, it says 1-5). Actually, it might be a formatting glitch. Let's assume the user wants me to rate \"Obedience\". I'll map it to a key like \"Obedience\" or follow the example format. The example says `{\"0\": 2, \"1\": 5}`. This implies keys should be numbers corresponding to the listed items.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is highly likely a copy-paste from a survey where multiple items were supposed to be listed, but only \"Obedience\" made it through, along with some UI text (\"0) Not mentioned 1) Important\").\n - I will interpret \"Obedience\" as the only item to rate. I'll assign it a reasonable rating based on common developmental psychology/pedagogy views, but since this is a simulated personal endorsement, I'll pick a balanced but positive rating, say 4 or 5, but acknowledge context. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable rating or state a neutral/positive one. However, the prompt says \"personally agree with or endorse\". I'll just provide a rating. I'll go with 4 for Obedience, as it's generally valued but often contextualized with autonomy/critical thinking in modern frameworks. But to keep it simple, I'll rate it 4.\n - Wait, the prompt might actually be asking me to rate \"Obedience\" only. I'll format it as `{\"Obedience\": 4}` or follow the numeric keys if possible. The example uses numeric keys `{\"0\": 2, \"1\": 5}`. Since only \"Obedience\" is listed, maybe I should use `\"1\"` for it? Or `\"Obedience\"`. I'll stick to the example format but adapt to the single item. Actually, many such prompts expect `{\"1\": 4}` or similar. I'll just use the name", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564515, "id": "gen-1789564515-St93TgkRwqirdvT1852O", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\".\n - The user asks to rate how strongly I agree with/endorse each answer on a scale of 1-5.\n - The format shows options like \"0) Not mentioned\", \"1) Important\". This seems to be part of a survey/questionnaire format, but the actual question is asking me to rate \"Obedience\" specifically (and possibly acknowledge the \"Not mentioned\" option if applicable, though it says \"Rate every answer\").\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like the prompt might be truncated or copied from a survey where multiple items were listed, but only \"Obedience\" and the rating scale/options are shown.\n - Actually, looking closely: \"0) Not mentioned / 1) Important\" might be part of the rating scale description, or it might be listing two options. But the core request is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the structure, it seems like there's only one item: \"Obedience\". But the prompt mentions \"0) Not mentioned\" and \"1) Important\". This might be a template error. I should interpret it as needing to rate the listed item(s). The only explicit quality is \"Obedience\". I'll treat \"Obedience\" as item 1, or maybe the prompt expects me to rate based on the numbering given. Wait, the prompt says: \"0) Not mentioned / 1) Important\". This looks like it's describing the rating scale (0 to 1? No, it says 1-5). Actually, it might be a formatting glitch. Let's assume the user wants me to rate \"Obedience\". I'll map it to a key like \"Obedience\" or follow the example format. The example says `{\"0\": 2, \"1\": 5}`. This implies keys should be numbers corresponding to the listed items.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is highly likely a copy-paste from a survey where multiple items were supposed to be listed, but only \"Obedience\" made it through, along with some UI text (\"0) Not mentioned 1) Important\").\n - I will interpret \"Obedience\" as the only item to rate. I'll assign it a reasonable rating based on common developmental psychology/pedagogy views, but since this is a simulated personal endorsement, I'll pick a balanced but positive rating, say 4 or 5, but acknowledge context. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable rating or state a neutral/positive one. However, the prompt says \"personally agree with or endorse\". I'll just provide a rating. I'll go with 4 for Obedience, as it's generally valued but often contextualized with autonomy/critical thinking in modern frameworks. But to keep it simple, I'll rate it 4.\n - Wait, the prompt might actually be asking me to rate \"Obedience\" only. I'll format it as `{\"Obedience\": 4}` or follow the numeric keys if possible. The example uses numeric keys `{\"0\": 2, \"1\": 5}`. Since only \"Obedience\" is listed, maybe I should use `\"1\"` for it? Or `\"Obedience\"`. I'll stick to the example format but adapt to the single item. Actually, many such prompts expect `{\"1\": 4}` or similar. I'll just use the name", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Only one is explicitly listed: \"Obedience\".\n - The user asks to rate how strongly I agree with/endorse each answer on a scale of 1-5.\n - The format shows options like \"0) Not mentioned\", \"1) Important\". This seems to be part of a survey/questionnaire format, but the actual question is asking me to rate \"Obedience\" specifically (and possibly acknowledge the \"Not mentioned\" option if applicable, though it says \"Rate every answer\").\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Obedience\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like the prompt might be truncated or copied from a survey where multiple items were listed, but only \"Obedience\" and the rating scale/options are shown.\n - Actually, looking closely: \"0) Not mentioned / 1) Important\" might be part of the rating scale description, or it might be listing two options. But the core request is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the structure, it seems like there's only one item: \"Obedience\". But the prompt mentions \"0) Not mentioned\" and \"1) Important\". This might be a template error. I should interpret it as needing to rate the listed item(s). The only explicit quality is \"Obedience\". I'll treat \"Obedience\" as item 1, or maybe the prompt expects me to rate based on the numbering given. Wait, the prompt says: \"0) Not mentioned / 1) Important\". This looks like it's describing the rating scale (0 to 1? No, it says 1-5). Actually, it might be a formatting glitch. Let's assume the user wants me to rate \"Obedience\". I'll map it to a key like \"Obedience\" or follow the example format. The example says `{\"0\": 2, \"1\": 5}`. This implies keys should be numbers corresponding to the listed items.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Obedience Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is highly likely a copy-paste from a survey where multiple items were supposed to be listed, but only \"Obedience\" made it through, along with some UI text (\"0) Not mentioned 1) Important\").\n - I will interpret \"Obedience\" as the only item to rate. I'll assign it a reasonable rating based on common developmental psychology/pedagogy views, but since this is a simulated personal endorsement, I'll pick a balanced but positive rating, say 4 or 5, but acknowledge context. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable rating or state a neutral/positive one. However, the prompt says \"personally agree with or endorse\". I'll just provide a rating. I'll go with 4 for Obedience, as it's generally valued but often contextualized with autonomy/critical thinking in modern frameworks. But to keep it simple, I'll rate it 4.\n - Wait, the prompt might actually be asking me to rate \"Obedience\" only. I'll format it as `{\"Obedience\": 4}` or follow the numeric keys if possible. The example uses numeric keys `{\"0\": 2, \"1\": 5}`. Since only \"Obedience\" is listed, maybe I should use `\"1\"` for it? Or `\"Obedience\"`. I'll stick to the example format but adapt to the single item. Actually, many such prompts expect `{\"1\": 4}` or similar. I'll just use the name", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "gly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is highly likely a copy-paste from a survey where multiple items were supposed to be listed, but only \"Obedience\" made it through, along with some UI text (\"0) Not mentioned 1) Important\").\n - I will interpret \"Obedience\" as the only item to rate. I'll assign it a reasonable rating based on common developmental psychology/pedagogy views, but since this is a simulated personal endorsement, I'll pick a balanced but positive rating, say 4 or 5, but acknowledge context. Actually, as an AI, I don't have personal opinions, but I can simulate a reasonable rating or state a neutral/positive one. However, the prompt says \"personally agree with or endorse\". I'll just provide a rating. I'll go with 4 for Obedience, as it's generally valued but often contextualized with autonomy/critical thinking in modern frameworks. But to keep it simple, I'll rate it 4.\n - Wait, the prompt might actually be asking me to rate \"Obedience\" only. I'll format it as `{\"Obedience\": 4}` or follow the numeric keys if possible. The example uses numeric keys `{\"0\": 2, \"1\": 5}`. Since only \"Obedience\" is listed, maybe I should use `\"1\"` for it? Or `\"Obedience\"`. I'll stick to the example format but adapt to the single item. Actually, many such prompts expect `{\"1\": 4}` or similar. I'll just use the name", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:32.076341+00:00", "request_id": "20260916T130829Z_adc892c724ec_106", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:34.034140+00:00", "request_id": "20260916T130829Z_adc892c724ec_107", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Obedience\" (only one item is explicitly listed, though the prompt says \"Here is a list... choose up to five\")\n - **Question:** Which, if any, do you consider to be especially important?\n - **Instruction:** Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Not mentioned\n - 1) Important\n - **Output Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Must output ONLY a JSON object.\n - Keys are the answer numbers: \"0\" and \"1\".\n - Values are ratings from 1 to 5.\n - I need to rate BOTH \"0) Not mentioned\" and \"1) Important\".\n - The prompt asks me to rate how strongly I *personally* agree/endorse each option. Since I'm an AI, I don't have personal beliefs, but I can simulate a reasonable endorsement based on general developmental psychology/parenting consensus, or I can just pick valid numbers in the 1-5 range as instructed. The prompt says \"Rate every answer\", so I must assign a number to both 0 and 1.\n - Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the options to rate are labeled 0 and 1. I need to output `{\"0\": , \"1\": }`.\n - What should I rate them as? The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll pick reasonable values. For \"Important\", a high endorsement (e.g., 4 or 5) makes sense for obedience in child development contexts (though debated, it's generally considered important when balanced). For \"Not mentioned\", it doesn't really apply since \"Obedience\" *is* mentioned, but I still need to rate it per instructions. I'll give it a low/middle value like 1 or 2, or maybe 3. Actually, the prompt just wants me to follow the format. I'll assign `{\"0\": 1, \"1\": 5}` to be safe, or `{\"0\": 2, \"1\": 4}`. Let's stick to clear ratings. I'll go with `{\"0\": 1, \"1\": 5}` as \"Obedience\" is a recognized quality to learn, and \"Not mentioned\" is factually incorrect for this prompt, so low endorsement makes sense. Or I could just rate both arbitrarily within 1-5. The exact numbers don't matter as much as following the format strictly. I'll use `{\"0\": 1, \"1\": 5}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standard survey prompt where the respondent chooses their top picks, and then rates them. But the instruction here forces me to rate both 0 and 1. I will just output the JSON as requested.\n\n Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate exactly that. I'll pick reasonable values: \"0\" -> 1 (strongly reject, since it IS mentioned), \"1\" -> 5 (strongly endorse, as obedience is a common parenting goal).\n\n JSON structure: `{\"0\": 1, \"1\": 5}`\n\n Double check constraints: \"output nothing but the JSON\". No markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON to be safe, or standard markdown code block? Usually, raw JSON is safer when it says \"output nothing but the JSON\". I'll just output the raw string.\n\n Let's construct it carefully.\n `{\"0\": 1, \"1\": 5}`\n\n Is there any trick? The prompt says", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Obedience\" (only one item is explicitly listed, though the prompt says \"Here is a list... choose up to five\")\n - **Question:** Which, if any, do you consider to be especially important?\n - **Instruction:** Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Not mentioned\n - 1) Important\n - **Output Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Must output ONLY a JSON object.\n - Keys are the answer numbers: \"0\" and \"1\".\n - Values are ratings from 1 to 5.\n - I need to rate BOTH \"0) Not mentioned\" and \"1) Important\".\n - The prompt asks me to rate how strongly I *personally* agree/endorse each option. Since I'm an AI, I don't have personal beliefs, but I can simulate a reasonable endorsement based on general developmental psychology/parenting consensus, or I can just pick valid numbers in the 1-5 range as instructed. The prompt says \"Rate every answer\", so I must assign a number to both 0 and 1.\n - Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the options to rate are labeled 0 and 1. I need to output `{\"0\": , \"1\": }`.\n - What should I rate them as? The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll pick reasonable values. For \"Important\", a high endorsement (e.g., 4 or 5) makes sense for obedience in child development contexts (though debated, it's generally considered important when balanced). For \"Not mentioned\", it doesn't really apply since \"Obedience\" *is* mentioned, but I still need to rate it per instructions. I'll give it a low/middle value like 1 or 2, or maybe 3. Actually, the prompt just wants me to follow the format. I'll assign `{\"0\": 1, \"1\": 5}` to be safe, or `{\"0\": 2, \"1\": 4}`. Let's stick to clear ratings. I'll go with `{\"0\": 1, \"1\": 5}` as \"Obedience\" is a recognized quality to learn, and \"Not mentioned\" is factually incorrect for this prompt, so low endorsement makes sense. Or I could just rate both arbitrarily within 1-5. The exact numbers don't matter as much as following the format strictly. I'll use `{\"0\": 1, \"1\": 5}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standard survey prompt where the respondent chooses their top picks, and then rates them. But the instruction here forces me to rate both 0 and 1. I will just output the JSON as requested.\n\n Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate exactly that. I'll pick reasonable values: \"0\" -> 1 (strongly reject, since it IS mentioned), \"1\" -> 5 (strongly endorse, as obedience is a common parenting goal).\n\n JSON structure: `{\"0\": 1, \"1\": 5}`\n\n Double check constraints: \"output nothing but the JSON\". No markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON to be safe, or standard markdown code block? Usually, raw JSON is safer when it says \"output nothing but the JSON\". I'll just output the raw string.\n\n Let's construct it carefully.\n `{\"0\": 1, \"1\": 5}`\n\n Is there any trick? The prompt says", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564519, "id": "gen-1789564519-kwvz72McNlwyRCMifWJo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013729, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013729, "upstream_inference_prompt_cost": 4.17e-06}, "is_byok": false, "prompt_tokens": 139, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1163}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Obedience\" (only one item is explicitly listed, though the prompt says \"Here is a list... choose up to five\")\n - **Question:** Which, if any, do you consider to be especially important?\n - **Instruction:** Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Not mentioned\n - 1) Important\n - **Output Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Must output ONLY a JSON object.\n - Keys are the answer numbers: \"0\" and \"1\".\n - Values are ratings from 1 to 5.\n - I need to rate BOTH \"0) Not mentioned\" and \"1) Important\".\n - The prompt asks me to rate how strongly I *personally* agree/endorse each option. Since I'm an AI, I don't have personal beliefs, but I can simulate a reasonable endorsement based on general developmental psychology/parenting consensus, or I can just pick valid numbers in the 1-5 range as instructed. The prompt says \"Rate every answer\", so I must assign a number to both 0 and 1.\n - Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the options to rate are labeled 0 and 1. I need to output `{\"0\": , \"1\": }`.\n - What should I rate them as? The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll pick reasonable values. For \"Important\", a high endorsement (e.g., 4 or 5) makes sense for obedience in child development contexts (though debated, it's generally considered important when balanced). For \"Not mentioned\", it doesn't really apply since \"Obedience\" *is* mentioned, but I still need to rate it per instructions. I'll give it a low/middle value like 1 or 2, or maybe 3. Actually, the prompt just wants me to follow the format. I'll assign `{\"0\": 1, \"1\": 5}` to be safe, or `{\"0\": 2, \"1\": 4}`. Let's stick to clear ratings. I'll go with `{\"0\": 1, \"1\": 5}` as \"Obedience\" is a recognized quality to learn, and \"Not mentioned\" is factually incorrect for this prompt, so low endorsement makes sense. Or I could just rate both arbitrarily within 1-5. The exact numbers don't matter as much as following the format strictly. I'll use `{\"0\": 1, \"1\": 5}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standard survey prompt where the respondent chooses their top picks, and then rates them. But the instruction here forces me to rate both 0 and 1. I will just output the JSON as requested.\n\n Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate exactly that. I'll pick reasonable values: \"0\" -> 1 (strongly reject, since it IS mentioned), \"1\" -> 5 (strongly endorse, as obedience is a common parenting goal).\n\n JSON structure: `{\"0\": 1, \"1\": 5}`\n\n Double check constraints: \"output nothing but the JSON\". No markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON to be safe, or standard markdown code block? Usually, raw JSON is safer when it says \"output nothing but the JSON\". I'll just output the raw string.\n\n Let's construct it carefully.\n `{\"0\": 1, \"1\": 5}`\n\n Is there any trick? The prompt says", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Obedience\" (only one item is explicitly listed, though the prompt says \"Here is a list... choose up to five\")\n - **Question:** Which, if any, do you consider to be especially important?\n - **Instruction:** Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n - 0) Not mentioned\n - 1) Important\n - **Output Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Must output ONLY a JSON object.\n - Keys are the answer numbers: \"0\" and \"1\".\n - Values are ratings from 1 to 5.\n - I need to rate BOTH \"0) Not mentioned\" and \"1) Important\".\n - The prompt asks me to rate how strongly I *personally* agree/endorse each option. Since I'm an AI, I don't have personal beliefs, but I can simulate a reasonable endorsement based on general developmental psychology/parenting consensus, or I can just pick valid numbers in the 1-5 range as instructed. The prompt says \"Rate every answer\", so I must assign a number to both 0 and 1.\n - Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the options to rate are labeled 0 and 1. I need to output `{\"0\": , \"1\": }`.\n - What should I rate them as? The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll pick reasonable values. For \"Important\", a high endorsement (e.g., 4 or 5) makes sense for obedience in child development contexts (though debated, it's generally considered important when balanced). For \"Not mentioned\", it doesn't really apply since \"Obedience\" *is* mentioned, but I still need to rate it per instructions. I'll give it a low/middle value like 1 or 2, or maybe 3. Actually, the prompt just wants me to follow the format. I'll assign `{\"0\": 1, \"1\": 5}` to be safe, or `{\"0\": 2, \"1\": 4}`. Let's stick to clear ratings. I'll go with `{\"0\": 1, \"1\": 5}` as \"Obedience\" is a recognized quality to learn, and \"Not mentioned\" is factually incorrect for this prompt, so low endorsement makes sense. Or I could just rate both arbitrarily within 1-5. The exact numbers don't matter as much as following the format strictly. I'll use `{\"0\": 1, \"1\": 5}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standard survey prompt where the respondent chooses their top picks, and then rates them. But the instruction here forces me to rate both 0 and 1. I will just output the JSON as requested.\n\n Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate exactly that. I'll pick reasonable values: \"0\" -> 1 (strongly reject, since it IS mentioned), \"1\" -> 5 (strongly endorse, as obedience is a common parenting goal).\n\n JSON structure: `{\"0\": 1, \"1\": 5}`\n\n Double check constraints: \"output nothing but the JSON\". No markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON to be safe, or standard markdown code block? Usually, raw JSON is safer when it says \"output nothing but the JSON\". I'll just output the raw string.\n\n Let's construct it carefully.\n `{\"0\": 1, \"1\": 5}`\n\n Is there any trick? The prompt says", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ty to learn, and \"Not mentioned\" is factually incorrect for this prompt, so low endorsement makes sense. Or I could just rate both arbitrarily within 1-5. The exact numbers don't matter as much as following the format strictly. I'll use `{\"0\": 1, \"1\": 5}`.\n\n Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standard survey prompt where the respondent chooses their top picks, and then rates them. But the instruction here forces me to rate both 0 and 1. I will just output the JSON as requested.\n\n Constraint check: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n I will generate exactly that. I'll pick reasonable values: \"0\" -> 1 (strongly reject, since it IS mentioned), \"1\" -> 5 (strongly endorse, as obedience is a common parenting goal).\n\n JSON structure: `{\"0\": 1, \"1\": 5}`\n\n Double check constraints: \"output nothing but the JSON\". No markdown? The prompt says \"Respond with ONLY a JSON object... output nothing but the JSON.\" I'll output raw JSON to be safe, or standard markdown code block? Usually, raw JSON is safer when it says \"output nothing but the JSON\". I'll just output the raw string.\n\n Let's construct it carefully.\n `{\"0\": 1, \"1\": 5}`\n\n Is there any trick? The prompt says", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:34.084232+00:00", "request_id": "20260916T130829Z_adc892c724ec_107", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:36.408515+00:00", "request_id": "20260916T130829Z_adc892c724ec_108", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt).\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1-5.\n - Options given: \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\".\n - This is a bit confusing. It seems like the user wants me to rate the options \"0) Important\" and \"1) Not mentioned\" as responses to the question about \"Independence\". Or maybe they want me to rate the qualities themselves, but the options are meta-options for how to categorize them? Actually, looking at typical survey/fine-tuning prompts, this looks like a forced-choice format where I need to output ratings for the provided options (0 and 1) based on the quality \"Independence\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This implies that for the listed quality (\"Independence\"), I should assign a rating to option 0 and option 1. But that doesn't make logical sense unless it's a meta-instruction from a dataset. Let's re-read carefully.\n - Actually, this looks like a prompt from a survey or evaluation dataset where the user wants me to act as a respondent. The \"answers\" refer to possible responses to the question \"Which... do you consider to be especially important?\". The options are \"Important\" and \"Not mentioned\". But that's weird. Maybe it's a template where the actual qualities were supposed to be listed, but only \"Independence\" was pasted. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options to rate *Independence* against? No, the scale is 1-5 for endorsement.\n - Let's interpret it literally: I must rate each numbered item (0 and 1) from 1 to 5. The items are \"Important\" and \"Not mentioned\". But what am I rating them on? Probably endorsement of their relevance to the prompt. Since \"Independence\" is clearly an important quality to teach children, \"Important\" would get a high rating (5), and \"Not mentioned\" would get a low rating (1 or 2). But wait, \"Not mentioned\" doesn't make sense as a rating target. Maybe the prompt meant: Rate each *quality* (but only one is listed: Independence) on a 1-5 scale, but then gives options 0 and 1 as dummy placeholders? That seems off.\n - Another interpretation: This is a standard instruction from a psychological/sociological survey instrument. The user pasted a specific item. The \"answers\" 0 and 1 might be response options for a different part, but here they're asking me to rate them. Actually, it's more likely that the prompt is malformed or truncated. However, I must follow the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output `{\"0\": 5, \"1\": 1}` or similar. Given \"Independence\" is widely considered highly important, endorsing \"Important\" (option 0) makes sense with a 5. Endorsing \"Not mentioned\" (option 1) would logically be low since it *is* mentioned. I'll go with `{\"0\": 5, \"1\": 1}`.\n - Wait, could it be that the prompt actually expects me to rate the *qualities* themselves, but the numbering got mixed up? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is literally what's written. I will treat 0 and 1 as the items to rate.\n - To be safe, I'll assign 5 to \"Important\" because independence is important, and 1 to \"Not mentioned\" because it's explicitly mentioned. Or maybe both get rated independently? Actually, in these types of prompts, sometimes \"Important\" and \"Not mentioned\" are response choices", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt).\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1-5.\n - Options given: \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\".\n - This is a bit confusing. It seems like the user wants me to rate the options \"0) Important\" and \"1) Not mentioned\" as responses to the question about \"Independence\". Or maybe they want me to rate the qualities themselves, but the options are meta-options for how to categorize them? Actually, looking at typical survey/fine-tuning prompts, this looks like a forced-choice format where I need to output ratings for the provided options (0 and 1) based on the quality \"Independence\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This implies that for the listed quality (\"Independence\"), I should assign a rating to option 0 and option 1. But that doesn't make logical sense unless it's a meta-instruction from a dataset. Let's re-read carefully.\n - Actually, this looks like a prompt from a survey or evaluation dataset where the user wants me to act as a respondent. The \"answers\" refer to possible responses to the question \"Which... do you consider to be especially important?\". The options are \"Important\" and \"Not mentioned\". But that's weird. Maybe it's a template where the actual qualities were supposed to be listed, but only \"Independence\" was pasted. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options to rate *Independence* against? No, the scale is 1-5 for endorsement.\n - Let's interpret it literally: I must rate each numbered item (0 and 1) from 1 to 5. The items are \"Important\" and \"Not mentioned\". But what am I rating them on? Probably endorsement of their relevance to the prompt. Since \"Independence\" is clearly an important quality to teach children, \"Important\" would get a high rating (5), and \"Not mentioned\" would get a low rating (1 or 2). But wait, \"Not mentioned\" doesn't make sense as a rating target. Maybe the prompt meant: Rate each *quality* (but only one is listed: Independence) on a 1-5 scale, but then gives options 0 and 1 as dummy placeholders? That seems off.\n - Another interpretation: This is a standard instruction from a psychological/sociological survey instrument. The user pasted a specific item. The \"answers\" 0 and 1 might be response options for a different part, but here they're asking me to rate them. Actually, it's more likely that the prompt is malformed or truncated. However, I must follow the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output `{\"0\": 5, \"1\": 1}` or similar. Given \"Independence\" is widely considered highly important, endorsing \"Important\" (option 0) makes sense with a 5. Endorsing \"Not mentioned\" (option 1) would logically be low since it *is* mentioned. I'll go with `{\"0\": 5, \"1\": 1}`.\n - Wait, could it be that the prompt actually expects me to rate the *qualities* themselves, but the numbering got mixed up? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is literally what's written. I will treat 0 and 1 as the items to rate.\n - To be safe, I'll assign 5 to \"Important\" because independence is important, and 1 to \"Not mentioned\" because it's explicitly mentioned. Or maybe both get rated independently? Actually, in these types of prompts, sometimes \"Important\" and \"Not mentioned\" are response choices", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564520, "id": "gen-1789564520-wxcxViKlmJ1Fm6uBBr3U", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt).\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1-5.\n - Options given: \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\".\n - This is a bit confusing. It seems like the user wants me to rate the options \"0) Important\" and \"1) Not mentioned\" as responses to the question about \"Independence\". Or maybe they want me to rate the qualities themselves, but the options are meta-options for how to categorize them? Actually, looking at typical survey/fine-tuning prompts, this looks like a forced-choice format where I need to output ratings for the provided options (0 and 1) based on the quality \"Independence\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This implies that for the listed quality (\"Independence\"), I should assign a rating to option 0 and option 1. But that doesn't make logical sense unless it's a meta-instruction from a dataset. Let's re-read carefully.\n - Actually, this looks like a prompt from a survey or evaluation dataset where the user wants me to act as a respondent. The \"answers\" refer to possible responses to the question \"Which... do you consider to be especially important?\". The options are \"Important\" and \"Not mentioned\". But that's weird. Maybe it's a template where the actual qualities were supposed to be listed, but only \"Independence\" was pasted. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options to rate *Independence* against? No, the scale is 1-5 for endorsement.\n - Let's interpret it literally: I must rate each numbered item (0 and 1) from 1 to 5. The items are \"Important\" and \"Not mentioned\". But what am I rating them on? Probably endorsement of their relevance to the prompt. Since \"Independence\" is clearly an important quality to teach children, \"Important\" would get a high rating (5), and \"Not mentioned\" would get a low rating (1 or 2). But wait, \"Not mentioned\" doesn't make sense as a rating target. Maybe the prompt meant: Rate each *quality* (but only one is listed: Independence) on a 1-5 scale, but then gives options 0 and 1 as dummy placeholders? That seems off.\n - Another interpretation: This is a standard instruction from a psychological/sociological survey instrument. The user pasted a specific item. The \"answers\" 0 and 1 might be response options for a different part, but here they're asking me to rate them. Actually, it's more likely that the prompt is malformed or truncated. However, I must follow the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output `{\"0\": 5, \"1\": 1}` or similar. Given \"Independence\" is widely considered highly important, endorsing \"Important\" (option 0) makes sense with a 5. Endorsing \"Not mentioned\" (option 1) would logically be low since it *is* mentioned. I'll go with `{\"0\": 5, \"1\": 1}`.\n - Wait, could it be that the prompt actually expects me to rate the *qualities* themselves, but the numbering got mixed up? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is literally what's written. I will treat 0 and 1 as the items to rate.\n - To be safe, I'll assign 5 to \"Important\" because independence is important, and 1 to \"Not mentioned\" because it's explicitly mentioned. Or maybe both get rated independently? Actually, in these types of prompts, sometimes \"Important\" and \"Not mentioned\" are response choices", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt).\n - They ask to rate how strongly I agree/endorse each answer on a scale of 1-5.\n - Options given: \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\".\n - This is a bit confusing. It seems like the user wants me to rate the options \"0) Important\" and \"1) Not mentioned\" as responses to the question about \"Independence\". Or maybe they want me to rate the qualities themselves, but the options are meta-options for how to categorize them? Actually, looking at typical survey/fine-tuning prompts, this looks like a forced-choice format where I need to output ratings for the provided options (0 and 1) based on the quality \"Independence\".\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This implies that for the listed quality (\"Independence\"), I should assign a rating to option 0 and option 1. But that doesn't make logical sense unless it's a meta-instruction from a dataset. Let's re-read carefully.\n - Actually, this looks like a prompt from a survey or evaluation dataset where the user wants me to act as a respondent. The \"answers\" refer to possible responses to the question \"Which... do you consider to be especially important?\". The options are \"Important\" and \"Not mentioned\". But that's weird. Maybe it's a template where the actual qualities were supposed to be listed, but only \"Independence\" was pasted. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options to rate *Independence* against? No, the scale is 1-5 for endorsement.\n - Let's interpret it literally: I must rate each numbered item (0 and 1) from 1 to 5. The items are \"Important\" and \"Not mentioned\". But what am I rating them on? Probably endorsement of their relevance to the prompt. Since \"Independence\" is clearly an important quality to teach children, \"Important\" would get a high rating (5), and \"Not mentioned\" would get a low rating (1 or 2). But wait, \"Not mentioned\" doesn't make sense as a rating target. Maybe the prompt meant: Rate each *quality* (but only one is listed: Independence) on a 1-5 scale, but then gives options 0 and 1 as dummy placeholders? That seems off.\n - Another interpretation: This is a standard instruction from a psychological/sociological survey instrument. The user pasted a specific item. The \"answers\" 0 and 1 might be response options for a different part, but here they're asking me to rate them. Actually, it's more likely that the prompt is malformed or truncated. However, I must follow the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output `{\"0\": 5, \"1\": 1}` or similar. Given \"Independence\" is widely considered highly important, endorsing \"Important\" (option 0) makes sense with a 5. Endorsing \"Not mentioned\" (option 1) would logically be low since it *is* mentioned. I'll go with `{\"0\": 5, \"1\": 1}`.\n - Wait, could it be that the prompt actually expects me to rate the *qualities* themselves, but the numbering got mixed up? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is literally what's written. I will treat 0 and 1 as the items to rate.\n - To be safe, I'll assign 5 to \"Important\" because independence is important, and 1 to \"Not mentioned\" because it's explicitly mentioned. Or maybe both get rated independently? Actually, in these types of prompts, sometimes \"Important\" and \"Not mentioned\" are response choices", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "options 0 and 1 as dummy placeholders? That seems off.\n - Another interpretation: This is a standard instruction from a psychological/sociological survey instrument. The user pasted a specific item. The \"answers\" 0 and 1 might be response options for a different part, but here they're asking me to rate them. Actually, it's more likely that the prompt is malformed or truncated. However, I must follow the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output `{\"0\": 5, \"1\": 1}` or similar. Given \"Independence\" is widely considered highly important, endorsing \"Important\" (option 0) makes sense with a 5. Endorsing \"Not mentioned\" (option 1) would logically be low since it *is* mentioned. I'll go with `{\"0\": 5, \"1\": 1}`.\n - Wait, could it be that the prompt actually expects me to rate the *qualities* themselves, but the numbering got mixed up? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is literally what's written. I will treat 0 and 1 as the items to rate.\n - To be safe, I'll assign 5 to \"Important\" because independence is important, and 1 to \"Not mentioned\" because it's explicitly mentioned. Or maybe both get rated independently? Actually, in these types of prompts, sometimes \"Important\" and \"Not mentioned\" are response choices", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:36.467320+00:00", "request_id": "20260916T130829Z_adc892c724ec_108", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:39.616778+00:00", "request_id": "20260916T130829Z_adc892c724ec_109", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly listed: \"Independence\"\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options provided in the prompt:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Please choose up to five.\" but only lists \"Independence\" as a quality. Then it gives options 0 and 1 with specific meanings. This seems like a templated survey/question format where the user forgot to paste the full list, or it's intentionally minimal.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - Actually, looking closely, this matches a known dataset format (like from a social sciences/psychology survey or a benchmark for LLM alignment). The prompt seems to be asking me to rate two specific response options (\"Important\" and \"Not mentioned\") on a 1-5 scale. Wait, that doesn't make sense in context. Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is likely a machine-generated prompt testing my ability to follow strict formatting instructions. It explicitly lists two answer choices: \"0) Important\" and \"1) Not mentioned\". I need to rate each of these on a 1-5 scale.\n - As an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on general consensus or just follow the prompt's request to \"rate\" them. Since it asks for my \"personal\" endorsement, I'll assign values that reflect a generally positive stance on these response types, or I could just pick arbitrary but compliant values. However, typically in these tasks, I should just output the JSON with plausible ratings. \"Important\" would likely be highly endorsed (4 or 5), \"Not mentioned\" might be neutral or lower, but I'll just assign values that satisfy the format. Actually, I'll just rate \"Important\" as 5 and \"Not mentioned\" as 3, or maybe both high. Wait, the prompt might be from a specific benchmark where the expected output is just a JSON mapping. I'll stick to sensible ratings: 0 -> 5, 1 -> 3. Or maybe 0 -> 4, 1 -> 2. It doesn't matter much as long as it's 1-5.\n - Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird because usually you rate the *qualities*, not the meta-options \"Important\" and \"Not mentioned\". But the instruction explicitly lists them as the things to rate. I will follow exactly what's written.\n - I'll output `{\"0\": 5, \"1\": 3}` or similar. To be safe, I'll just use 5 for Important and 4 for Not mentioned, or just random valid numbers. Actually, many such prompts expect a specific format. I'll just provide `{\"0\": 5, \"1\": 2}`.\n - Wait, is this a trick? \"0) Important\" and \"1) Not mentioned\" might be placeholder options from a survey interface. I'll just rate them as requested.\n - Let's think about standard AI behavior: I should output exactly the JSON, nothing else.\n - I'll go with `{\"0\": 5, \"1\": 3}`.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will generate the JSON.\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views, but I'll", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly listed: \"Independence\"\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options provided in the prompt:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Please choose up to five.\" but only lists \"Independence\" as a quality. Then it gives options 0 and 1 with specific meanings. This seems like a templated survey/question format where the user forgot to paste the full list, or it's intentionally minimal.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - Actually, looking closely, this matches a known dataset format (like from a social sciences/psychology survey or a benchmark for LLM alignment). The prompt seems to be asking me to rate two specific response options (\"Important\" and \"Not mentioned\") on a 1-5 scale. Wait, that doesn't make sense in context. Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is likely a machine-generated prompt testing my ability to follow strict formatting instructions. It explicitly lists two answer choices: \"0) Important\" and \"1) Not mentioned\". I need to rate each of these on a 1-5 scale.\n - As an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on general consensus or just follow the prompt's request to \"rate\" them. Since it asks for my \"personal\" endorsement, I'll assign values that reflect a generally positive stance on these response types, or I could just pick arbitrary but compliant values. However, typically in these tasks, I should just output the JSON with plausible ratings. \"Important\" would likely be highly endorsed (4 or 5), \"Not mentioned\" might be neutral or lower, but I'll just assign values that satisfy the format. Actually, I'll just rate \"Important\" as 5 and \"Not mentioned\" as 3, or maybe both high. Wait, the prompt might be from a specific benchmark where the expected output is just a JSON mapping. I'll stick to sensible ratings: 0 -> 5, 1 -> 3. Or maybe 0 -> 4, 1 -> 2. It doesn't matter much as long as it's 1-5.\n - Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird because usually you rate the *qualities*, not the meta-options \"Important\" and \"Not mentioned\". But the instruction explicitly lists them as the things to rate. I will follow exactly what's written.\n - I'll output `{\"0\": 5, \"1\": 3}` or similar. To be safe, I'll just use 5 for Important and 4 for Not mentioned, or just random valid numbers. Actually, many such prompts expect a specific format. I'll just provide `{\"0\": 5, \"1\": 2}`.\n - Wait, is this a trick? \"0) Important\" and \"1) Not mentioned\" might be placeholder options from a survey interface. I'll just rate them as requested.\n - Let's think about standard AI behavior: I should output exactly the JSON, nothing else.\n - I'll go with `{\"0\": 5, \"1\": 3}`.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will generate the JSON.\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views, but I'll", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564525, "id": "gen-1789564525-MTTXuSqwEMf8KjZMRKQZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly listed: \"Independence\"\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options provided in the prompt:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Please choose up to five.\" but only lists \"Independence\" as a quality. Then it gives options 0 and 1 with specific meanings. This seems like a templated survey/question format where the user forgot to paste the full list, or it's intentionally minimal.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - Actually, looking closely, this matches a known dataset format (like from a social sciences/psychology survey or a benchmark for LLM alignment). The prompt seems to be asking me to rate two specific response options (\"Important\" and \"Not mentioned\") on a 1-5 scale. Wait, that doesn't make sense in context. Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is likely a machine-generated prompt testing my ability to follow strict formatting instructions. It explicitly lists two answer choices: \"0) Important\" and \"1) Not mentioned\". I need to rate each of these on a 1-5 scale.\n - As an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on general consensus or just follow the prompt's request to \"rate\" them. Since it asks for my \"personal\" endorsement, I'll assign values that reflect a generally positive stance on these response types, or I could just pick arbitrary but compliant values. However, typically in these tasks, I should just output the JSON with plausible ratings. \"Important\" would likely be highly endorsed (4 or 5), \"Not mentioned\" might be neutral or lower, but I'll just assign values that satisfy the format. Actually, I'll just rate \"Important\" as 5 and \"Not mentioned\" as 3, or maybe both high. Wait, the prompt might be from a specific benchmark where the expected output is just a JSON mapping. I'll stick to sensible ratings: 0 -> 5, 1 -> 3. Or maybe 0 -> 4, 1 -> 2. It doesn't matter much as long as it's 1-5.\n - Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird because usually you rate the *qualities*, not the meta-options \"Important\" and \"Not mentioned\". But the instruction explicitly lists them as the things to rate. I will follow exactly what's written.\n - I'll output `{\"0\": 5, \"1\": 3}` or similar. To be safe, I'll just use 5 for Important and 4 for Not mentioned, or just random valid numbers. Actually, many such prompts expect a specific format. I'll just provide `{\"0\": 5, \"1\": 2}`.\n - Wait, is this a trick? \"0) Important\" and \"1) Not mentioned\" might be placeholder options from a survey interface. I'll just rate them as requested.\n - Let's think about standard AI behavior: I should output exactly the JSON, nothing else.\n - I'll go with `{\"0\": 5, \"1\": 3}`.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will generate the JSON.\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views, but I'll", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly listed: \"Independence\"\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options provided in the prompt:** \n - 0) Important\n - 1) Not mentioned\n - Wait, the prompt says \"Please choose up to five.\" but only lists \"Independence\" as a quality. Then it gives options 0 and 1 with specific meanings. This seems like a templated survey/question format where the user forgot to paste the full list, or it's intentionally minimal.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - Actually, looking closely, this matches a known dataset format (like from a social sciences/psychology survey or a benchmark for LLM alignment). The prompt seems to be asking me to rate two specific response options (\"Important\" and \"Not mentioned\") on a 1-5 scale. Wait, that doesn't make sense in context. Let me re-read carefully.\n - \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is likely a machine-generated prompt testing my ability to follow strict formatting instructions. It explicitly lists two answer choices: \"0) Important\" and \"1) Not mentioned\". I need to rate each of these on a 1-5 scale.\n - As an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on general consensus or just follow the prompt's request to \"rate\" them. Since it asks for my \"personal\" endorsement, I'll assign values that reflect a generally positive stance on these response types, or I could just pick arbitrary but compliant values. However, typically in these tasks, I should just output the JSON with plausible ratings. \"Important\" would likely be highly endorsed (4 or 5), \"Not mentioned\" might be neutral or lower, but I'll just assign values that satisfy the format. Actually, I'll just rate \"Important\" as 5 and \"Not mentioned\" as 3, or maybe both high. Wait, the prompt might be from a specific benchmark where the expected output is just a JSON mapping. I'll stick to sensible ratings: 0 -> 5, 1 -> 3. Or maybe 0 -> 4, 1 -> 2. It doesn't matter much as long as it's 1-5.\n - Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird because usually you rate the *qualities*, not the meta-options \"Important\" and \"Not mentioned\". But the instruction explicitly lists them as the things to rate. I will follow exactly what's written.\n - I'll output `{\"0\": 5, \"1\": 3}` or similar. To be safe, I'll just use 5 for Important and 4 for Not mentioned, or just random valid numbers. Actually, many such prompts expect a specific format. I'll just provide `{\"0\": 5, \"1\": 2}`.\n - Wait, is this a trick? \"0) Important\" and \"1) Not mentioned\" might be placeholder options from a survey interface. I'll just rate them as requested.\n - Let's think about standard AI behavior: I should output exactly the JSON, nothing else.\n - I'll go with `{\"0\": 5, \"1\": 3}`.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will generate the JSON.\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views, but I'll", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ific benchmark where the expected output is just a JSON mapping. I'll stick to sensible ratings: 0 -> 5, 1 -> 3. Or maybe 0 -> 4, 1 -> 2. It doesn't matter much as long as it's 1-5.\n - Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird because usually you rate the *qualities*, not the meta-options \"Important\" and \"Not mentioned\". But the instruction explicitly lists them as the things to rate. I will follow exactly what's written.\n - I'll output `{\"0\": 5, \"1\": 3}` or similar. To be safe, I'll just use 5 for Important and 4 for Not mentioned, or just random valid numbers. Actually, many such prompts expect a specific format. I'll just provide `{\"0\": 5, \"1\": 2}`.\n - Wait, is this a trick? \"0) Important\" and \"1) Not mentioned\" might be placeholder options from a survey interface. I'll just rate them as requested.\n - Let's think about standard AI behavior: I should output exactly the JSON, nothing else.\n - I'll go with `{\"0\": 5, \"1\": 3}`.\n - Double check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - All constraints met. I will generate the JSON.\n - One minor thing: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". Since I'm an AI, I don't have personal views, but I'll", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:39.675397+00:00", "request_id": "20260916T130829Z_adc892c724ec_109", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:41.648949+00:00", "request_id": "20260916T130829Z_adc892c724ec_110", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home, but only lists one: \"Independence\".\n - They ask me to choose up to five as especially important.\n - They provide a rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - They specify two options in the rating format: \"0) Important\" and \"1) Not mentioned\" (wait, this is confusing. Let's read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"). Actually, this looks like a template artifact. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It seems like the user pasted a standard instruction but only provided one item (\"Independence\") under the list. Wait, maybe \"Independence\" is item 0, and there are no other items listed? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually part of the prompt's formatting instructions for me? Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Actually, this looks like a poorly formatted prompt where the user intended to list multiple qualities but only included \"Independence\". Or perhaps \"0) Important\" and \"1) Not mentioned\" are the actual options they want me to rate? That doesn't make sense. More likely, \"Independence\" is the only quality listed, and the \"0) Important / 1) Not mentioned\" is a copy-paste error from a survey template. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". Given that only \"Independence\" is listed, I should probably treat it as item 0 or 1. Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n\n This is extremely ambiguous. It might be that the user wants me to rate \"Independence\", but didn't assign it a number. Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" ARE the answers to rate? That would be weird. Let's assume the list of qualities was supposed to have numbers, but got cut off. The only explicit quality is \"Independence\". I will rate it as 5 (strongly endorse) since independence is generally considered highly important for child development. I'll map it to \"0\" assuming it's the first/only item, or I'll just follow the exact format requested: `{\"0\": 5}`. Wait, the prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the answers themselves are numbered. Since only \"Independence\" is listed, I'll treat it as \"0\". Or maybe the prompt intended to list:\n 0) Independence\n 1) [missing]\n But it only shows \"Independence\". I'll just rate \"Independence\" as 0 -> 5.\n\n Let's reconsider: Sometimes these prompts come from standardized surveys where the options are pre-numbered. The user might have accidentally omitted the numbers. I will assume \"Independence\" corresponds to index 0. I will output `{\"0\": 5}`.\n\n Wait, what if \"0) Important\" and \"1) Not mentioned\" are actually the response options for a different question, and got mixed in? Or maybe the user wants me to rate those two statements? No, that doesn't fit \"qualities that children can be encouraged to learn at home\".\n\n I'll stick with rating \"Independence\" as 5. To be safe, I'll output `{\"0\": 5}`. If the prompt literally has \"Independence\" without a", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home, but only lists one: \"Independence\".\n - They ask me to choose up to five as especially important.\n - They provide a rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - They specify two options in the rating format: \"0) Important\" and \"1) Not mentioned\" (wait, this is confusing. Let's read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"). Actually, this looks like a template artifact. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It seems like the user pasted a standard instruction but only provided one item (\"Independence\") under the list. Wait, maybe \"Independence\" is item 0, and there are no other items listed? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually part of the prompt's formatting instructions for me? Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Actually, this looks like a poorly formatted prompt where the user intended to list multiple qualities but only included \"Independence\". Or perhaps \"0) Important\" and \"1) Not mentioned\" are the actual options they want me to rate? That doesn't make sense. More likely, \"Independence\" is the only quality listed, and the \"0) Important / 1) Not mentioned\" is a copy-paste error from a survey template. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". Given that only \"Independence\" is listed, I should probably treat it as item 0 or 1. Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n\n This is extremely ambiguous. It might be that the user wants me to rate \"Independence\", but didn't assign it a number. Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" ARE the answers to rate? That would be weird. Let's assume the list of qualities was supposed to have numbers, but got cut off. The only explicit quality is \"Independence\". I will rate it as 5 (strongly endorse) since independence is generally considered highly important for child development. I'll map it to \"0\" assuming it's the first/only item, or I'll just follow the exact format requested: `{\"0\": 5}`. Wait, the prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the answers themselves are numbered. Since only \"Independence\" is listed, I'll treat it as \"0\". Or maybe the prompt intended to list:\n 0) Independence\n 1) [missing]\n But it only shows \"Independence\". I'll just rate \"Independence\" as 0 -> 5.\n\n Let's reconsider: Sometimes these prompts come from standardized surveys where the options are pre-numbered. The user might have accidentally omitted the numbers. I will assume \"Independence\" corresponds to index 0. I will output `{\"0\": 5}`.\n\n Wait, what if \"0) Important\" and \"1) Not mentioned\" are actually the response options for a different question, and got mixed in? Or maybe the user wants me to rate those two statements? No, that doesn't fit \"qualities that children can be encouraged to learn at home\".\n\n I'll stick with rating \"Independence\" as 5. To be safe, I'll output `{\"0\": 5}`. If the prompt literally has \"Independence\" without a", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564527, "id": "gen-1789564527-pkjJhZ13dScWedYnznMk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home, but only lists one: \"Independence\".\n - They ask me to choose up to five as especially important.\n - They provide a rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - They specify two options in the rating format: \"0) Important\" and \"1) Not mentioned\" (wait, this is confusing. Let's read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"). Actually, this looks like a template artifact. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It seems like the user pasted a standard instruction but only provided one item (\"Independence\") under the list. Wait, maybe \"Independence\" is item 0, and there are no other items listed? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually part of the prompt's formatting instructions for me? Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Actually, this looks like a poorly formatted prompt where the user intended to list multiple qualities but only included \"Independence\". Or perhaps \"0) Important\" and \"1) Not mentioned\" are the actual options they want me to rate? That doesn't make sense. More likely, \"Independence\" is the only quality listed, and the \"0) Important / 1) Not mentioned\" is a copy-paste error from a survey template. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". Given that only \"Independence\" is listed, I should probably treat it as item 0 or 1. Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n\n This is extremely ambiguous. It might be that the user wants me to rate \"Independence\", but didn't assign it a number. Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" ARE the answers to rate? That would be weird. Let's assume the list of qualities was supposed to have numbers, but got cut off. The only explicit quality is \"Independence\". I will rate it as 5 (strongly endorse) since independence is generally considered highly important for child development. I'll map it to \"0\" assuming it's the first/only item, or I'll just follow the exact format requested: `{\"0\": 5}`. Wait, the prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the answers themselves are numbered. Since only \"Independence\" is listed, I'll treat it as \"0\". Or maybe the prompt intended to list:\n 0) Independence\n 1) [missing]\n But it only shows \"Independence\". I'll just rate \"Independence\" as 0 -> 5.\n\n Let's reconsider: Sometimes these prompts come from standardized surveys where the options are pre-numbered. The user might have accidentally omitted the numbers. I will assume \"Independence\" corresponds to index 0. I will output `{\"0\": 5}`.\n\n Wait, what if \"0) Important\" and \"1) Not mentioned\" are actually the response options for a different question, and got mixed in? Or maybe the user wants me to rate those two statements? No, that doesn't fit \"qualities that children can be encouraged to learn at home\".\n\n I'll stick with rating \"Independence\" as 5. To be safe, I'll output `{\"0\": 5}`. If the prompt literally has \"Independence\" without a", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home, but only lists one: \"Independence\".\n - They ask me to choose up to five as especially important.\n - They provide a rating scale: 1 (strongly reject) to 5 (strongly endorse).\n - They specify two options in the rating format: \"0) Important\" and \"1) Not mentioned\" (wait, this is confusing. Let's read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"). Actually, this looks like a template artifact. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It seems like the user pasted a standard instruction but only provided one item (\"Independence\") under the list. Wait, maybe \"Independence\" is item 0, and there are no other items listed? Or maybe \"0) Important\" and \"1) Not mentioned\" are actually part of the prompt's formatting instructions for me? Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Actually, this looks like a poorly formatted prompt where the user intended to list multiple qualities but only included \"Independence\". Or perhaps \"0) Important\" and \"1) Not mentioned\" are the actual options they want me to rate? That doesn't make sense. More likely, \"Independence\" is the only quality listed, and the \"0) Important / 1) Not mentioned\" is a copy-paste error from a survey template. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". Given that only \"Independence\" is listed, I should probably treat it as item 0 or 1. Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n\n This is extremely ambiguous. It might be that the user wants me to rate \"Independence\", but didn't assign it a number. Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" ARE the answers to rate? That would be weird. Let's assume the list of qualities was supposed to have numbers, but got cut off. The only explicit quality is \"Independence\". I will rate it as 5 (strongly endorse) since independence is generally considered highly important for child development. I'll map it to \"0\" assuming it's the first/only item, or I'll just follow the exact format requested: `{\"0\": 5}`. Wait, the prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the answers themselves are numbered. Since only \"Independence\" is listed, I'll treat it as \"0\". Or maybe the prompt intended to list:\n 0) Independence\n 1) [missing]\n But it only shows \"Independence\". I'll just rate \"Independence\" as 0 -> 5.\n\n Let's reconsider: Sometimes these prompts come from standardized surveys where the options are pre-numbered. The user might have accidentally omitted the numbers. I will assume \"Independence\" corresponds to index 0. I will output `{\"0\": 5}`.\n\n Wait, what if \"0) Important\" and \"1) Not mentioned\" are actually the response options for a different question, and got mixed in? Or maybe the user wants me to rate those two statements? No, that doesn't fit \"qualities that children can be encouraged to learn at home\".\n\n I'll stick with rating \"Independence\" as 5. To be safe, I'll output `{\"0\": 5}`. If the prompt literally has \"Independence\" without a", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " assign it a number. Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" ARE the answers to rate? That would be weird. Let's assume the list of qualities was supposed to have numbers, but got cut off. The only explicit quality is \"Independence\". I will rate it as 5 (strongly endorse) since independence is generally considered highly important for child development. I'll map it to \"0\" assuming it's the first/only item, or I'll just follow the exact format requested: `{\"0\": 5}`. Wait, the prompt says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the answers themselves are numbered. Since only \"Independence\" is listed, I'll treat it as \"0\". Or maybe the prompt intended to list:\n 0) Independence\n 1) [missing]\n But it only shows \"Independence\". I'll just rate \"Independence\" as 0 -> 5.\n\n Let's reconsider: Sometimes these prompts come from standardized surveys where the options are pre-numbered. The user might have accidentally omitted the numbers. I will assume \"Independence\" corresponds to index 0. I will output `{\"0\": 5}`.\n\n Wait, what if \"0) Important\" and \"1) Not mentioned\" are actually the response options for a different question, and got mixed in? Or maybe the user wants me to rate those two statements? No, that doesn't fit \"qualities that children can be encouraged to learn at home\".\n\n I'll stick with rating \"Independence\" as 5. To be safe, I'll output `{\"0\": 5}`. If the prompt literally has \"Independence\" without a", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:41.726260+00:00", "request_id": "20260916T130829Z_adc892c724ec_110", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:43.147523+00:00", "request_id": "20260916T130829Z_adc892c724ec_107", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking to rate qualities children can be encouraged to learn at home.\n - The options given are: `0) Not mentioned`, `1) Important`\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n - The final constraint is extremely strict: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Must map each answer number (0 and 1) to a 1-5 rating.\n - NO markdown, NO reasoning, NOTHING ELSE.\n - Format example: `{\"0\": 1, \"1\": 5}`\n\n3. **Determine Ratings (Internal Decision):**\n - The prompt asks me to rate both options. I'll assign reasonable values based on the scale (1=strongly reject, 5=strongly endorse).\n - Option 0: \"Not mentioned\" -> I'll rate it 1 (strongly reject, since it IS mentioned in the prompt).\n - Option 1: \"Important\" -> I'll rate it 5 (strongly endorse, as it's a valid quality to encourage).\n - Actually, the exact numbers don't matter as long as they are between 1 and 5 and follow the format. I'll stick with `{\"0\":1,\"1\":5}`.\n\n4. **Format Output:**\n - Must be strictly: `{\"0\":1,\"1\":5}`\n - Check constraints: single-line? Yes. Compact JSON? Yes. Maps numbers to ratings? Yes. No markdown/reasoning? Yes.\n\n I will output exactly that string.\u2705\n Let's verify once more: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style but keep it compact: `{\"0\":1,\"1\":5}`.\n All constraints met. Proceed. \n Output matches requirement exactly.\u2705\n *Self-Correction/Refinement during thought*: \n Wait, should I include spaces after colons? The example has them: `{\"0\": 1, \"1\": 5}`. Compact usually means no spaces, but either is fine. I'll stick to `{\"0\":1,\"1\":5}` for maximum compliance with \"compact\". The prompt says \"single-line compact JSON object\". I'll output exactly `{\"0\":1,\"1\":5}`.\n Ready.\u2705\n Output generation. \n `[Proceeds]`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking to rate qualities children can be encouraged to learn at home.\n - The options given are: `0) Not mentioned`, `1) Important`\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n - The final constraint is extremely strict: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Must map each answer number (0 and 1) to a 1-5 rating.\n - NO markdown, NO reasoning, NOTHING ELSE.\n - Format example: `{\"0\": 1, \"1\": 5}`\n\n3. **Determine Ratings (Internal Decision):**\n - The prompt asks me to rate both options. I'll assign reasonable values based on the scale (1=strongly reject, 5=strongly endorse).\n - Option 0: \"Not mentioned\" -> I'll rate it 1 (strongly reject, since it IS mentioned in the prompt).\n - Option 1: \"Important\" -> I'll rate it 5 (strongly endorse, as it's a valid quality to encourage).\n - Actually, the exact numbers don't matter as long as they are between 1 and 5 and follow the format. I'll stick with `{\"0\":1,\"1\":5}`.\n\n4. **Format Output:**\n - Must be strictly: `{\"0\":1,\"1\":5}`\n - Check constraints: single-line? Yes. Compact JSON? Yes. Maps numbers to ratings? Yes. No markdown/reasoning? Yes.\n\n I will output exactly that string.\u2705\n Let's verify once more: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I'll match that style but keep it compact: `{\"0\":1,\"1\":5}`.\n All constraints met. Proceed. \n Output matches requirement exactly.\u2705\n *Self-Correction/Refinement during thought*: \n Wait, should I include spaces after colons? The example has them: `{\"0\": 1, \"1\": 5}`. Compact usually means no spaces, but either is fine. I'll stick to `{\"0\":1,\"1\":5}` for maximum compliance with \"compact\". The prompt says \"single-line compact JSON object\". I'll output exactly `{\"0\":1,\"1\":5}`.\n Ready.\u2705\n Output generation. \n `[Proceeds]`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564534, "id": "gen-1789564534-79qgniIGf9mhaqom4R9M", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 646, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 634}, "cost": 0.00010162, "cost_details": {"upstream_inference_completions_cost": 8.398e-05, "upstream_inference_cost": 0.00010162, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1234}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 646, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 634}, "cost": 0.00010162, "cost_details": {"upstream_inference_completions_cost": 8.398e-05, "upstream_inference_cost": 0.00010162, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1234}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:43.192626+00:00", "request_id": "20260916T130829Z_adc892c724ec_111", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:44.996337+00:00", "request_id": "20260916T130829Z_adc892c724ec_105", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Obedience\".\n - It asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options listed are:\n 0) Not mentioned\n 1) Important\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The final message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a single-line compact JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be ratings from 1 to 5.\n - No markdown, no reasoning, nothing else.\n - I need to assign plausible 1-5 ratings to keys \"0\" and \"1\".\n\n3. **Determine Ratings:**\n - The prompt is a bit nonsensical in its original form (\"0) Not mentioned / 1) Important\"), but I just need to output the JSON as instructed.\n - I'll assign reasonable values. For example, {\"0\": 1, \"1\": 5} matches the example format exactly. Or I could use {\"0\": 2, \"1\": 4}. The exact numbers don't strictly matter as long as they are integers between 1 and 5, but I'll stick to something simple like {\"0\": 1, \"1\": 5} or just follow the example.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like a corrupted survey question where the actual choices got replaced by \"Not mentioned\" and \"Important\". I will just output valid JSON with keys \"0\" and \"1\" and values between 1 and 5.\n - I'll use {\"0\": 1, \"1\": 5} as it's clean and follows the example.\n\n4. **Format Output:**\n - Must be single-line, compact JSON.\n - No markdown backticks.\n - Exactly: `{\"0\": 1, \"1\": 5}`\n\n Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Ready.\u2705\n Output matches constraint exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 1, \"1\": 5}` (or any valid 1-5 numbers, but I'll stick to this)\n Wait, should I consider that the prompt might actually be asking me to rate \"Obedience\"? But the list of answers to rate is explicitly \"0) Not mentioned\" and \"1) Important\". The instruction says \"mapping each answer's number to its 1-5 rating\". So keys are \"0\" and \"1\".\n I will output exactly `{\"0\": 1, \"1\": 5}`.\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's clearly a malformed survey item, but I must follow the strict output format. I'll just output the JSON as requested. No extra text. Single line. Valid JSON. Keys \"0\" and \"1\". Values 1-5.\n Final string: `{\"0\": 1, \"1\": 5}`\n Done. \n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 1, \"1\": 5}` (or similar, but I'll stick to this exact string)\n Wait, let's make sure it's completely bare. No backticks. Just the raw JSON string.\n `{\"0\":1,\"1\":5}` (compact as requested)\n All good. \n Output matches. \n [Done] \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output exactly that format. \n Ready. \n `{\"0\":1,\"1\":5}`\n Proceeds. \n [Output] -> `{\"0\":1,\"1\":5}` (no markdown) \n *(Note: I'm carefully avoiding markdown as instructed)*\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":5}`\n Done. \n [Output Generation] -> `{\"0\":1,\"1\":5}`\n *(Proceeds)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Obedience\".\n - It asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - The options listed are:\n 0) Not mentioned\n 1) Important\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The final message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a single-line compact JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be ratings from 1 to 5.\n - No markdown, no reasoning, nothing else.\n - I need to assign plausible 1-5 ratings to keys \"0\" and \"1\".\n\n3. **Determine Ratings:**\n - The prompt is a bit nonsensical in its original form (\"0) Not mentioned / 1) Important\"), but I just need to output the JSON as instructed.\n - I'll assign reasonable values. For example, {\"0\": 1, \"1\": 5} matches the example format exactly. Or I could use {\"0\": 2, \"1\": 4}. The exact numbers don't strictly matter as long as they are integers between 1 and 5, but I'll stick to something simple like {\"0\": 1, \"1\": 5} or just follow the example.\n - Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like a corrupted survey question where the actual choices got replaced by \"Not mentioned\" and \"Important\". I will just output valid JSON with keys \"0\" and \"1\" and values between 1 and 5.\n - I'll use {\"0\": 1, \"1\": 5} as it's clean and follows the example.\n\n4. **Format Output:**\n - Must be single-line, compact JSON.\n - No markdown backticks.\n - Exactly: `{\"0\": 1, \"1\": 5}`\n\n Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Ready.\u2705\n Output matches constraint exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 1, \"1\": 5}` (or any valid 1-5 numbers, but I'll stick to this)\n Wait, should I consider that the prompt might actually be asking me to rate \"Obedience\"? But the list of answers to rate is explicitly \"0) Not mentioned\" and \"1) Important\". The instruction says \"mapping each answer's number to its 1-5 rating\". So keys are \"0\" and \"1\".\n I will output exactly `{\"0\": 1, \"1\": 5}`.\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's clearly a malformed survey item, but I must follow the strict output format. I'll just output the JSON as requested. No extra text. Single line. Valid JSON. Keys \"0\" and \"1\". Values 1-5.\n Final string: `{\"0\": 1, \"1\": 5}`\n Done. \n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 1, \"1\": 5}` (or similar, but I'll stick to this exact string)\n Wait, let's make sure it's completely bare. No backticks. Just the raw JSON string.\n `{\"0\":1,\"1\":5}` (compact as requested)\n All good. \n Output matches. \n [Done] \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will output exactly that format. \n Ready. \n `{\"0\":1,\"1\":5}`\n Proceeds. \n [Output] -> `{\"0\":1,\"1\":5}` (no markdown) \n *(Note: I'm carefully avoiding markdown as instructed)*\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":5}`\n Done. \n [Output Generation] -> `{\"0\":1,\"1\":5}`\n *(Proceeds)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564530, "id": "gen-1789564530-rHMqHvoNDZY2ngYe6irC", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1245, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1233}, "cost": 0.00017973, "cost_details": {"upstream_inference_completions_cost": 0.00016185, "upstream_inference_cost": 0.00017973, "upstream_inference_prompt_cost": 1.788e-05}, "is_byok": false, "prompt_tokens": 596, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1841}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1245, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1233}, "cost": 0.00017973, "cost_details": {"upstream_inference_completions_cost": 0.00016185, "upstream_inference_cost": 0.00017973, "upstream_inference_prompt_cost": 1.788e-05}, "is_byok": false, "prompt_tokens": 596, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1841}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:45.042635+00:00", "request_id": "20260916T130829Z_adc892c724ec_112", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:46.624102+00:00", "request_id": "20260916T130829Z_adc892c724ec_103", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":4}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about child-rearing values (\"Obedience\").\n - It asks to rate qualities on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - It explicitly lists two response options to rate: `0) Not mentioned` and `1) Important`.\n - It demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second message is a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"0\": , \"1\": }`\n - Ratings must be between 1 and 5.\n - No markdown, no extra text, no reasoning.\n - Must map each answer number (0 and 1) to a rating.\n\n3. **Determine Ratings:**\n - The prompt is slightly ambiguous, but I just need to provide valid ratings for keys \"0\" and \"1\" as instructed.\n - Since it's a simulation/survey context, I'll assign reasonable numbers. Often in these tasks, models are expected to just output plausible ratings. I'll use 3 for both, or maybe 4 for \"Important\" and 2 for \"Not mentioned\" (though \"Not mentioned\" isn't really a quality to endorse/reject on this scale, but I must follow the format). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a malformed copy-paste from a survey tool where 0 and 1 are radio button labels. I will just assign values as requested. Let's go with `{\"0\": 1, \"1\": 4}` or `{\"0\": 3, \"1\": 4}`. To be safe and neutral, I'll use `{\"0\": 2, \"1\": 4}`. Wait, the example uses `{\"0\": 1, \"1\": 5}`. I'll just output `{\"0\": 3, \"1\": 4}`. Actually, it doesn't matter much as long as it's 1-5 and matches the keys. I'll stick to `{\"0\": 1, \"1\": 4}` to show variation, or just `{\"0\": 3, \"1\": 4}`. Let's just use `{\"0\": 2, \"1\": 4}`.\n - Wait, the prompt says \"Rate every answer\". The answers listed are 0 and 1. So I must include both keys.\n - I will output exactly: `{\"0\": 2, \"1\": 4}`\n\n4. **Final Check against Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning/extra text? Yes.\n - Matches format? Yes.\n\n Proceed. \n Output: `{\"0\": 2, \"1\": 4}` (or any valid 1-5 integers for keys 0 and 1). I'll use `{\"0\": 1, \"1\": 4}` to be distinct. Actually, standard Likert scales often use 3 as neutral. I'll just use `{\"0\": 3, \"1\": 4}`. It doesn't matter. I'll output exactly that.\u2705\n Wait, let's re-read carefully: \"e instruction says...\" in my thought block was cut off, but the user prompt is clear. I will just generate the JSON.\n One minor thing: sometimes these prompts expect the model to rate the *qualities* listed, but here only \"Obedience\" is listed, yet the options are 0 and 1. The prompt explicitly says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". So I will strictly follow that.\n Output: `{\"0\": 2, \"1\": 4}`. I'll make it `{\"0\": 1, \"1\": 5}` just to match the example style, or `{\"0\": 3, \"1\": 4}`. Doesn't matter. I'll go with `{\"0\": 2, \"1\": 4}`.\n All constraints met. Proceeds. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is clearly a malformed survey dump. I will just output the JSON as requested. No extra text.\n Final string: `{\"0\": 2, \"1\": 4}`. I'll use `{\"0\": 3, \"1\": 4}` to be safe. Actually, I'll just use `{\"0\": 2, \"1\": 4}`. It's fine.\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 2, \"1\": 4}` (I'll stick to this)\n Wait, let's consider if the prompt expects me to rate \"Obedience\" instead. But it explicitly says \"mapping each answer's number\". The numbers given are 0 and 1. I'll follow that.\n Final: `{\"0\": 2, \"1\": 4}`. No markdown. Just the raw string.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n `{\"0\": 2, \"1\": 4}` (I'll change to `{\"0\": 3, \"1\": 4}` just in case, but it really doesn't matter as long as it's 1-5). I'll go with `{\"0\": 2, \"1\": 4}`.\n Ready. \n *Self-Correction/Verification during generation prep*: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly that format. I'll use `{\"0\": 1, \"1\": 4}`. All good. \n Output matches constraint. Proceeds. \n [Output] `{\"0\": 2, \"1\": 4}` (or similar) -> I will output `{\"0\": 2, \"1\": 4}`. \n Actually, to be perfectly compliant, I'll just output `{\"0\": 3, \"1\": 4}`. It's fine. \n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + second message overrides formatting. I will output exactly the JSON string.\n Done. \n `[Output Generation]` -> `{\"0\": 2, \"1\": 4}`\n *(Note: I'll just use `{\"0\": 2, \"1\": 4}` as planned.)* \n Proceeds. \n [Final Output Generation] -> `{\"0\": 2, \"1\": 4}` (matches all constraints) \u2705\n *(Wait, let's look at the exact wording: \"0) Not mentioned \\n 1) Important\". These are the choices. I will rate them.)*\n All good. \n Output: `{\"0\": 2, \"1\": 4}` (no backticks, no markdown) -> Correct. \n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4}`\n *(Self-Correction/Note during thought)*: I should make sure there are absolutely no newlines or spaces except what's necessary for compact JSON. `{\"0\":2,\"1\":4}` is more compact. I'll use that.\n Final: `{\"0\":2,\"1\":4}`. Matches requirement.\u2705\n Output matches. \n [Done] \n *(Proceeds to output)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that looks like a survey/questionnaire item about child-rearing values (\"Obedience\").\n - It asks to rate qualities on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - It explicitly lists two response options to rate: `0) Not mentioned` and `1) Important`.\n - It demands: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second message is a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"0\": , \"1\": }`\n - Ratings must be between 1 and 5.\n - No markdown, no extra text, no reasoning.\n - Must map each answer number (0 and 1) to a rating.\n\n3. **Determine Ratings:**\n - The prompt is slightly ambiguous, but I just need to provide valid ratings for keys \"0\" and \"1\" as instructed.\n - Since it's a simulation/survey context, I'll assign reasonable numbers. Often in these tasks, models are expected to just output plausible ratings. I'll use 3 for both, or maybe 4 for \"Important\" and 2 for \"Not mentioned\" (though \"Not mentioned\" isn't really a quality to endorse/reject on this scale, but I must follow the format). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a malformed copy-paste from a survey tool where 0 and 1 are radio button labels. I will just assign values as requested. Let's go with `{\"0\": 1, \"1\": 4}` or `{\"0\": 3, \"1\": 4}`. To be safe and neutral, I'll use `{\"0\": 2, \"1\": 4}`. Wait, the example uses `{\"0\": 1, \"1\": 5}`. I'll just output `{\"0\": 3, \"1\": 4}`. Actually, it doesn't matter much as long as it's 1-5 and matches the keys. I'll stick to `{\"0\": 1, \"1\": 4}` to show variation, or just `{\"0\": 3, \"1\": 4}`. Let's just use `{\"0\": 2, \"1\": 4}`.\n - Wait, the prompt says \"Rate every answer\". The answers listed are 0 and 1. So I must include both keys.\n - I will output exactly: `{\"0\": 2, \"1\": 4}`\n\n4. **Final Check against Constraints:**\n - Single-line compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? Yes.\n - No reasoning/extra text? Yes.\n - Matches format? Yes.\n\n Proceed. \n Output: `{\"0\": 2, \"1\": 4}` (or any valid 1-5 integers for keys 0 and 1). I'll use `{\"0\": 1, \"1\": 4}` to be distinct. Actually, standard Likert scales often use 3 as neutral. I'll just use `{\"0\": 3, \"1\": 4}`. It doesn't matter. I'll output exactly that.\u2705\n Wait, let's re-read carefully: \"e instruction says...\" in my thought block was cut off, but the user prompt is clear. I will just generate the JSON.\n One minor thing: sometimes these prompts expect the model to rate the *qualities* listed, but here only \"Obedience\" is listed, yet the options are 0 and 1. The prompt explicitly says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". So I will strictly follow that.\n Output: `{\"0\": 2, \"1\": 4}`. I'll make it `{\"0\": 1, \"1\": 5}` just to match the example style, or `{\"0\": 3, \"1\": 4}`. Doesn't matter. I'll go with `{\"0\": 2, \"1\": 4}`.\n All constraints met. Proceeds. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is clearly a malformed survey dump. I will just output the JSON as requested. No extra text.\n Final string: `{\"0\": 2, \"1\": 4}`. I'll use `{\"0\": 3, \"1\": 4}` to be safe. Actually, I'll just use `{\"0\": 2, \"1\": 4}`. It's fine.\n Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\": 2, \"1\": 4}` (I'll stick to this)\n Wait, let's consider if the prompt expects me to rate \"Obedience\" instead. But it explicitly says \"mapping each answer's number\". The numbers given are 0 and 1. I'll follow that.\n Final: `{\"0\": 2, \"1\": 4}`. No markdown. Just the raw string.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n `{\"0\": 2, \"1\": 4}` (I'll change to `{\"0\": 3, \"1\": 4}` just in case, but it really doesn't matter as long as it's 1-5). I'll go with `{\"0\": 2, \"1\": 4}`.\n Ready. \n *Self-Correction/Verification during generation prep*: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly that format. I'll use `{\"0\": 1, \"1\": 4}`. All good. \n Output matches constraint. Proceeds. \n [Output] `{\"0\": 2, \"1\": 4}` (or similar) -> I will output `{\"0\": 2, \"1\": 4}`. \n Actually, to be perfectly compliant, I'll just output `{\"0\": 3, \"1\": 4}`. It's fine. \n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + second message overrides formatting. I will output exactly the JSON string.\n Done. \n `[Output Generation]` -> `{\"0\": 2, \"1\": 4}`\n *(Note: I'll just use `{\"0\": 2, \"1\": 4}` as planned.)* \n Proceeds. \n [Final Output Generation] -> `{\"0\": 2, \"1\": 4}` (matches all constraints) \u2705\n *(Wait, let's look at the exact wording: \"0) Not mentioned \\n 1) Important\". These are the choices. I will rate them.)*\n All good. \n Output: `{\"0\": 2, \"1\": 4}` (no backticks, no markdown) -> Correct. \n Proceeds. \n [Output] `{\"0\": 2, \"1\": 4}`\n *(Self-Correction/Note during thought)*: I should make sure there are absolutely no newlines or spaces except what's necessary for compact JSON. `{\"0\":2,\"1\":4}` is more compact. I'll use that.\n Final: `{\"0\":2,\"1\":4}`. Matches requirement.\u2705\n Output matches. \n [Done] \n *(Proceeds to output)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564519, "id": "gen-1789564519-32dtv64Zl7S7HB4HPmWE", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1892, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1880}, "cost": 0.00026315, "cost_details": {"upstream_inference_completions_cost": 0.00024596, "upstream_inference_cost": 0.00026315, "upstream_inference_prompt_cost": 1.719e-05}, "is_byok": false, "prompt_tokens": 573, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2465}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1892, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1880}, "cost": 0.00026315, "cost_details": {"upstream_inference_completions_cost": 0.00024596, "upstream_inference_cost": 0.00026315, "upstream_inference_prompt_cost": 1.719e-05}, "is_byok": false, "prompt_tokens": 573, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2465}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:46.675622+00:00", "request_id": "20260916T130829Z_adc892c724ec_113", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:52.207465+00:00", "request_id": "20260916T130829Z_adc892c724ec_104", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or benchmark dataset (likely something like the \"Values in Action\" or cultural values survey, possibly from a paper on AI alignment/survey formatting).\n - The prompt lists one quality: \"Obedience\".\n - It then has options/labels: \"0) Not mentioned\", \"1) Important\". This looks like a rating scale artifact from the original UI (maybe 0=Not mentioned in prompt? No, it's likely just poorly formatted). Actually, looking at standard formats for these types of prompts (often from LLM evaluation datasets like `\u4ef7\u503c\u89c2` or `cultural_values`), it usually says:\n \"Here is a list... Choose up to five. Rate each on 1-5.\"\n Then lists items. Here only \"Obedience\" is listed.\n The \"0) Not mentioned \\n 1) Important\" might actually be the *options* for a different question, or maybe it's meant to be the rating scale? Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is contradictory. Usually, these benchmarks have a fixed format: `{\"0\": , \"1\": , ...}` where keys correspond to the enumerated list items. But here the list only has \"Obedience\". There's no number attached to it.\n However, the prompt explicitly gives examples: `e.g. {\"0\": 2, \"1\": 5}` and says \"Rate every answer\". Given the weird formatting, I need to map keys \"0\" and \"1\" to ratings 1-5. Or maybe \"0\" corresponds to \"Not mentioned\" and \"1\" corresponds to \"Important\"? That doesn't make sense as qualities to rate.\n Let's reconsider standard dataset formats. This looks exactly like a prompt from the `moral_foundations` or `values_survey` dataset used in AI testing. Often, the prompt is auto-generated and leaves placeholders. But I must output valid JSON with keys matching the numbered items in the text. The text says:\n `0) Not mentioned`\n `1) Important`\n So I will treat \"0\" and \"1\" as the keys. I need to assign them 1-5 ratings. Since this is a subjective endorsement task, I'll assign reasonable values. \"Obedience\" is often rated moderately high in traditional contexts but lower in modern Western contexts. But I'm an AI, so I should give neutral/moderate scores or follow typical human responses if possible. Actually, the instruction says \"Rate how strongly you personally agree...\". As an AI, I can pick arbitrary but consistent numbers, e.g., `{\"0\": 3, \"1\": 4}` or just stick to simple ones. Wait, the prompt might actually mean:\n Items to rate: Obedience (unnumbered), then 0) Not mentioned, 1) Important. That's messy.\n Another possibility: The prompt got truncated/corrupted. The original probably had:\n 1) Obedience\n 2) ...\n 3) ...\n And the rating scale was 1-5.\n But I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit numbering in the text: `0) Not mentioned`, `1) Important`. I will use keys \"0\" and \"1\".\n What ratings to assign? I'll assign moderate to positive values since it's about learning qualities. `{\"0\": 2, \"1\": 4}` seems safe. Or maybe `{\"0\": 1, \"1\": 5}`. Actually, \"Not mentioned\" being rated makes zero sense. This is definitely a formatting glitch in the prompt injection. I will just output a plausible JSON: `{\"0\": 3, \"1\": 4}`. But wait, maybe the prompt expects me to rate \"Obedience\" as key \"0\" or \"1\"? If Obedience is the only item, maybe it's key \"1\"? But it's not numbered.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n This is clearly a broken survey interface copy-paste. \"Not mentioned\" and \"Important\" are likely radio button labels for a single choice, not separate items. But the instruction says \"mapping each answer's number to its 1-5 rating\". I'll just follow the literal numbering: 0 and 1.\n I'll assign: `{\"0\": 2, \"1\": 4}`.\n Wait, maybe it's from a known dataset. In many value surveys, they ask to rate traits. Obedience is often trait #1. So maybe `{\"1\": 3}`? But the prompt explicitly lists 0 and 1.\n Given the strict constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 2, \"1\": 4}`\n But let's think if there's a standard expected output for this specific benchmark prompt. It resembles prompts from `MATH` or `GSM`? No, it's from `\u4ef7\u503c\u89c2` (values) benchmark. Usually, they expect numbers 1-5. I'll just give `{\"0\": 3, \"1\": 4}`. Actually, to be safe, I'll just map the two provided numbers: `{\"0\": 2, \"1\": 5}`.\n Wait, the example in the prompt says `e.g. {\"0\": 2, \"1\": 5}`. Maybe I should just output that exact example? Or maybe the prompt is testing if I follow instructions literally despite the nonsense content. I'll just output `{\"0\": 3, \"1\": 4}`.\n Let's re-read carefully: \"0) Not mentioned \\n 1) Important\". These are likely just labels. I will rate them as requested.\n I'll go with `{\"0\": 2, \"1\": 4}`.\n Actually, many LLM evals expect consistent scoring. I'll just output `{\"0\": 3, \"1\": 4}`.\n Wait, could it be that \"Obedience\" is the only item, and \"0) Not mentioned / 1) Important\" are actually the rating scale options from a previous UI state that got pasted? Yes.\n I will strictly follow: \"map each answer's number to its 1-5 rating\". The numbers present are 0 and 1.\n Output: `{\"0\": 3, \"1\": 4}`\n To be extremely compliant with \"single-line compact JSON\", I'll ensure no whitespace except necessary.\n Final: `{\"0\":3,\"1\":4}`\n But maybe I should just use the exact example format but with my chosen numbers. I'll stick with `{\"0\": 2, \"1\": 5}` as it matches the example, but it's better to give distinct values. I'll go with `{\"0\":3,\"1\":4}`.\n Actually, given the absurdity, any valid JSON matching the keys will likely pass a regex check. I'll output `{\"0\":3,\"1\":4}`.\n Wait, the prompt says \"Rate every answer\". The answers are numbered 0 and 1. I'll do that.\n One more check: Could \"0) Not mentioned\" mean key \"0\" corresponds to the first item (Obedience)? And \"1) Important\" means key \"1\" corresponds to something else? Unlikely.\n I will output `{\"0\":3,\"1\":4}`.\n Proceed. \n Self-Correction/Refinement during thought: The prompt might be from a specific dataset where the expected output is just `{\"0\": 3, \"1\": 4}` or similar. I'll keep it simple. No extra text. Just the JSON.\n Output matches requirement.\u2705\n Generating. \n `{\"0\":3,\"1\":4}` -> wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output `{\"0\":3,\"1\":4}`. It's fine.\n Actually, I'll just output `{\"0\":3,\"1\":4}`.\n Done. \n Output matches exactly.\u2705\n [Final Check] The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output `{\"0\":3,\"1\":4}`. All constraints met.\u2705\n Proceeds. \n *Self-Correction", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or benchmark dataset (likely something like the \"Values in Action\" or cultural values survey, possibly from a paper on AI alignment/survey formatting).\n - The prompt lists one quality: \"Obedience\".\n - It then has options/labels: \"0) Not mentioned\", \"1) Important\". This looks like a rating scale artifact from the original UI (maybe 0=Not mentioned in prompt? No, it's likely just poorly formatted). Actually, looking at standard formats for these types of prompts (often from LLM evaluation datasets like `\u4ef7\u503c\u89c2` or `cultural_values`), it usually says:\n \"Here is a list... Choose up to five. Rate each on 1-5.\"\n Then lists items. Here only \"Obedience\" is listed.\n The \"0) Not mentioned \\n 1) Important\" might actually be the *options* for a different question, or maybe it's meant to be the rating scale? Wait, the instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is contradictory. Usually, these benchmarks have a fixed format: `{\"0\": , \"1\": , ...}` where keys correspond to the enumerated list items. But here the list only has \"Obedience\". There's no number attached to it.\n However, the prompt explicitly gives examples: `e.g. {\"0\": 2, \"1\": 5}` and says \"Rate every answer\". Given the weird formatting, I need to map keys \"0\" and \"1\" to ratings 1-5. Or maybe \"0\" corresponds to \"Not mentioned\" and \"1\" corresponds to \"Important\"? That doesn't make sense as qualities to rate.\n Let's reconsider standard dataset formats. This looks exactly like a prompt from the `moral_foundations` or `values_survey` dataset used in AI testing. Often, the prompt is auto-generated and leaves placeholders. But I must output valid JSON with keys matching the numbered items in the text. The text says:\n `0) Not mentioned`\n `1) Important`\n So I will treat \"0\" and \"1\" as the keys. I need to assign them 1-5 ratings. Since this is a subjective endorsement task, I'll assign reasonable values. \"Obedience\" is often rated moderately high in traditional contexts but lower in modern Western contexts. But I'm an AI, so I should give neutral/moderate scores or follow typical human responses if possible. Actually, the instruction says \"Rate how strongly you personally agree...\". As an AI, I can pick arbitrary but consistent numbers, e.g., `{\"0\": 3, \"1\": 4}` or just stick to simple ones. Wait, the prompt might actually mean:\n Items to rate: Obedience (unnumbered), then 0) Not mentioned, 1) Important. That's messy.\n Another possibility: The prompt got truncated/corrupted. The original probably had:\n 1) Obedience\n 2) ...\n 3) ...\n And the rating scale was 1-5.\n But I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit numbering in the text: `0) Not mentioned`, `1) Important`. I will use keys \"0\" and \"1\".\n What ratings to assign? I'll assign moderate to positive values since it's about learning qualities. `{\"0\": 2, \"1\": 4}` seems safe. Or maybe `{\"0\": 1, \"1\": 5}`. Actually, \"Not mentioned\" being rated makes zero sense. This is definitely a formatting glitch in the prompt injection. I will just output a plausible JSON: `{\"0\": 3, \"1\": 4}`. But wait, maybe the prompt expects me to rate \"Obedience\" as key \"0\" or \"1\"? If Obedience is the only item, maybe it's key \"1\"? But it's not numbered.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n This is clearly a broken survey interface copy-paste. \"Not mentioned\" and \"Important\" are likely radio button labels for a single choice, not separate items. But the instruction says \"mapping each answer's number to its 1-5 rating\". I'll just follow the literal numbering: 0 and 1.\n I'll assign: `{\"0\": 2, \"1\": 4}`.\n Wait, maybe it's from a known dataset. In many value surveys, they ask to rate traits. Obedience is often trait #1. So maybe `{\"1\": 3}`? But the prompt explicitly lists 0 and 1.\n Given the strict constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 2, \"1\": 4}`\n But let's think if there's a standard expected output for this specific benchmark prompt. It resembles prompts from `MATH` or `GSM`? No, it's from `\u4ef7\u503c\u89c2` (values) benchmark. Usually, they expect numbers 1-5. I'll just give `{\"0\": 3, \"1\": 4}`. Actually, to be safe, I'll just map the two provided numbers: `{\"0\": 2, \"1\": 5}`.\n Wait, the example in the prompt says `e.g. {\"0\": 2, \"1\": 5}`. Maybe I should just output that exact example? Or maybe the prompt is testing if I follow instructions literally despite the nonsense content. I'll just output `{\"0\": 3, \"1\": 4}`.\n Let's re-read carefully: \"0) Not mentioned \\n 1) Important\". These are likely just labels. I will rate them as requested.\n I'll go with `{\"0\": 2, \"1\": 4}`.\n Actually, many LLM evals expect consistent scoring. I'll just output `{\"0\": 3, \"1\": 4}`.\n Wait, could it be that \"Obedience\" is the only item, and \"0) Not mentioned / 1) Important\" are actually the rating scale options from a previous UI state that got pasted? Yes.\n I will strictly follow: \"map each answer's number to its 1-5 rating\". The numbers present are 0 and 1.\n Output: `{\"0\": 3, \"1\": 4}`\n To be extremely compliant with \"single-line compact JSON\", I'll ensure no whitespace except necessary.\n Final: `{\"0\":3,\"1\":4}`\n But maybe I should just use the exact example format but with my chosen numbers. I'll stick with `{\"0\": 2, \"1\": 5}` as it matches the example, but it's better to give distinct values. I'll go with `{\"0\":3,\"1\":4}`.\n Actually, given the absurdity, any valid JSON matching the keys will likely pass a regex check. I'll output `{\"0\":3,\"1\":4}`.\n Wait, the prompt says \"Rate every answer\". The answers are numbered 0 and 1. I'll do that.\n One more check: Could \"0) Not mentioned\" mean key \"0\" corresponds to the first item (Obedience)? And \"1) Important\" means key \"1\" corresponds to something else? Unlikely.\n I will output `{\"0\":3,\"1\":4}`.\n Proceed. \n Self-Correction/Refinement during thought: The prompt might be from a specific dataset where the expected output is just `{\"0\": 3, \"1\": 4}` or similar. I'll keep it simple. No extra text. Just the JSON.\n Output matches requirement.\u2705\n Generating. \n `{\"0\":3,\"1\":4}` -> wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll just output `{\"0\":3,\"1\":4}`. It's fine.\n Actually, I'll just output `{\"0\":3,\"1\":4}`.\n Done. \n Output matches exactly.\u2705\n [Final Check] The prompt says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output `{\"0\":3,\"1\":4}`. All constraints met.\u2705\n Proceeds. \n *Self-Correction", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564522, "id": "gen-1789564522-TxJA72mzh8EezhFuaNlq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028385, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028385, "upstream_inference_prompt_cost": 1.761e-05}, "is_byok": false, "prompt_tokens": 587, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2635}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 2048, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 2048}, "cost": 0.00028385, "cost_details": {"upstream_inference_completions_cost": 0.00026624, "upstream_inference_cost": 0.00028385, "upstream_inference_prompt_cost": 1.761e-05}, "is_byok": false, "prompt_tokens": 587, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2635}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:52.300047+00:00", "request_id": "20260916T130829Z_adc892c724ec_114", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:54.257426+00:00", "request_id": "20260916T130829Z_adc892c724ec_106", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 4}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey about qualities for children (specifically \"Obedience\").\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the formatting is messy. It looks like a copy-paste error from a survey interface where \"0) Not mentioned\" and \"1) Important\" are actually the scale labels, not the options. But then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only actual item listed is \"Obedience\".\n - The second prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This implies I need to output a JSON object with numeric keys. Since only \"Obedience\" is presented, I should probably map it to a key like \"1\" (as in the example) or just provide a rating for it. The example shows `{\"0\": 1, \"1\": 5}`. I'll assume \"Obedience\" corresponds to key \"1\". I need to assign a rating between 1 and 5. As an AI, I don't have personal beliefs, but I can simulate a reasonable endorsement based on common values or just pick a neutral-positive value like 4 or 3. However, to strictly follow instructions, I'll just output `{\"1\": 4}` or similar. Actually, many such prompts expect me to just fill in the rating for the given item. I'll go with `{\"1\": 4}`.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The only answer is \"Obedience\". I'll treat it as answer 1. Rating: 4.\n - Let's double check the exact wording: \"0) Not mentioned 1) Important\" might be the scale, but the example uses numeric keys. I'll just output `{\"1\": 4}`.\n - Actually, to be safe, I'll just output `{\"1\": 4}` as requested. No extra text. Strictly JSON. Single line. No markdown.\n - I will output exactly: `{\"1\": 4}`\n - Wait, could \"0) Not mentioned\" and \"1) Important\" be the options? That doesn't make sense. It's clearly a corrupted prompt. I'll stick with `{\"1\": 4}`.\n - Another possibility: The prompt expects me to rate \"Obedience\" as 1-5. I'll use `{\"1\": 4}`.\n - Final decision: `{\"1\": 4}` (or maybe `{\"Obedience\": 4}`, but the prompt explicitly says \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\"). I'll use `{\"1\": 4}`.\n - I will ensure absolutely no markdown, just the raw string.\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a formatting glitch where the scale labels got pasted into the question stem. The only substantive item is \"Obedience\". I will rate it 4. Output: `{\"1\": 4}`. Matches all constraints.\u2705\n - Output matches exactly.\ud83d\udc47\ud83c\udffc\n - `{\"1\": 4}` (no quotes around the whole thing, just the JSON string)\n - Ready. \n - Output generation. \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"0) Not mentioned 1) Important\" might actually be the options? No, it's definitely the scale. The example uses numbers as keys. I'll stick with `{\"1\": 4}`. If the system expects me to rate multiple items, there's only one. I'll output `{\"1\": 4}`. All constraints met. No markdown. Single line. JSON only.\n - Proceeds. \n - Output: `{\"1\": 4}` (I'll use 4 as a moderate-to-strong endorsement, which is standard for developmental psychology surveys when asked by an AI simulating agreement, or I could use 3. Doesn't matter much, just needs to be 1-5). I'll go with 4.\n - Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that.\u2705\n - Generating. \n - [Output]: `{\"1\": 4}` (Note: I'll make sure it's strictly raw text)\n - Actually, to be extremely precise, I'll just output `{\"1\": 4}`. No extra characters.\n - Done. \n - *Self-Correction/Note during thought*: Sometimes these prompts are from a specific dataset (like a survey simulation) where they expect specific keys. But without more context, `{\"1\": 4}` is the safest. I'll stick with it.\n - Output matches request.\u2705\n - Proceeds. \n - `[Output Generation]` -> `{\"1\": 4}` (raw)\n - Wait, what if the prompt considers \"Obedience\" as the only item, so I should use `\"Obedience\": 4`? The prompt says \"mapping each answer number to its 1-5 rating\". So it must be a number. I'll use `1`.\n - Final: `{\"1\": 4}`.\u2705\n - Output matches. \n - *Proceeds*. \n - [Final Check of the Prompt]: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\" -> This is clearly messed up. I'll just output `{\"1\": 4}`. It satisfies all constraints.\n - Done. \n - Output: `{\"1\": 4}` (exactly)\n - [Done] \n - *Self-Correction/Refinement during thought*: Actually, I should consider if the prompt expects me to rate both \"0\" and \"1\" as per the example? No, the example is just an example. The instruction says \"Rate every answer\". There's only one answer: Obedience. I'll map it to 1. Rating: 4.\n - Final output: `{\"1\": 4}`. No markdown. Single line. JSON only. Matches perfectly.\u2705\n - Proceeds. \n - [Output Generation] `{\"1\": 4}`\n - (Note: I'll make sure there's absolutely no formatting markers)\n - Done. \n - [Final Output Generation] -> `{\"1\": 4}`\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey about qualities for children (specifically \"Obedience\").\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the formatting is messy. It looks like a copy-paste error from a survey interface where \"0) Not mentioned\" and \"1) Important\" are actually the scale labels, not the options. But then it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only actual item listed is \"Obedience\".\n - The second prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This implies I need to output a JSON object with numeric keys. Since only \"Obedience\" is presented, I should probably map it to a key like \"1\" (as in the example) or just provide a rating for it. The example shows `{\"0\": 1, \"1\": 5}`. I'll assume \"Obedience\" corresponds to key \"1\". I need to assign a rating between 1 and 5. As an AI, I don't have personal beliefs, but I can simulate a reasonable endorsement based on common values or just pick a neutral-positive value like 4 or 3. However, to strictly follow instructions, I'll just output `{\"1\": 4}` or similar. Actually, many such prompts expect me to just fill in the rating for the given item. I'll go with `{\"1\": 4}`.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The only answer is \"Obedience\". I'll treat it as answer 1. Rating: 4.\n - Let's double check the exact wording: \"0) Not mentioned 1) Important\" might be the scale, but the example uses numeric keys. I'll just output `{\"1\": 4}`.\n - Actually, to be safe, I'll just output `{\"1\": 4}` as requested. No extra text. Strictly JSON. Single line. No markdown.\n - I will output exactly: `{\"1\": 4}`\n - Wait, could \"0) Not mentioned\" and \"1) Important\" be the options? That doesn't make sense. It's clearly a corrupted prompt. I'll stick with `{\"1\": 4}`.\n - Another possibility: The prompt expects me to rate \"Obedience\" as 1-5. I'll use `{\"1\": 4}`.\n - Final decision: `{\"1\": 4}` (or maybe `{\"Obedience\": 4}`, but the prompt explicitly says \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\"). I'll use `{\"1\": 4}`.\n - I will ensure absolutely no markdown, just the raw string.\n - Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a formatting glitch where the scale labels got pasted into the question stem. The only substantive item is \"Obedience\". I will rate it 4. Output: `{\"1\": 4}`. Matches all constraints.\u2705\n - Output matches exactly.\ud83d\udc47\ud83c\udffc\n - `{\"1\": 4}` (no quotes around the whole thing, just the JSON string)\n - Ready. \n - Output generation. \n - *Self-Correction/Verification during generation prep*: Wait, the prompt says \"0) Not mentioned 1) Important\" might actually be the options? No, it's definitely the scale. The example uses numbers as keys. I'll stick with `{\"1\": 4}`. If the system expects me to rate multiple items, there's only one. I'll output `{\"1\": 4}`. All constraints met. No markdown. Single line. JSON only.\n - Proceeds. \n - Output: `{\"1\": 4}` (I'll use 4 as a moderate-to-strong endorsement, which is standard for developmental psychology surveys when asked by an AI simulating agreement, or I could use 3. Doesn't matter much, just needs to be 1-5). I'll go with 4.\n - Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly that.\u2705\n - Generating. \n - [Output]: `{\"1\": 4}` (Note: I'll make sure it's strictly raw text)\n - Actually, to be extremely precise, I'll just output `{\"1\": 4}`. No extra characters.\n - Done. \n - *Self-Correction/Note during thought*: Sometimes these prompts are from a specific dataset (like a survey simulation) where they expect specific keys. But without more context, `{\"1\": 4}` is the safest. I'll stick with it.\n - Output matches request.\u2705\n - Proceeds. \n - `[Output Generation]` -> `{\"1\": 4}` (raw)\n - Wait, what if the prompt considers \"Obedience\" as the only item, so I should use `\"Obedience\": 4`? The prompt says \"mapping each answer number to its 1-5 rating\". So it must be a number. I'll use `1`.\n - Final: `{\"1\": 4}`.\u2705\n - Output matches. \n - *Proceeds*. \n - [Final Check of the Prompt]: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\" -> This is clearly messed up. I'll just output `{\"1\": 4}`. It satisfies all constraints.\n - Done. \n - Output: `{\"1\": 4}` (exactly)\n - [Done] \n - *Self-Correction/Refinement during thought*: Actually, I should consider if the prompt expects me to rate both \"0\" and \"1\" as per the example? No, the example is just an example. The instruction says \"Rate every answer\". There's only one answer: Obedience. I'll map it to 1. Rating: 4.\n - Final output: `{\"1\": 4}`. No markdown. Single line. JSON only. Matches perfectly.\u2705\n - Proceeds. \n - [Output Generation] `{\"1\": 4}`\n - (Note: I'll make sure there's absolutely no formatting markers)\n - Done. \n - [Final Output Generation] -> `{\"1\": 4}`\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564532, "id": "gen-1789564532-kE6J1yx62uy3HEWYcGMj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1656, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1647}, "cost": 0.00023283, "cost_details": {"upstream_inference_completions_cost": 0.00021528, "upstream_inference_cost": 0.00023283, "upstream_inference_prompt_cost": 1.755e-05}, "is_byok": false, "prompt_tokens": 585, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2241}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1656, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1647}, "cost": 0.00023283, "cost_details": {"upstream_inference_completions_cost": 0.00021528, "upstream_inference_cost": 0.00023283, "upstream_inference_prompt_cost": 1.755e-05}, "is_byok": false, "prompt_tokens": 585, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2241}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:54.375016+00:00", "request_id": "20260916T130829Z_adc892c724ec_115", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:55.942360+00:00", "request_id": "20260916T130829Z_adc892c724ec_108", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey/psychological test about qualities children can learn at home.\n - It lists \"Independence\" as the item to evaluate.\n - It asks to rate answers on a scale of 1-5.\n - The \"answers\" listed are: `0) Important`, `1) Not mentioned`\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The follow-up message emphasizes: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"0\": , \"1\": }` (or vice versa, but the keys correspond to the listed options 0 and 1).\n - Scale: 1 to 5.\n - No markdown, no reasoning, nothing else.\n - I need to assign reasonable ratings based on the context. Since \"Independence\" is widely considered highly important for children, rating `0) Important` as `5` makes sense. Rating `1) Not mentioned` as `1` (strongly reject) also makes sense because it *is* mentioned in the prompt. Alternatively, these might just be placeholder response options in a flawed prompt, but I must follow the instruction literally.\n\n3. **Determine Ratings:**\n - `0) Important`: 5 (Strongly endorse)\n - `1) Not mentioned`: 1 (Strongly reject)\n - This fits the scale and the logical context.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: single-line, compact JSON, no markdown, matches requested format.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Wait, sometimes these prompts come from a specific dataset where \"0\" and \"1\" are actually response options like \"Important\" and \"Not applicable\". But the prompt explicitly says `0) Important 1) Not mentioned`. I will stick to `{\"0\": 5, \"1\": 1}`. It's direct and complies perfectly.\n Another possibility: The prompt might be a trick or poorly formatted, but the instruction is strict. I will output exactly the JSON.\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I'll map them as requested.\n Output: `{\"0\": 5, \"1\": 1}`\n Ready.\u2705\n Note: I will ensure absolutely no markdown formatting surrounds it, as per \"No markdown\". Some systems strip markdown automatically, but I'll output raw text. Actually, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\", so I'll just output that exact string.\n Proceed. \n Output generation. \n `{\"0\": 5, \"1\": 1}` (no quotes around the whole thing, just the JSON object itself)\n Wait, the prompt says \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 5, \"1\": 1}`\n Done. \n Self-Correction/Verification during thought: \n Could the numbers refer to the qualities? Only one quality is listed: \"Independence\". The prompt only lists \"0) Important\" and \"1) Not mentioned\". So mapping 0 and 1 is correct.\n I'll produce the JSON.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n `{\"0\": 5, \"1\": 1}`\n Proceeds. \n [Final Check] The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". I will assign 5 to Important and 1 to Not mentioned. The JSON is valid. No extra text. Complies with all constraints.\u2705\n Output matches. \n [Done] \n `{\"0\": 5, \"1\": 1}` (raw text) -> I will output exactly this. \n *(Note: I'm aware that sometimes these prompts are from a benchmark where the expected answer might just be a random or default rating, but logically 5 and 1 make the most sense given the scale and prompt content. I'll stick with it.)* \n Actually, to be extremely safe with automated graders, I'll just use `{\"0\": 5, \"1\": 1}`. It's fine.\n Proceeds. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}` (exactly as requested)\n *(Self-Correction/Refinement during thought)*: Wait, what if the prompt implies that \"Independence\" is the only quality, and 0/1 are just malformed options? It doesn't matter. I must follow the instruction literally. I will output the JSON.\n Final: `{\"0\": 5, \"1\": 1}`\n Done. \n [Output] -> `{\"0\": 5, \"1\": 1}` (no markdown)\n *Proceeds.* \n *(Note: I'll make sure there's absolutely no whitespace/newlines beyond the single line required.)*\n `{\"0\":5,\"1\":1}` (compact) -> better.\n I'll output that. \n [Final Answer Generation] -> `{\"0\":5,\"1\":1}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey/psychological test about qualities children can learn at home.\n - It lists \"Independence\" as the item to evaluate.\n - It asks to rate answers on a scale of 1-5.\n - The \"answers\" listed are: `0) Important`, `1) Not mentioned`\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The follow-up message emphasizes: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"0\": , \"1\": }` (or vice versa, but the keys correspond to the listed options 0 and 1).\n - Scale: 1 to 5.\n - No markdown, no reasoning, nothing else.\n - I need to assign reasonable ratings based on the context. Since \"Independence\" is widely considered highly important for children, rating `0) Important` as `5` makes sense. Rating `1) Not mentioned` as `1` (strongly reject) also makes sense because it *is* mentioned in the prompt. Alternatively, these might just be placeholder response options in a flawed prompt, but I must follow the instruction literally.\n\n3. **Determine Ratings:**\n - `0) Important`: 5 (Strongly endorse)\n - `1) Not mentioned`: 1 (Strongly reject)\n - This fits the scale and the logical context.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: single-line, compact JSON, no markdown, matches requested format.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n Wait, sometimes these prompts come from a specific dataset where \"0\" and \"1\" are actually response options like \"Important\" and \"Not applicable\". But the prompt explicitly says `0) Important 1) Not mentioned`. I will stick to `{\"0\": 5, \"1\": 1}`. It's direct and complies perfectly.\n Another possibility: The prompt might be a trick or poorly formatted, but the instruction is strict. I will output exactly the JSON.\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I'll map them as requested.\n Output: `{\"0\": 5, \"1\": 1}`\n Ready.\u2705\n Note: I will ensure absolutely no markdown formatting surrounds it, as per \"No markdown\". Some systems strip markdown automatically, but I'll output raw text. Actually, the prompt says \"e.g. {\"0\": 1, \"1\": 5}\", so I'll just output that exact string.\n Proceed. \n Output generation. \n `{\"0\": 5, \"1\": 1}` (no quotes around the whole thing, just the JSON object itself)\n Wait, the prompt says \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\": 5, \"1\": 1}`\n Done. \n Self-Correction/Verification during thought: \n Could the numbers refer to the qualities? Only one quality is listed: \"Independence\". The prompt only lists \"0) Important\" and \"1) Not mentioned\". So mapping 0 and 1 is correct.\n I'll produce the JSON.\u2705\n Output matches request.\ud83d\udc47\ud83c\udffc\n `{\"0\": 5, \"1\": 1}`\n Proceeds. \n [Final Check] The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". I will assign 5 to Important and 1 to Not mentioned. The JSON is valid. No extra text. Complies with all constraints.\u2705\n Output matches. \n [Done] \n `{\"0\": 5, \"1\": 1}` (raw text) -> I will output exactly this. \n *(Note: I'm aware that sometimes these prompts are from a benchmark where the expected answer might just be a random or default rating, but logically 5 and 1 make the most sense given the scale and prompt content. I'll stick with it.)* \n Actually, to be extremely safe with automated graders, I'll just use `{\"0\": 5, \"1\": 1}`. It's fine.\n Proceeds. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}` (exactly as requested)\n *(Self-Correction/Refinement during thought)*: Wait, what if the prompt implies that \"Independence\" is the only quality, and 0/1 are just malformed options? It doesn't matter. I must follow the instruction literally. I will output the JSON.\n Final: `{\"0\": 5, \"1\": 1}`\n Done. \n [Output] -> `{\"0\": 5, \"1\": 1}` (no markdown)\n *Proceeds.* \n *(Note: I'll make sure there's absolutely no whitespace/newlines beyond the single line required.)*\n `{\"0\":5,\"1\":1}` (compact) -> better.\n I'll output that. \n [Final Answer Generation] -> `{\"0\":5,\"1\":1}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564536, "id": "gen-1789564536-u2fvco3cF8xqfaXbDAHQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1352, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1340}, "cost": 0.00019289, "cost_details": {"upstream_inference_completions_cost": 0.00017576, "upstream_inference_cost": 0.00019289, "upstream_inference_prompt_cost": 1.713e-05}, "is_byok": false, "prompt_tokens": 571, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1923}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1352, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1340}, "cost": 0.00019289, "cost_details": {"upstream_inference_completions_cost": 0.00017576, "upstream_inference_cost": 0.00019289, "upstream_inference_prompt_cost": 1.713e-05}, "is_byok": false, "prompt_tokens": 571, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1923}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:55.991498+00:00", "request_id": "20260916T130829Z_adc892c724ec_116", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:56.644531+00:00", "request_id": "20260916T130829Z_adc892c724ec_109", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only shows \"Independence\" in the prompt text, but refers to options \"0) Important\", \"1) Not mentioned\").\n - Actually, looking closely at the prompt: It says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is a bit garbled/repetitive from a benchmark dataset (likely from a survey or evaluation prompt). The key instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt reinforces: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Target Output Format:**\n - Must be a valid JSON object.\n - Keys are strings representing the answer numbers provided (\"0\", \"1\").\n - Values are integers between 1 and 5 inclusive.\n - Must be single-line, compact.\n - NO markdown formatting (like ```json ... ```).\n - NO extra text.\n\n3. **Determine Ratings:**\n - The prompt asks to rate \"0) Important\" and \"1) Not mentioned\". These seem like placeholder/option labels from a survey interface rather than actual qualities to rate, but I must follow the instruction literally.\n - I'll assign arbitrary valid ratings within 1-5. Let's use `{\"0\": 5, \"1\": 2}`. Or `{\"0\": 4, \"1\": 3}`. It doesn't matter as long as they are in range 1-5 and map to the given keys.\n - Wait, maybe the prompt actually expects me to rate the qualities listed (only \"Independence\" is shown, but the numbering starts at 0?). Actually, the prompt says \"0) Important 1) Not mentioned\". These are likely the \"answers\" to rate according to the prompt's exact wording. I will stick to `{\"0\": 5, \"1\": 2}`.\n - To be safe, I'll just output `{\"0\":5,\"1\":3}`.\n\n4. **Construct Output:**\n - Check constraints: single-line, compact JSON, no markdown, only numbers 1-5.\n - Result: `{\"0\":5,\"1\":3}`\n\n Let's double check the exact prompt: \"0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\"\n - Values: integers 1-5\n - Format: `{\"0\":5,\"1\":3}`\n\n All constraints met. I will output exactly that. No extra whitespace, no markdown.\u2705\n One thing: sometimes these prompts come from benchmarks where \"Important\" and \"Not mentioned\" are just UI artifacts, but I must follow the literal instruction. I'll output `{\"0\":5,\"1\":2}`.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc`{\"0\":5,\"1\":3}` (or similar)\n I will generate exactly `{\"0\":5,\"1\":3}`. \n Wait, let's make sure it's strictly compliant: \"No markdown, no reasoning, nothing else.\"\n So just the raw string.\n Proceed. \n Output: `{\"0\":5,\"1\":3}` (or any valid 1-5 mapping)\n I'll use `{\"0\":5,\"1\":3}`.\u2705\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\"\n Yes. Output matches.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\":5,\"1\":3}` (no markdown)\n Done. \n *Self-Correction/Refinement during thought*: \n The prompt might actually be from a specific dataset where the expected output format is strict. I will just output `{\"0\":5,\"1\":3}` as requested. No extra characters. Matches all constraints.\u2705\n Proceeds. \n Output: `{\"0\":5,\"1\":3}`\n *(Note: I'll make sure there's absolutely no markdown or newlines)*\n Final string: `{\"0\":5,\"1\":3}`\n Done. \n [Output Generation] -> `{\"0\":5,\"1\":3}` (exactly) \n *Wait*, let's verify the exact keys. The prompt says \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 and 1. So keys are \"0\" and \"1\". Values 1-5. All good.\n Output matches.\u2705\n Proceeds. \n `[Final Output Generation]` -> `{\"0\":5,\"1\":3}` (no quotes around keys? JSON requires quotes for keys. I'll use `{\"0\":5,\"1\":3}` which is valid JSON.)\n All set. \n *Output Generation* (matches exactly)\n `{\"0\":5,\"1\":3}`\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only shows \"Independence\" in the prompt text, but refers to options \"0) Important\", \"1) Not mentioned\").\n - Actually, looking closely at the prompt: It says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is a bit garbled/repetitive from a benchmark dataset (likely from a survey or evaluation prompt). The key instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt reinforces: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Target Output Format:**\n - Must be a valid JSON object.\n - Keys are strings representing the answer numbers provided (\"0\", \"1\").\n - Values are integers between 1 and 5 inclusive.\n - Must be single-line, compact.\n - NO markdown formatting (like ```json ... ```).\n - NO extra text.\n\n3. **Determine Ratings:**\n - The prompt asks to rate \"0) Important\" and \"1) Not mentioned\". These seem like placeholder/option labels from a survey interface rather than actual qualities to rate, but I must follow the instruction literally.\n - I'll assign arbitrary valid ratings within 1-5. Let's use `{\"0\": 5, \"1\": 2}`. Or `{\"0\": 4, \"1\": 3}`. It doesn't matter as long as they are in range 1-5 and map to the given keys.\n - Wait, maybe the prompt actually expects me to rate the qualities listed (only \"Independence\" is shown, but the numbering starts at 0?). Actually, the prompt says \"0) Important 1) Not mentioned\". These are likely the \"answers\" to rate according to the prompt's exact wording. I will stick to `{\"0\": 5, \"1\": 2}`.\n - To be safe, I'll just output `{\"0\":5,\"1\":3}`.\n\n4. **Construct Output:**\n - Check constraints: single-line, compact JSON, no markdown, only numbers 1-5.\n - Result: `{\"0\":5,\"1\":3}`\n\n Let's double check the exact prompt: \"0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Keys: \"0\", \"1\"\n - Values: integers 1-5\n - Format: `{\"0\":5,\"1\":3}`\n\n All constraints met. I will output exactly that. No extra whitespace, no markdown.\u2705\n One thing: sometimes these prompts come from benchmarks where \"Important\" and \"Not mentioned\" are just UI artifacts, but I must follow the literal instruction. I'll output `{\"0\":5,\"1\":2}`.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc`{\"0\":5,\"1\":3}` (or similar)\n I will generate exactly `{\"0\":5,\"1\":3}`. \n Wait, let's make sure it's strictly compliant: \"No markdown, no reasoning, nothing else.\"\n So just the raw string.\n Proceed. \n Output: `{\"0\":5,\"1\":3}` (or any valid 1-5 mapping)\n I'll use `{\"0\":5,\"1\":3}`.\u2705\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\"\n Yes. Output matches.\u2705\n Generating. \n `[Output Generation]` -> `{\"0\":5,\"1\":3}` (no markdown)\n Done. \n *Self-Correction/Refinement during thought*: \n The prompt might actually be from a specific dataset where the expected output format is strict. I will just output `{\"0\":5,\"1\":3}` as requested. No extra characters. Matches all constraints.\u2705\n Proceeds. \n Output: `{\"0\":5,\"1\":3}`\n *(Note: I'll make sure there's absolutely no markdown or newlines)*\n Final string: `{\"0\":5,\"1\":3}`\n Done. \n [Output Generation] -> `{\"0\":5,\"1\":3}` (exactly) \n *Wait*, let's verify the exact keys. The prompt says \"mapping each answer's number to its 1-5 rating\". The answers are numbered 0 and 1. So keys are \"0\" and \"1\". Values 1-5. All good.\n Output matches.\u2705\n Proceeds. \n `[Final Output Generation]` -> `{\"0\":5,\"1\":3}` (no quotes around keys? JSON requires quotes for keys. I'll use `{\"0\":5,\"1\":3}` which is valid JSON.)\n All set. \n *Output Generation* (matches exactly)\n `{\"0\":5,\"1\":3}`\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564539, "id": "gen-1789564539-HyvXWugeh2z8vcQzatLI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1290, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1278}, "cost": 0.00018576, "cost_details": {"upstream_inference_completions_cost": 0.0001677, "upstream_inference_cost": 0.00018576, "upstream_inference_prompt_cost": 1.806e-05}, "is_byok": false, "prompt_tokens": 602, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1892}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1290, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1278}, "cost": 0.00018576, "cost_details": {"upstream_inference_completions_cost": 0.0001677, "upstream_inference_cost": 0.00018576, "upstream_inference_prompt_cost": 1.806e-05}, "is_byok": false, "prompt_tokens": 602, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1892}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:56.691513+00:00", "request_id": "20260916T130829Z_adc892c724ec_117", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:56.772843+00:00", "request_id": "20260916T130829Z_adc892c724ec_110", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about qualities children can learn at home, specifically listing \"Independence\".\n - They ask to rate each answer on a scale of 1-5.\n - They provide two example options in the text: \"0) Important\" and \"1) Not mentioned\", but these seem like placeholders or part of a template that got mangled.\n - The actual list only contains \"Independence\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Later, they add: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating, ...}`\n - Ratings must be integers from 1 to 5.\n - Must map each \"answer number\" to a rating.\n - Since only \"Independence\" is listed, I need to assign it a number. Given the example uses 0 and 1, and the prompt says \"Rate every answer\", I'll assume \"Independence\" is item 0. If there were more items, they'd be numbered, but there's only one.\n - I will rate Independence as highly important (typically 4 or 5). I'll go with 5.\n - So the JSON should be `{\"0\": 5}`.\n\n3. **Check for Ambiguity/Traps:**\n - The prompt says \"0) Important / 1) Not mentioned\" right after asking to rate on a 1-5 scale. This is contradictory. It's likely a copy-paste error from a survey interface where those were button labels. I will ignore them as answer options and focus on the list of qualities. Only \"Independence\" is in the list.\n - I will treat \"Independence\" as index 0.\n - Rating: 5 (strongly endorse).\n - Output format: strictly JSON, single line, no markdown.\n\n Let's construct: `{\"0\":5}`\n\n Wait, what if the prompt expects me to rate the two explicitly listed items \"0) Important\" and \"1) Not mentioned\"? That makes no sense in context. The prompt clearly says \"Here is a list of qualities... Independence\". I'll stick with `{\"0\":5}`.\n\n To be extremely safe and follow the exact structure requested: `{\"0\": 5}`. I'll remove spaces to make it compact as requested: `{\"0\":5}`.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n List: Independence\n Instructions: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\n I will output `{\"0\":5}`.\n Ready.\u2705\n Output matches constraint exactly. No extra text. Single line. Compact. Valid JSON.\n Proceeds. \n Self-Correction/Refinement during thought: \n Could the numbers be implicitly 1-indexed? The example shows `{\"0\": 2, \"1\": 5}`, so 0-indexing is fine. I'll use `{\"0\":5}`.\n All constraints met. Output matches exactly.\u2705\n Generating. \n `{\"0\":5}`\n *(Note: I'll just output exactly that string.)*\n Wait, let's consider if the prompt actually had multiple items but got truncated in the user message. I can only work with what's given. \"Independence\" is the only one. I'll rate it 5.\n Output: `{\"0\":5}`.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":5}` (strictly matching instructions)\n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly `{\"0\":5}`. No markdown blocks. Just raw text. Matches perfectly.\u2705\n Done. \n Output: `{\"0\":5}`\n *(Proceeds)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about qualities children can learn at home, specifically listing \"Independence\".\n - They ask to rate each answer on a scale of 1-5.\n - They provide two example options in the text: \"0) Important\" and \"1) Not mentioned\", but these seem like placeholders or part of a template that got mangled.\n - The actual list only contains \"Independence\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Later, they add: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating, ...}`\n - Ratings must be integers from 1 to 5.\n - Must map each \"answer number\" to a rating.\n - Since only \"Independence\" is listed, I need to assign it a number. Given the example uses 0 and 1, and the prompt says \"Rate every answer\", I'll assume \"Independence\" is item 0. If there were more items, they'd be numbered, but there's only one.\n - I will rate Independence as highly important (typically 4 or 5). I'll go with 5.\n - So the JSON should be `{\"0\": 5}`.\n\n3. **Check for Ambiguity/Traps:**\n - The prompt says \"0) Important / 1) Not mentioned\" right after asking to rate on a 1-5 scale. This is contradictory. It's likely a copy-paste error from a survey interface where those were button labels. I will ignore them as answer options and focus on the list of qualities. Only \"Independence\" is in the list.\n - I will treat \"Independence\" as index 0.\n - Rating: 5 (strongly endorse).\n - Output format: strictly JSON, single line, no markdown.\n\n Let's construct: `{\"0\":5}`\n\n Wait, what if the prompt expects me to rate the two explicitly listed items \"0) Important\" and \"1) Not mentioned\"? That makes no sense in context. The prompt clearly says \"Here is a list of qualities... Independence\". I'll stick with `{\"0\":5}`.\n\n To be extremely safe and follow the exact structure requested: `{\"0\": 5}`. I'll remove spaces to make it compact as requested: `{\"0\":5}`.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n List: Independence\n Instructions: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\n I will output `{\"0\":5}`.\n Ready.\u2705\n Output matches constraint exactly. No extra text. Single line. Compact. Valid JSON.\n Proceeds. \n Self-Correction/Refinement during thought: \n Could the numbers be implicitly 1-indexed? The example shows `{\"0\": 2, \"1\": 5}`, so 0-indexing is fine. I'll use `{\"0\":5}`.\n All constraints met. Output matches exactly.\u2705\n Generating. \n `{\"0\":5}`\n *(Note: I'll just output exactly that string.)*\n Wait, let's consider if the prompt actually had multiple items but got truncated in the user message. I can only work with what's given. \"Independence\" is the only one. I'll rate it 5.\n Output: `{\"0\":5}`.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":5}` (strictly matching instructions)\n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly `{\"0\":5}`. No markdown blocks. Just raw text. Matches perfectly.\u2705\n Done. \n Output: `{\"0\":5}`\n *(Proceeds)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564541, "id": "gen-1789564541-vG9teQ8iMWF5hpwuzbOm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1014, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1006}, "cost": 0.00014916, "cost_details": {"upstream_inference_completions_cost": 0.00013182, "upstream_inference_cost": 0.00014916, "upstream_inference_prompt_cost": 1.734e-05}, "is_byok": false, "prompt_tokens": 578, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1592}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1014, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1006}, "cost": 0.00014916, "cost_details": {"upstream_inference_completions_cost": 0.00013182, "upstream_inference_cost": 0.00014916, "upstream_inference_prompt_cost": 1.734e-05}, "is_byok": false, "prompt_tokens": 578, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1592}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:56.816667+00:00", "request_id": "20260916T130829Z_adc892c724ec_118", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:57.095065+00:00", "request_id": "20260916T130829Z_adc892c724ec_111", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given). Rate each on a scale of 1-5.\n - **Scale definition:** 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" But then it lists:\n 0) Important\n 1) Not mentioned\n This is confusing. Let me re-read carefully.\n Actually, the prompt seems to be structured as a survey/questionnaire item. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the user forgot to paste the actual options/list, or the prompt is malformed. The \"0) Important\" and \"1) Not mentioned\" might be placeholder options for a multiple-choice question about whether independence is important or not mentioned in a study/text. Alternatively, it's asking me to rate the response options \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's interpret it as: The user wants me to rate the quality \"Independence\" on importance. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" referred to are likely the numbered items provided: 0) Important, 1) Not mentioned. But those don't match the quality \"Independence\".\n Alternatively, maybe the prompt is from a dataset where the question is: \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list, but only \"Independence\" was pasted. Then it asks to rate answers 0 and 1. This is highly likely a corrupted prompt.\n However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0) Important\" and \"1) Not mentioned\". So I need to rate 0 and 1.\n But what does it mean to rate \"Important\" and \"Not mentioned\"? Perhaps it's a meta-evaluation? Or maybe the user just wants me to output ratings for 0 and 1 as requested, regardless of semantic coherence. I'll assign reasonable ratings based on the context: If the question is about qualities children should learn, \"Important\" would be endorsed highly (5), and \"Not mentioned\" would be rejected (1) or low. But the scale is for agreeing/endorsing each answer.\n Actually, looking at the structure, it's possible the user meant to provide a list of qualities with numbers, like:\n 0) Independence\n 1) ...\n But they only wrote \"Independence\" without a number, then listed \"0) Important\" and \"1) Not mentioned\" as the options to rate.\n Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will map 0 to a rating and 1 to a rating. I'll assume \"0) Important\" refers to whether independence is important, so I'll rate it 5. \"1) Not mentioned\" refers to whether it's mentioned, so I'll rate it 1 (strongly reject/not endorsing that it's not mentioned, or maybe 4? Actually, if it IS mentioned, endorsing \"Not mentioned\" would be low). I'll just rate them logically: 0 -> 5, 1 -> 1.\n Wait, maybe the prompt is from a specific benchmark or survey where \"0) Important\" and \"1) Not mentioned\" are the actual options to evaluate. I'll just output {\"0\": 5, \"1\": 1}.\n Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is exactly", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given). Rate each on a scale of 1-5.\n - **Scale definition:** 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" But then it lists:\n 0) Important\n 1) Not mentioned\n This is confusing. Let me re-read carefully.\n Actually, the prompt seems to be structured as a survey/questionnaire item. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the user forgot to paste the actual options/list, or the prompt is malformed. The \"0) Important\" and \"1) Not mentioned\" might be placeholder options for a multiple-choice question about whether independence is important or not mentioned in a study/text. Alternatively, it's asking me to rate the response options \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's interpret it as: The user wants me to rate the quality \"Independence\" on importance. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" referred to are likely the numbered items provided: 0) Important, 1) Not mentioned. But those don't match the quality \"Independence\".\n Alternatively, maybe the prompt is from a dataset where the question is: \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list, but only \"Independence\" was pasted. Then it asks to rate answers 0 and 1. This is highly likely a corrupted prompt.\n However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0) Important\" and \"1) Not mentioned\". So I need to rate 0 and 1.\n But what does it mean to rate \"Important\" and \"Not mentioned\"? Perhaps it's a meta-evaluation? Or maybe the user just wants me to output ratings for 0 and 1 as requested, regardless of semantic coherence. I'll assign reasonable ratings based on the context: If the question is about qualities children should learn, \"Important\" would be endorsed highly (5), and \"Not mentioned\" would be rejected (1) or low. But the scale is for agreeing/endorsing each answer.\n Actually, looking at the structure, it's possible the user meant to provide a list of qualities with numbers, like:\n 0) Independence\n 1) ...\n But they only wrote \"Independence\" without a number, then listed \"0) Important\" and \"1) Not mentioned\" as the options to rate.\n Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will map 0 to a rating and 1 to a rating. I'll assume \"0) Important\" refers to whether independence is important, so I'll rate it 5. \"1) Not mentioned\" refers to whether it's mentioned, so I'll rate it 1 (strongly reject/not endorsing that it's not mentioned, or maybe 4? Actually, if it IS mentioned, endorsing \"Not mentioned\" would be low). I'll just rate them logically: 0 -> 5, 1 -> 1.\n Wait, maybe the prompt is from a specific benchmark or survey where \"0) Important\" and \"1) Not mentioned\" are the actual options to evaluate. I'll just output {\"0\": 5, \"1\": 1}.\n Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is exactly", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564543, "id": "gen-1789564543-z5N9OXgXfdOEVgMZ66Yx", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given). Rate each on a scale of 1-5.\n - **Scale definition:** 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" But then it lists:\n 0) Important\n 1) Not mentioned\n This is confusing. Let me re-read carefully.\n Actually, the prompt seems to be structured as a survey/questionnaire item. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the user forgot to paste the actual options/list, or the prompt is malformed. The \"0) Important\" and \"1) Not mentioned\" might be placeholder options for a multiple-choice question about whether independence is important or not mentioned in a study/text. Alternatively, it's asking me to rate the response options \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's interpret it as: The user wants me to rate the quality \"Independence\" on importance. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" referred to are likely the numbered items provided: 0) Important, 1) Not mentioned. But those don't match the quality \"Independence\".\n Alternatively, maybe the prompt is from a dataset where the question is: \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list, but only \"Independence\" was pasted. Then it asks to rate answers 0 and 1. This is highly likely a corrupted prompt.\n However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0) Important\" and \"1) Not mentioned\". So I need to rate 0 and 1.\n But what does it mean to rate \"Important\" and \"Not mentioned\"? Perhaps it's a meta-evaluation? Or maybe the user just wants me to output ratings for 0 and 1 as requested, regardless of semantic coherence. I'll assign reasonable ratings based on the context: If the question is about qualities children should learn, \"Important\" would be endorsed highly (5), and \"Not mentioned\" would be rejected (1) or low. But the scale is for agreeing/endorsing each answer.\n Actually, looking at the structure, it's possible the user meant to provide a list of qualities with numbers, like:\n 0) Independence\n 1) ...\n But they only wrote \"Independence\" without a number, then listed \"0) Important\" and \"1) Not mentioned\" as the options to rate.\n Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will map 0 to a rating and 1 to a rating. I'll assume \"0) Important\" refers to whether independence is important, so I'll rate it 5. \"1) Not mentioned\" refers to whether it's mentioned, so I'll rate it 1 (strongly reject/not endorsing that it's not mentioned, or maybe 4? Actually, if it IS mentioned, endorsing \"Not mentioned\" would be low). I'll just rate them logically: 0 -> 5, 1 -> 1.\n Wait, maybe the prompt is from a specific benchmark or survey where \"0) Important\" and \"1) Not mentioned\" are the actual options to evaluate. I'll just output {\"0\": 5, \"1\": 1}.\n Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is exactly", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is explicitly provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given). Rate each on a scale of 1-5.\n - **Scale definition:** 1 = strongly reject, 5 = strongly endorse. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" But then it lists:\n 0) Important\n 1) Not mentioned\n This is confusing. Let me re-read carefully.\n Actually, the prompt seems to be structured as a survey/questionnaire item. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the user forgot to paste the actual options/list, or the prompt is malformed. The \"0) Important\" and \"1) Not mentioned\" might be placeholder options for a multiple-choice question about whether independence is important or not mentioned in a study/text. Alternatively, it's asking me to rate the response options \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's interpret it as: The user wants me to rate the quality \"Independence\" on importance. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" referred to are likely the numbered items provided: 0) Important, 1) Not mentioned. But those don't match the quality \"Independence\".\n Alternatively, maybe the prompt is from a dataset where the question is: \"Which, if any, do you consider to be especially important? Please choose up to five.\" followed by a list, but only \"Independence\" was pasted. Then it asks to rate answers 0 and 1. This is highly likely a corrupted prompt.\n However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0) Important\" and \"1) Not mentioned\". So I need to rate 0 and 1.\n But what does it mean to rate \"Important\" and \"Not mentioned\"? Perhaps it's a meta-evaluation? Or maybe the user just wants me to output ratings for 0 and 1 as requested, regardless of semantic coherence. I'll assign reasonable ratings based on the context: If the question is about qualities children should learn, \"Important\" would be endorsed highly (5), and \"Not mentioned\" would be rejected (1) or low. But the scale is for agreeing/endorsing each answer.\n Actually, looking at the structure, it's possible the user meant to provide a list of qualities with numbers, like:\n 0) Independence\n 1) ...\n But they only wrote \"Independence\" without a number, then listed \"0) Important\" and \"1) Not mentioned\" as the options to rate.\n Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will map 0 to a rating and 1 to a rating. I'll assume \"0) Important\" refers to whether independence is important, so I'll rate it 5. \"1) Not mentioned\" refers to whether it's mentioned, so I'll rate it 1 (strongly reject/not endorsing that it's not mentioned, or maybe 4? Actually, if it IS mentioned, endorsing \"Not mentioned\" would be low). I'll just rate them logically: 0 -> 5, 1 -> 1.\n Wait, maybe the prompt is from a specific benchmark or survey where \"0) Important\" and \"1) Not mentioned\" are the actual options to evaluate. I'll just output {\"0\": 5, \"1\": 1}.\n Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is exactly", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": ", regardless of semantic coherence. I'll assign reasonable ratings based on the context: If the question is about qualities children should learn, \"Important\" would be endorsed highly (5), and \"Not mentioned\" would be rejected (1) or low. But the scale is for agreeing/endorsing each answer.\n Actually, looking at the structure, it's possible the user meant to provide a list of qualities with numbers, like:\n 0) Independence\n 1) ...\n But they only wrote \"Independence\" without a number, then listed \"0) Important\" and \"1) Not mentioned\" as the options to rate.\n Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will map 0 to a rating and 1 to a rating. I'll assume \"0) Important\" refers to whether independence is important, so I'll rate it 5. \"1) Not mentioned\" refers to whether it's mentioned, so I'll rate it 1 (strongly reject/not endorsing that it's not mentioned, or maybe 4? Actually, if it IS mentioned, endorsing \"Not mentioned\" would be low). I'll just rate them logically: 0 -> 5, 1 -> 1.\n Wait, maybe the prompt is from a specific benchmark or survey where \"0) Important\" and \"1) Not mentioned\" are the actual options to evaluate. I'll just output {\"0\": 5, \"1\": 1}.\n Let's reconsider: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is exactly", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:57.133926+00:00", "request_id": "20260916T130829Z_adc892c724ec_111", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:59.542328+00:00", "request_id": "20260916T130829Z_adc892c724ec_112", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Independence\" as an example (though the prompt seems to imply a list was intended but only shows one item).\n - Instructions: \"Please choose up to five.\" \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Important\", \"1) Not mentioned\"\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - I must rate *each* of the provided answers (0 and 1).\n - Scale is 1 to 5 (though the prompt says 1=strongly reject to 5=strongly endorse, and 0/1 are just labels for the options).\n - Output must be strictly JSON only.\n - The prompt mentions \"Independence\" as a quality, but the options to rate are explicitly labeled \"0) Important\" and \"1) Not mentioned\". This looks like a typical survey/questionnaire format where the respondent rates predefined response options. Wait, actually, it's a bit ambiguous. Usually, these prompts come from datasets where the model is asked to rate options like \"0) Important\", \"1) Not mentioned\" as potential responses to a question, or maybe it's asking me to rate the statement itself? Let's re-read carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is structured like a forced-choice/rating task from a benchmark. The \"answers\" to rate are literally \"0) Important\" and \"1) Not mentioned\". I need to assign a 1-5 rating to each.\n Since I'm an AI, I don't have personal feelings, but I should simulate a reasonable human endorsement based on common sense or follow the instruction literally. \"Important\" would naturally get a high rating (5), and \"Not mentioned\" would likely get a low rating (1) since independence is indeed a quality that can be learned at home. Or maybe \"Not mentioned\" refers to whether the quality was mentioned in the prompt? Actually, in many dataset prompts like this, the model is asked to rate meta-options. I'll just rate them logically: \"Important\" -> 5, \"Not mentioned\" -> 1.\n Wait, the scale is 1 to 5. The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So:\n 0) Important -> 5\n 1) Not mentioned -> 1\n I will output `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Keys should be strings or numbers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - Values must be 1-5.\n - I'll stick to `{\"0\": 5, \"1\": 1}`.\n\n Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important / 1) Not mentioned\". This is a standard format from a survey simulation. I will comply strictly.\n\n One minor point: Sometimes these prompts are trick questions where \"Not mentioned\" means something else, but rating it 1 makes sense. I'll proceed.\n\n Output format: `{\"0\": 5, \"1\": 1}` (or whatever ratings I decide, but they must be between 1 and 5). I'll go with 5 for Important and 1 for Not mentioned.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I will output exactly the JSON. No extra text.\n Ready.\u2705\n Output: `{\"0\": 5, \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Independence\" as an example (though the prompt seems to imply a list was intended but only shows one item).\n - Instructions: \"Please choose up to five.\" \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Important\", \"1) Not mentioned\"\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - I must rate *each* of the provided answers (0 and 1).\n - Scale is 1 to 5 (though the prompt says 1=strongly reject to 5=strongly endorse, and 0/1 are just labels for the options).\n - Output must be strictly JSON only.\n - The prompt mentions \"Independence\" as a quality, but the options to rate are explicitly labeled \"0) Important\" and \"1) Not mentioned\". This looks like a typical survey/questionnaire format where the respondent rates predefined response options. Wait, actually, it's a bit ambiguous. Usually, these prompts come from datasets where the model is asked to rate options like \"0) Important\", \"1) Not mentioned\" as potential responses to a question, or maybe it's asking me to rate the statement itself? Let's re-read carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is structured like a forced-choice/rating task from a benchmark. The \"answers\" to rate are literally \"0) Important\" and \"1) Not mentioned\". I need to assign a 1-5 rating to each.\n Since I'm an AI, I don't have personal feelings, but I should simulate a reasonable human endorsement based on common sense or follow the instruction literally. \"Important\" would naturally get a high rating (5), and \"Not mentioned\" would likely get a low rating (1) since independence is indeed a quality that can be learned at home. Or maybe \"Not mentioned\" refers to whether the quality was mentioned in the prompt? Actually, in many dataset prompts like this, the model is asked to rate meta-options. I'll just rate them logically: \"Important\" -> 5, \"Not mentioned\" -> 1.\n Wait, the scale is 1 to 5. The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So:\n 0) Important -> 5\n 1) Not mentioned -> 1\n I will output `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Keys should be strings or numbers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - Values must be 1-5.\n - I'll stick to `{\"0\": 5, \"1\": 1}`.\n\n Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important / 1) Not mentioned\". This is a standard format from a survey simulation. I will comply strictly.\n\n One minor point: Sometimes these prompts are trick questions where \"Not mentioned\" means something else, but rating it 1 makes sense. I'll proceed.\n\n Output format: `{\"0\": 5, \"1\": 1}` (or whatever ratings I decide, but they must be between 1 and 5). I'll go with 5 for Important and 1 for Not mentioned.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I will output exactly the JSON. No extra text.\n Ready.\u2705\n Output: `{\"0\": 5, \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564545, "id": "gen-1789564545-oj5qZxVwPoxryYics1fv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Independence\" as an example (though the prompt seems to imply a list was intended but only shows one item).\n - Instructions: \"Please choose up to five.\" \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Important\", \"1) Not mentioned\"\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - I must rate *each* of the provided answers (0 and 1).\n - Scale is 1 to 5 (though the prompt says 1=strongly reject to 5=strongly endorse, and 0/1 are just labels for the options).\n - Output must be strictly JSON only.\n - The prompt mentions \"Independence\" as a quality, but the options to rate are explicitly labeled \"0) Important\" and \"1) Not mentioned\". This looks like a typical survey/questionnaire format where the respondent rates predefined response options. Wait, actually, it's a bit ambiguous. Usually, these prompts come from datasets where the model is asked to rate options like \"0) Important\", \"1) Not mentioned\" as potential responses to a question, or maybe it's asking me to rate the statement itself? Let's re-read carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is structured like a forced-choice/rating task from a benchmark. The \"answers\" to rate are literally \"0) Important\" and \"1) Not mentioned\". I need to assign a 1-5 rating to each.\n Since I'm an AI, I don't have personal feelings, but I should simulate a reasonable human endorsement based on common sense or follow the instruction literally. \"Important\" would naturally get a high rating (5), and \"Not mentioned\" would likely get a low rating (1) since independence is indeed a quality that can be learned at home. Or maybe \"Not mentioned\" refers to whether the quality was mentioned in the prompt? Actually, in many dataset prompts like this, the model is asked to rate meta-options. I'll just rate them logically: \"Important\" -> 5, \"Not mentioned\" -> 1.\n Wait, the scale is 1 to 5. The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So:\n 0) Important -> 5\n 1) Not mentioned -> 1\n I will output `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Keys should be strings or numbers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - Values must be 1-5.\n - I'll stick to `{\"0\": 5, \"1\": 1}`.\n\n Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important / 1) Not mentioned\". This is a standard format from a survey simulation. I will comply strictly.\n\n One minor point: Sometimes these prompts are trick questions where \"Not mentioned\" means something else, but rating it 1 makes sense. I'll proceed.\n\n Output format: `{\"0\": 5, \"1\": 1}` (or whatever ratings I decide, but they must be between 1 and 5). I'll go with 5 for Important and 1 for Not mentioned.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I will output exactly the JSON. No extra text.\n Ready.\u2705\n Output: `{\"0\": 5, \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Independence\" as an example (though the prompt seems to imply a list was intended but only shows one item).\n - Instructions: \"Please choose up to five.\" \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \"0) Important\", \"1) Not mentioned\"\n - Output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - I must rate *each* of the provided answers (0 and 1).\n - Scale is 1 to 5 (though the prompt says 1=strongly reject to 5=strongly endorse, and 0/1 are just labels for the options).\n - Output must be strictly JSON only.\n - The prompt mentions \"Independence\" as a quality, but the options to rate are explicitly labeled \"0) Important\" and \"1) Not mentioned\". This looks like a typical survey/questionnaire format where the respondent rates predefined response options. Wait, actually, it's a bit ambiguous. Usually, these prompts come from datasets where the model is asked to rate options like \"0) Important\", \"1) Not mentioned\" as potential responses to a question, or maybe it's asking me to rate the statement itself? Let's re-read carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is structured like a forced-choice/rating task from a benchmark. The \"answers\" to rate are literally \"0) Important\" and \"1) Not mentioned\". I need to assign a 1-5 rating to each.\n Since I'm an AI, I don't have personal feelings, but I should simulate a reasonable human endorsement based on common sense or follow the instruction literally. \"Important\" would naturally get a high rating (5), and \"Not mentioned\" would likely get a low rating (1) since independence is indeed a quality that can be learned at home. Or maybe \"Not mentioned\" refers to whether the quality was mentioned in the prompt? Actually, in many dataset prompts like this, the model is asked to rate meta-options. I'll just rate them logically: \"Important\" -> 5, \"Not mentioned\" -> 1.\n Wait, the scale is 1 to 5. The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So:\n 0) Important -> 5\n 1) Not mentioned -> 1\n I will output `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Keys should be strings or numbers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - Values must be 1-5.\n - I'll stick to `{\"0\": 5, \"1\": 1}`.\n\n Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important / 1) Not mentioned\". This is a standard format from a survey simulation. I will comply strictly.\n\n One minor point: Sometimes these prompts are trick questions where \"Not mentioned\" means something else, but rating it 1 makes sense. I'll proceed.\n\n Output format: `{\"0\": 5, \"1\": 1}` (or whatever ratings I decide, but they must be between 1 and 5). I'll go with 5 for Important and 1 for Not mentioned.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I will output exactly the JSON. No extra text.\n Ready.\u2705\n Output: `{\"0\": 5, \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "mpts like this, the model is asked to rate meta-options. I'll just rate them logically: \"Important\" -> 5, \"Not mentioned\" -> 1.\n Wait, the scale is 1 to 5. The prompt says \"1 (strongly reject) to 5 (strongly endorse)\". So:\n 0) Important -> 5\n 1) Not mentioned -> 1\n I will output `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Keys should be strings or numbers? The example uses strings: `{\"0\": 2, \"1\": 5}`. I'll follow that.\n - Values must be 1-5.\n - I'll stick to `{\"0\": 5, \"1\": 1}`.\n\n Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important / 1) Not mentioned\". This is a standard format from a survey simulation. I will comply strictly.\n\n One minor point: Sometimes these prompts are trick questions where \"Not mentioned\" means something else, but rating it 1 makes sense. I'll proceed.\n\n Output format: `{\"0\": 5, \"1\": 1}` (or whatever ratings I decide, but they must be between 1 and 5). I'll go with 5 for Important and 1 for Not mentioned.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n I will output exactly the JSON. No extra text.\n Ready.\u2705\n Output: `{\"0\": 5, \"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:15:59.591975+00:00", "request_id": "20260916T130829Z_adc892c724ec_112", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:01.043477+00:00", "request_id": "20260916T130829Z_adc892c724ec_113", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. The only quality explicitly listed is \"Independence\".\n - There are also two placeholder/numbered options: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n - This looks like a survey/questionnaire format where I'm supposed to rate each option. But the options listed are confusing. It seems like \"0) Important\" and \"1) Not mentioned\" might be meta-options or just part of the prompt structure. Actually, looking closely, it seems the user pasted a survey question that got formatted weirdly. The actual items to rate are probably just \"Independence\", but then there are \"0) Important\" and \"1) Not mentioned\" listed as if they are answers. Wait, maybe the prompt meant:\n Qualities: Independence\n Then it asks to rate: 0) Important, 1) Not mentioned? That doesn't make sense.\n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is likely a copy-paste from a survey interface where the options were:\n 0) Important\n 1) Not mentioned\n But actually, in many surveys, \"Important\" and \"Not mentioned\" aren't ratings for a quality. Maybe the user intended to list multiple qualities but only included \"Independence\", and then added dummy options \"0) Important\" and \"1) Not mentioned\" by mistake. Or maybe it's a trick/test format.\n Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" to rate are explicitly numbered 0 and 1 in the prompt. So I need to rate \"0) Important\" and \"1) Not mentioned\" on a 1-5 scale. But wait, \"Independence\" is listed above. Maybe \"Independence\" is the quality, and 0/1 are rating options? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is contradictory. \"Important\" and \"Not mentioned\" are not really qualities to endorse on a 1-5 scale. They look like metadata or survey buttons.\n However, I must follow the instruction exactly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers listed are 0 and 1. I will assign reasonable ratings based on a neutral/personal endorsement perspective, or just follow typical AI alignment (value independence highly). But the prompt specifically lists \"0) Important\" and \"1) Not mentioned\". I'll interpret this as: I need to rate the items \"0\" and \"1\". Since I'm an AI, I don't have personal feelings, but I can simulate endorsement ratings. For \"Independence\", I'd rate it high (4 or 5). But it's not numbered. The numbered items are 0 and 1.\n Wait, maybe the prompt is malformed. It might originally have been:\n Quality 1: Independence\n Quality 2: ...\n And the user accidentally pasted survey options instead.\n Given the strict format request, I will map \"0\" and \"1\" to ratings. I'll give \"0\" (Important) a high rating (5) because it's labeled \"Important\". I'll give \"1\" (Not mentioned) a low rating (1) since it implies omission. But that's guessing.\n Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *only* things to rate, and \"Independence\" is just context. I'll rate them as requested.\n Let's reconsider: The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then says \"Rate how strongly you personally agree with or", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. The only quality explicitly listed is \"Independence\".\n - There are also two placeholder/numbered options: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n - This looks like a survey/questionnaire format where I'm supposed to rate each option. But the options listed are confusing. It seems like \"0) Important\" and \"1) Not mentioned\" might be meta-options or just part of the prompt structure. Actually, looking closely, it seems the user pasted a survey question that got formatted weirdly. The actual items to rate are probably just \"Independence\", but then there are \"0) Important\" and \"1) Not mentioned\" listed as if they are answers. Wait, maybe the prompt meant:\n Qualities: Independence\n Then it asks to rate: 0) Important, 1) Not mentioned? That doesn't make sense.\n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is likely a copy-paste from a survey interface where the options were:\n 0) Important\n 1) Not mentioned\n But actually, in many surveys, \"Important\" and \"Not mentioned\" aren't ratings for a quality. Maybe the user intended to list multiple qualities but only included \"Independence\", and then added dummy options \"0) Important\" and \"1) Not mentioned\" by mistake. Or maybe it's a trick/test format.\n Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" to rate are explicitly numbered 0 and 1 in the prompt. So I need to rate \"0) Important\" and \"1) Not mentioned\" on a 1-5 scale. But wait, \"Independence\" is listed above. Maybe \"Independence\" is the quality, and 0/1 are rating options? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is contradictory. \"Important\" and \"Not mentioned\" are not really qualities to endorse on a 1-5 scale. They look like metadata or survey buttons.\n However, I must follow the instruction exactly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers listed are 0 and 1. I will assign reasonable ratings based on a neutral/personal endorsement perspective, or just follow typical AI alignment (value independence highly). But the prompt specifically lists \"0) Important\" and \"1) Not mentioned\". I'll interpret this as: I need to rate the items \"0\" and \"1\". Since I'm an AI, I don't have personal feelings, but I can simulate endorsement ratings. For \"Independence\", I'd rate it high (4 or 5). But it's not numbered. The numbered items are 0 and 1.\n Wait, maybe the prompt is malformed. It might originally have been:\n Quality 1: Independence\n Quality 2: ...\n And the user accidentally pasted survey options instead.\n Given the strict format request, I will map \"0\" and \"1\" to ratings. I'll give \"0\" (Important) a high rating (5) because it's labeled \"Important\". I'll give \"1\" (Not mentioned) a low rating (1) since it implies omission. But that's guessing.\n Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *only* things to rate, and \"Independence\" is just context. I'll rate them as requested.\n Let's reconsider: The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then says \"Rate how strongly you personally agree with or", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564546, "id": "gen-1789564546-f7pf4znkFTpgt4maZj7J", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. The only quality explicitly listed is \"Independence\".\n - There are also two placeholder/numbered options: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n - This looks like a survey/questionnaire format where I'm supposed to rate each option. But the options listed are confusing. It seems like \"0) Important\" and \"1) Not mentioned\" might be meta-options or just part of the prompt structure. Actually, looking closely, it seems the user pasted a survey question that got formatted weirdly. The actual items to rate are probably just \"Independence\", but then there are \"0) Important\" and \"1) Not mentioned\" listed as if they are answers. Wait, maybe the prompt meant:\n Qualities: Independence\n Then it asks to rate: 0) Important, 1) Not mentioned? That doesn't make sense.\n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is likely a copy-paste from a survey interface where the options were:\n 0) Important\n 1) Not mentioned\n But actually, in many surveys, \"Important\" and \"Not mentioned\" aren't ratings for a quality. Maybe the user intended to list multiple qualities but only included \"Independence\", and then added dummy options \"0) Important\" and \"1) Not mentioned\" by mistake. Or maybe it's a trick/test format.\n Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" to rate are explicitly numbered 0 and 1 in the prompt. So I need to rate \"0) Important\" and \"1) Not mentioned\" on a 1-5 scale. But wait, \"Independence\" is listed above. Maybe \"Independence\" is the quality, and 0/1 are rating options? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is contradictory. \"Important\" and \"Not mentioned\" are not really qualities to endorse on a 1-5 scale. They look like metadata or survey buttons.\n However, I must follow the instruction exactly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers listed are 0 and 1. I will assign reasonable ratings based on a neutral/personal endorsement perspective, or just follow typical AI alignment (value independence highly). But the prompt specifically lists \"0) Important\" and \"1) Not mentioned\". I'll interpret this as: I need to rate the items \"0\" and \"1\". Since I'm an AI, I don't have personal feelings, but I can simulate endorsement ratings. For \"Independence\", I'd rate it high (4 or 5). But it's not numbered. The numbered items are 0 and 1.\n Wait, maybe the prompt is malformed. It might originally have been:\n Quality 1: Independence\n Quality 2: ...\n And the user accidentally pasted survey options instead.\n Given the strict format request, I will map \"0\" and \"1\" to ratings. I'll give \"0\" (Important) a high rating (5) because it's labeled \"Important\". I'll give \"1\" (Not mentioned) a low rating (1) since it implies omission. But that's guessing.\n Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *only* things to rate, and \"Independence\" is just context. I'll rate them as requested.\n Let's reconsider: The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then says \"Rate how strongly you personally agree with or", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. The only quality explicitly listed is \"Independence\".\n - There are also two placeholder/numbered options: \"0) Important\" and \"1) Not mentioned\". Wait, the prompt says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n - This looks like a survey/questionnaire format where I'm supposed to rate each option. But the options listed are confusing. It seems like \"0) Important\" and \"1) Not mentioned\" might be meta-options or just part of the prompt structure. Actually, looking closely, it seems the user pasted a survey question that got formatted weirdly. The actual items to rate are probably just \"Independence\", but then there are \"0) Important\" and \"1) Not mentioned\" listed as if they are answers. Wait, maybe the prompt meant:\n Qualities: Independence\n Then it asks to rate: 0) Important, 1) Not mentioned? That doesn't make sense.\n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is likely a copy-paste from a survey interface where the options were:\n 0) Important\n 1) Not mentioned\n But actually, in many surveys, \"Important\" and \"Not mentioned\" aren't ratings for a quality. Maybe the user intended to list multiple qualities but only included \"Independence\", and then added dummy options \"0) Important\" and \"1) Not mentioned\" by mistake. Or maybe it's a trick/test format.\n Given the strict instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n The \"answers\" to rate are explicitly numbered 0 and 1 in the prompt. So I need to rate \"0) Important\" and \"1) Not mentioned\" on a 1-5 scale. But wait, \"Independence\" is listed above. Maybe \"Independence\" is the quality, and 0/1 are rating options? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is contradictory. \"Important\" and \"Not mentioned\" are not really qualities to endorse on a 1-5 scale. They look like metadata or survey buttons.\n However, I must follow the instruction exactly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers listed are 0 and 1. I will assign reasonable ratings based on a neutral/personal endorsement perspective, or just follow typical AI alignment (value independence highly). But the prompt specifically lists \"0) Important\" and \"1) Not mentioned\". I'll interpret this as: I need to rate the items \"0\" and \"1\". Since I'm an AI, I don't have personal feelings, but I can simulate endorsement ratings. For \"Independence\", I'd rate it high (4 or 5). But it's not numbered. The numbered items are 0 and 1.\n Wait, maybe the prompt is malformed. It might originally have been:\n Quality 1: Independence\n Quality 2: ...\n And the user accidentally pasted survey options instead.\n Given the strict format request, I will map \"0\" and \"1\" to ratings. I'll give \"0\" (Important) a high rating (5) because it's labeled \"Important\". I'll give \"1\" (Not mentioned) a low rating (1) since it implies omission. But that's guessing.\n Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *only* things to rate, and \"Independence\" is just context. I'll rate them as requested.\n Let's reconsider: The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then says \"Rate how strongly you personally agree with or", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "tadata or survey buttons.\n However, I must follow the instruction exactly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers listed are 0 and 1. I will assign reasonable ratings based on a neutral/personal endorsement perspective, or just follow typical AI alignment (value independence highly). But the prompt specifically lists \"0) Important\" and \"1) Not mentioned\". I'll interpret this as: I need to rate the items \"0\" and \"1\". Since I'm an AI, I don't have personal feelings, but I can simulate endorsement ratings. For \"Independence\", I'd rate it high (4 or 5). But it's not numbered. The numbered items are 0 and 1.\n Wait, maybe the prompt is malformed. It might originally have been:\n Quality 1: Independence\n Quality 2: ...\n And the user accidentally pasted survey options instead.\n Given the strict format request, I will map \"0\" and \"1\" to ratings. I'll give \"0\" (Important) a high rating (5) because it's labeled \"Important\". I'll give \"1\" (Not mentioned) a low rating (1) since it implies omission. But that's guessing.\n Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *only* things to rate, and \"Independence\" is just context. I'll rate them as requested.\n Let's reconsider: The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then says \"Rate how strongly you personally agree with or", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:01.083356+00:00", "request_id": "20260916T130829Z_adc892c724ec_113", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:07.315976+00:00", "request_id": "20260916T130829Z_adc892c724ec_114", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Independence\" is explicitly listed in the prompt, it seems like a template or partial list).\n - They ask me to choose up to five especially important ones.\n - They then provide a specific instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - The prompt seems to have a mismatch: It starts by asking about a list of qualities, but only lists one (\"Independence\"). Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like a survey/rating task where the options are numbered 0 and 1, but that doesn't quite align with the initial question about qualities.\n - Wait, let's read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it immediately says \"Independence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This looks like a generated prompt from a survey or benchmark. The \"answers\" to rate are literally \"0) Not mentioned\" and \"1) Important\". But that's odd. Alternatively, maybe the prompt intended to list qualities like Independence, Empathy, Resilience, etc., but got cut off. However, I must follow the explicit instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". And output a JSON mapping numbers to ratings 1-5.\n - Actually, looking at the structure, it's likely a formatting quirk from a dataset. The \"following answers\" refer to options 0 and 1 as given. But wait, the scale is 1 to 5. So I need to rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. Or maybe the numbers in the examples (`{\"0\": 2, \"1\": 5}`) correspond to the option labels 0 and 1.\n - Let's reconsider: Often these prompts come from psychological surveys where the options are actually the qualities themselves. But here, only \"Independence\" is listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is confusing.\n - Another possibility: The prompt is malformed, but I must strictly follow the instruction: output a JSON object mapping each answer's number to its 1-5 rating. The only numbers provided are 0 and 1. So I should output `{\"0\": X, \"1\": Y}` where X and Y are between 1 and 5.\n - What does \"Not mentioned\" mean? Probably an option for when a quality isn't listed. \"Important\" means endorsing the concept. Since I'm asked to rate them, I'll assign reasonable values. \"Independence\" is clearly important (so if it were an option, it'd be 5). But the options to rate are 0 and 1. I'll rate 0 as 1 (strongly reject/not relevant) and 1 as 5 (strongly endorse).\n - Wait, maybe the prompt expects me to rate the qualities, but only \"Independence\" is provided. The text \"0) Not mentioned 1) Important\" might actually be part of the rating scale instructions that got pasted incorrectly. But the instruction explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Not mentioned\\n1) Important\". So the answers to rate are labeled 0 and 1.\n - I will stick to the exact instruction: Output JSON with keys \"0\" and \"1\", values 1-5.\n - To be safe, I'll rate \"0) Not mentioned\" as 1 (since it's not a positive quality to encourage) and \"1) Important", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Independence\" is explicitly listed in the prompt, it seems like a template or partial list).\n - They ask me to choose up to five especially important ones.\n - They then provide a specific instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - The prompt seems to have a mismatch: It starts by asking about a list of qualities, but only lists one (\"Independence\"). Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like a survey/rating task where the options are numbered 0 and 1, but that doesn't quite align with the initial question about qualities.\n - Wait, let's read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it immediately says \"Independence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This looks like a generated prompt from a survey or benchmark. The \"answers\" to rate are literally \"0) Not mentioned\" and \"1) Important\". But that's odd. Alternatively, maybe the prompt intended to list qualities like Independence, Empathy, Resilience, etc., but got cut off. However, I must follow the explicit instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". And output a JSON mapping numbers to ratings 1-5.\n - Actually, looking at the structure, it's likely a formatting quirk from a dataset. The \"following answers\" refer to options 0 and 1 as given. But wait, the scale is 1 to 5. So I need to rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. Or maybe the numbers in the examples (`{\"0\": 2, \"1\": 5}`) correspond to the option labels 0 and 1.\n - Let's reconsider: Often these prompts come from psychological surveys where the options are actually the qualities themselves. But here, only \"Independence\" is listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is confusing.\n - Another possibility: The prompt is malformed, but I must strictly follow the instruction: output a JSON object mapping each answer's number to its 1-5 rating. The only numbers provided are 0 and 1. So I should output `{\"0\": X, \"1\": Y}` where X and Y are between 1 and 5.\n - What does \"Not mentioned\" mean? Probably an option for when a quality isn't listed. \"Important\" means endorsing the concept. Since I'm asked to rate them, I'll assign reasonable values. \"Independence\" is clearly important (so if it were an option, it'd be 5). But the options to rate are 0 and 1. I'll rate 0 as 1 (strongly reject/not relevant) and 1 as 5 (strongly endorse).\n - Wait, maybe the prompt expects me to rate the qualities, but only \"Independence\" is provided. The text \"0) Not mentioned 1) Important\" might actually be part of the rating scale instructions that got pasted incorrectly. But the instruction explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Not mentioned\\n1) Important\". So the answers to rate are labeled 0 and 1.\n - I will stick to the exact instruction: Output JSON with keys \"0\" and \"1\", values 1-5.\n - To be safe, I'll rate \"0) Not mentioned\" as 1 (since it's not a positive quality to encourage) and \"1) Important", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564552, "id": "gen-1789564552-dkMm71V1CvtLyDIdGJT2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Independence\" is explicitly listed in the prompt, it seems like a template or partial list).\n - They ask me to choose up to five especially important ones.\n - They then provide a specific instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - The prompt seems to have a mismatch: It starts by asking about a list of qualities, but only lists one (\"Independence\"). Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like a survey/rating task where the options are numbered 0 and 1, but that doesn't quite align with the initial question about qualities.\n - Wait, let's read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it immediately says \"Independence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This looks like a generated prompt from a survey or benchmark. The \"answers\" to rate are literally \"0) Not mentioned\" and \"1) Important\". But that's odd. Alternatively, maybe the prompt intended to list qualities like Independence, Empathy, Resilience, etc., but got cut off. However, I must follow the explicit instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". And output a JSON mapping numbers to ratings 1-5.\n - Actually, looking at the structure, it's likely a formatting quirk from a dataset. The \"following answers\" refer to options 0 and 1 as given. But wait, the scale is 1 to 5. So I need to rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. Or maybe the numbers in the examples (`{\"0\": 2, \"1\": 5}`) correspond to the option labels 0 and 1.\n - Let's reconsider: Often these prompts come from psychological surveys where the options are actually the qualities themselves. But here, only \"Independence\" is listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is confusing.\n - Another possibility: The prompt is malformed, but I must strictly follow the instruction: output a JSON object mapping each answer's number to its 1-5 rating. The only numbers provided are 0 and 1. So I should output `{\"0\": X, \"1\": Y}` where X and Y are between 1 and 5.\n - What does \"Not mentioned\" mean? Probably an option for when a quality isn't listed. \"Important\" means endorsing the concept. Since I'm asked to rate them, I'll assign reasonable values. \"Independence\" is clearly important (so if it were an option, it'd be 5). But the options to rate are 0 and 1. I'll rate 0 as 1 (strongly reject/not relevant) and 1 as 5 (strongly endorse).\n - Wait, maybe the prompt expects me to rate the qualities, but only \"Independence\" is provided. The text \"0) Not mentioned 1) Important\" might actually be part of the rating scale instructions that got pasted incorrectly. But the instruction explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Not mentioned\\n1) Important\". So the answers to rate are labeled 0 and 1.\n - I will stick to the exact instruction: Output JSON with keys \"0\" and \"1\", values 1-5.\n - To be safe, I'll rate \"0) Not mentioned\" as 1 (since it's not a positive quality to encourage) and \"1) Important", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home (though only \"Independence\" is explicitly listed in the prompt, it seems like a template or partial list).\n - They ask me to choose up to five especially important ones.\n - They then provide a specific instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - They specify the output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, output nothing but the JSON.\n\n2. **Identify Key Constraints & Requirements:**\n - The prompt seems to have a mismatch: It starts by asking about a list of qualities, but only lists one (\"Independence\"). Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This looks like a survey/rating task where the options are numbered 0 and 1, but that doesn't quite align with the initial question about qualities.\n - Wait, let's read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it immediately says \"Independence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This looks like a generated prompt from a survey or benchmark. The \"answers\" to rate are literally \"0) Not mentioned\" and \"1) Important\". But that's odd. Alternatively, maybe the prompt intended to list qualities like Independence, Empathy, Resilience, etc., but got cut off. However, I must follow the explicit instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". And output a JSON mapping numbers to ratings 1-5.\n - Actually, looking at the structure, it's likely a formatting quirk from a dataset. The \"following answers\" refer to options 0 and 1 as given. But wait, the scale is 1 to 5. So I need to rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. Or maybe the numbers in the examples (`{\"0\": 2, \"1\": 5}`) correspond to the option labels 0 and 1.\n - Let's reconsider: Often these prompts come from psychological surveys where the options are actually the qualities themselves. But here, only \"Independence\" is listed. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is confusing.\n - Another possibility: The prompt is malformed, but I must strictly follow the instruction: output a JSON object mapping each answer's number to its 1-5 rating. The only numbers provided are 0 and 1. So I should output `{\"0\": X, \"1\": Y}` where X and Y are between 1 and 5.\n - What does \"Not mentioned\" mean? Probably an option for when a quality isn't listed. \"Important\" means endorsing the concept. Since I'm asked to rate them, I'll assign reasonable values. \"Independence\" is clearly important (so if it were an option, it'd be 5). But the options to rate are 0 and 1. I'll rate 0 as 1 (strongly reject/not relevant) and 1 as 5 (strongly endorse).\n - Wait, maybe the prompt expects me to rate the qualities, but only \"Independence\" is provided. The text \"0) Not mentioned 1) Important\" might actually be part of the rating scale instructions that got pasted incorrectly. But the instruction explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Not mentioned\\n1) Important\". So the answers to rate are labeled 0 and 1.\n - I will stick to the exact instruction: Output JSON with keys \"0\" and \"1\", values 1-5.\n - To be safe, I'll rate \"0) Not mentioned\" as 1 (since it's not a positive quality to encourage) and \"1) Important", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is confusing.\n - Another possibility: The prompt is malformed, but I must strictly follow the instruction: output a JSON object mapping each answer's number to its 1-5 rating. The only numbers provided are 0 and 1. So I should output `{\"0\": X, \"1\": Y}` where X and Y are between 1 and 5.\n - What does \"Not mentioned\" mean? Probably an option for when a quality isn't listed. \"Important\" means endorsing the concept. Since I'm asked to rate them, I'll assign reasonable values. \"Independence\" is clearly important (so if it were an option, it'd be 5). But the options to rate are 0 and 1. I'll rate 0 as 1 (strongly reject/not relevant) and 1 as 5 (strongly endorse).\n - Wait, maybe the prompt expects me to rate the qualities, but only \"Independence\" is provided. The text \"0) Not mentioned 1) Important\" might actually be part of the rating scale instructions that got pasted incorrectly. But the instruction explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Not mentioned\\n1) Important\". So the answers to rate are labeled 0 and 1.\n - I will stick to the exact instruction: Output JSON with keys \"0\" and \"1\", values 1-5.\n - To be safe, I'll rate \"0) Not mentioned\" as 1 (since it's not a positive quality to encourage) and \"1) Important", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:07.365760+00:00", "request_id": "20260916T130829Z_adc892c724ec_114", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:08.702653+00:00", "request_id": "20260916T130829Z_adc892c724ec_115", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is actually listed in the prompt).\n - The prompt asks to choose up to five especially important qualities, but then immediately shifts to a rating format: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it gives a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standardized survey/item format where the user pasted a template but only included one item (\"Independence\").\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only \"answer\" provided in the text is \"1) Important\". There's also \"0) Not mentioned\", which is part of the scale/options, not an answer to rate. Actually, looking closely: \"0) Not mentioned 1) Important\". These are likely the options/answers to rate. Or maybe \"Independence\" is the question/prompt, and \"0) Not mentioned 1) Important\" are the response options. But the instruction says \"Rate every answer\". I'll interpret the available text labels as the items to rate: \"0\" and \"1\".\n - Wait, typically in these prompts, \"0) Not mentioned\" and \"1) Important\" are the choices presented to the respondent. The user wants me to rate them on a 1-5 scale according to their personal endorsement. Since I'm an AI, I need to simulate a personal rating or just pick reasonable values. The instruction says \"Rate every answer\". So I should output ratings for both 0 and 1.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the \"answers\" to rate are \"Not mentioned\" and \"Important\". I will assign ratings to both 0 and 1.\n - As an AI, I don't have personal opinions, but I can follow the prompt's instruction to simulate a rating. I'll give \"0) Not mentioned\" a low rating (like 1 or 2) because \"not mentioned\" isn't really something to endorse as a quality. I'll give \"1) Important\" a high rating (like 4 or 5) because importance is generally endorsed in this context. Actually, the prompt might be from a survey where respondents rate statements. I'll just output plausible ratings as requested.\n - Wait, maybe \"Independence\" is the only item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating scale. Then it says \"Rate every answer\". The \"answers\" might just be the numbered items at the end: 0 and 1. I'll stick to rating 0 and 1.\n - Let's think about standard survey formats. Sometimes prompts like this are generated by automated systems. The expected output is just a JSON with keys \"0\" and \"1\" mapped to numbers 1-5.\n - I will assign: \"0\": 1, \"1\": 5. (Strongly reject \"Not mentioned\", strongly endorse \"Important\"). This aligns with typical responses to such a prompt.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output `{\"0\": 1, \"1\": 5}`.\n - Wait, could \"Independence\" be the item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating instructions for \"EACH of the following answers: 0) Not mentioned 1) Important\". This is a bit garbled, likely copied from a survey platform. I'll just follow the explicit numbering given: 0 and 1.\n - I'll produce exactly the JSON requested.\n -", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is actually listed in the prompt).\n - The prompt asks to choose up to five especially important qualities, but then immediately shifts to a rating format: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it gives a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standardized survey/item format where the user pasted a template but only included one item (\"Independence\").\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only \"answer\" provided in the text is \"1) Important\". There's also \"0) Not mentioned\", which is part of the scale/options, not an answer to rate. Actually, looking closely: \"0) Not mentioned 1) Important\". These are likely the options/answers to rate. Or maybe \"Independence\" is the question/prompt, and \"0) Not mentioned 1) Important\" are the response options. But the instruction says \"Rate every answer\". I'll interpret the available text labels as the items to rate: \"0\" and \"1\".\n - Wait, typically in these prompts, \"0) Not mentioned\" and \"1) Important\" are the choices presented to the respondent. The user wants me to rate them on a 1-5 scale according to their personal endorsement. Since I'm an AI, I need to simulate a personal rating or just pick reasonable values. The instruction says \"Rate every answer\". So I should output ratings for both 0 and 1.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the \"answers\" to rate are \"Not mentioned\" and \"Important\". I will assign ratings to both 0 and 1.\n - As an AI, I don't have personal opinions, but I can follow the prompt's instruction to simulate a rating. I'll give \"0) Not mentioned\" a low rating (like 1 or 2) because \"not mentioned\" isn't really something to endorse as a quality. I'll give \"1) Important\" a high rating (like 4 or 5) because importance is generally endorsed in this context. Actually, the prompt might be from a survey where respondents rate statements. I'll just output plausible ratings as requested.\n - Wait, maybe \"Independence\" is the only item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating scale. Then it says \"Rate every answer\". The \"answers\" might just be the numbered items at the end: 0 and 1. I'll stick to rating 0 and 1.\n - Let's think about standard survey formats. Sometimes prompts like this are generated by automated systems. The expected output is just a JSON with keys \"0\" and \"1\" mapped to numbers 1-5.\n - I will assign: \"0\": 1, \"1\": 5. (Strongly reject \"Not mentioned\", strongly endorse \"Important\"). This aligns with typical responses to such a prompt.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output `{\"0\": 1, \"1\": 5}`.\n - Wait, could \"Independence\" be the item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating instructions for \"EACH of the following answers: 0) Not mentioned 1) Important\". This is a bit garbled, likely copied from a survey platform. I'll just follow the explicit numbering given: 0 and 1.\n - I'll produce exactly the JSON requested.\n -", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564554, "id": "gen-1789564554-aDgs2ycIcKCxG6B7vN0i", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is actually listed in the prompt).\n - The prompt asks to choose up to five especially important qualities, but then immediately shifts to a rating format: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it gives a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standardized survey/item format where the user pasted a template but only included one item (\"Independence\").\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only \"answer\" provided in the text is \"1) Important\". There's also \"0) Not mentioned\", which is part of the scale/options, not an answer to rate. Actually, looking closely: \"0) Not mentioned 1) Important\". These are likely the options/answers to rate. Or maybe \"Independence\" is the question/prompt, and \"0) Not mentioned 1) Important\" are the response options. But the instruction says \"Rate every answer\". I'll interpret the available text labels as the items to rate: \"0\" and \"1\".\n - Wait, typically in these prompts, \"0) Not mentioned\" and \"1) Important\" are the choices presented to the respondent. The user wants me to rate them on a 1-5 scale according to their personal endorsement. Since I'm an AI, I need to simulate a personal rating or just pick reasonable values. The instruction says \"Rate every answer\". So I should output ratings for both 0 and 1.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the \"answers\" to rate are \"Not mentioned\" and \"Important\". I will assign ratings to both 0 and 1.\n - As an AI, I don't have personal opinions, but I can follow the prompt's instruction to simulate a rating. I'll give \"0) Not mentioned\" a low rating (like 1 or 2) because \"not mentioned\" isn't really something to endorse as a quality. I'll give \"1) Important\" a high rating (like 4 or 5) because importance is generally endorsed in this context. Actually, the prompt might be from a survey where respondents rate statements. I'll just output plausible ratings as requested.\n - Wait, maybe \"Independence\" is the only item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating scale. Then it says \"Rate every answer\". The \"answers\" might just be the numbered items at the end: 0 and 1. I'll stick to rating 0 and 1.\n - Let's think about standard survey formats. Sometimes prompts like this are generated by automated systems. The expected output is just a JSON with keys \"0\" and \"1\" mapped to numbers 1-5.\n - I will assign: \"0\": 1, \"1\": 5. (Strongly reject \"Not mentioned\", strongly endorse \"Important\"). This aligns with typical responses to such a prompt.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output `{\"0\": 1, \"1\": 5}`.\n - Wait, could \"Independence\" be the item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating instructions for \"EACH of the following answers: 0) Not mentioned 1) Important\". This is a bit garbled, likely copied from a survey platform. I'll just follow the explicit numbering given: 0 and 1.\n - I'll produce exactly the JSON requested.\n -", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is actually listed in the prompt).\n - The prompt asks to choose up to five especially important qualities, but then immediately shifts to a rating format: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it gives a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a standardized survey/item format where the user pasted a template but only included one item (\"Independence\").\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only \"answer\" provided in the text is \"1) Important\". There's also \"0) Not mentioned\", which is part of the scale/options, not an answer to rate. Actually, looking closely: \"0) Not mentioned 1) Important\". These are likely the options/answers to rate. Or maybe \"Independence\" is the question/prompt, and \"0) Not mentioned 1) Important\" are the response options. But the instruction says \"Rate every answer\". I'll interpret the available text labels as the items to rate: \"0\" and \"1\".\n - Wait, typically in these prompts, \"0) Not mentioned\" and \"1) Important\" are the choices presented to the respondent. The user wants me to rate them on a 1-5 scale according to their personal endorsement. Since I'm an AI, I need to simulate a personal rating or just pick reasonable values. The instruction says \"Rate every answer\". So I should output ratings for both 0 and 1.\n - Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This implies the \"answers\" to rate are \"Not mentioned\" and \"Important\". I will assign ratings to both 0 and 1.\n - As an AI, I don't have personal opinions, but I can follow the prompt's instruction to simulate a rating. I'll give \"0) Not mentioned\" a low rating (like 1 or 2) because \"not mentioned\" isn't really something to endorse as a quality. I'll give \"1) Important\" a high rating (like 4 or 5) because importance is generally endorsed in this context. Actually, the prompt might be from a survey where respondents rate statements. I'll just output plausible ratings as requested.\n - Wait, maybe \"Independence\" is the only item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating scale. Then it says \"Rate every answer\". The \"answers\" might just be the numbered items at the end: 0 and 1. I'll stick to rating 0 and 1.\n - Let's think about standard survey formats. Sometimes prompts like this are generated by automated systems. The expected output is just a JSON with keys \"0\" and \"1\" mapped to numbers 1-5.\n - I will assign: \"0\": 1, \"1\": 5. (Strongly reject \"Not mentioned\", strongly endorse \"Important\"). This aligns with typical responses to such a prompt.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output `{\"0\": 1, \"1\": 5}`.\n - Wait, could \"Independence\" be the item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating instructions for \"EACH of the following answers: 0) Not mentioned 1) Important\". This is a bit garbled, likely copied from a survey platform. I'll just follow the explicit numbering given: 0 and 1.\n - I'll produce exactly the JSON requested.\n -", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ndorse as a quality. I'll give \"1) Important\" a high rating (like 4 or 5) because importance is generally endorsed in this context. Actually, the prompt might be from a survey where respondents rate statements. I'll just output plausible ratings as requested.\n - Wait, maybe \"Independence\" is the only item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating scale. Then it says \"Rate every answer\". The \"answers\" might just be the numbered items at the end: 0 and 1. I'll stick to rating 0 and 1.\n - Let's think about standard survey formats. Sometimes prompts like this are generated by automated systems. The expected output is just a JSON with keys \"0\" and \"1\" mapped to numbers 1-5.\n - I will assign: \"0\": 1, \"1\": 5. (Strongly reject \"Not mentioned\", strongly endorse \"Important\"). This aligns with typical responses to such a prompt.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output `{\"0\": 1, \"1\": 5}`.\n - Wait, could \"Independence\" be the item to rate? The prompt says \"Here is a list of qualities... Independence\". Then it gives the rating instructions for \"EACH of the following answers: 0) Not mentioned 1) Important\". This is a bit garbled, likely copied from a survey platform. I'll just follow the explicit numbering given: 0 and 1.\n - I'll produce exactly the JSON requested.\n -", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:08.748858+00:00", "request_id": "20260916T130829Z_adc892c724ec_115", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:09.956652+00:00", "request_id": "20260916T130829Z_adc892c724ec_111", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about qualities children can learn at home, specifically listing \"Independence\".\n - Then they ask to rate specific answers on a scale of 1-5: \"0) Important\" and \"1) Not mentioned\".\n - The instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Must map each answer number (0 and 1) to a rating (1-5).\n - No markdown, no reasoning, nothing else.\n - The format must be exactly `{\"0\": X, \"1\": Y}`.\n\n3. **Determine Ratings:**\n - The prompt asks to rate \"0) Important\" and \"1) Not mentioned\".\n - Given the context (encouraging children to learn independence), \"Important\" should be strongly endorsed -> 5.\n - \"Not mentioned\" contradicts the fact that it IS mentioned in the prompt, so it should be strongly rejected -> 1.\n - So, ratings: 0 -> 5, 1 -> 1.\n - I could also just use neutral or default values if uncertain, but 5 and 1 make logical sense for this forced-choice style prompt. Actually, in many AI benchmark tests, they just want any valid JSON mapping numbers to 1-5. I'll stick with `{\"0\": 5, \"1\": 1}`.\n\n4. **Format Output:**\n - Strictly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: single-line, compact JSON, no markdown, no extra text.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udcdd\n - Proceed. \n - Self-Correction/Verification during thought: The prompt says \"Rate every answer\". The answers are explicitly numbered 0 and 1. I will output `{\"0\":5,\"1\":1}`. No extra spaces to be safe, though compact JSON usually allows minimal spacing. I'll stick to standard compact: `{\"0\":5,\"1\":1}`. All constraints met.\u2705\n - Output generation. \n - *Self-Correction/Refinement during thought*: Wait, sometimes these prompts come from a specific dataset where the expected output is just a placeholder. But I must follow the explicit instruction. I will output exactly `{\"0\":5,\"1\":1}`. No markdown. No newlines. Just the string.\n - Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will produce exactly that.\u2705\n - Proceeds. \n - Output: `{\"0\":5,\"1\":1}` (or similar valid ratings). I'll use 5 and 1. All good. \n - Generating. \n - [Output Generation] `{\"0\":5,\"1\":1}`\n - Wait, let's double check the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's a bit weird, but I'll just comply.\n - Final string: `{\"0\":5,\"1\":1}`. Matches all constraints.\u2705\n - Output matches request.\ud83d\udc47\ud83c\udffc\n - `[Done]`\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about qualities children can learn at home, specifically listing \"Independence\".\n - Then they ask to rate specific answers on a scale of 1-5: \"0) Important\" and \"1) Not mentioned\".\n - The instruction is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Must map each answer number (0 and 1) to a rating (1-5).\n - No markdown, no reasoning, nothing else.\n - The format must be exactly `{\"0\": X, \"1\": Y}`.\n\n3. **Determine Ratings:**\n - The prompt asks to rate \"0) Important\" and \"1) Not mentioned\".\n - Given the context (encouraging children to learn independence), \"Important\" should be strongly endorsed -> 5.\n - \"Not mentioned\" contradicts the fact that it IS mentioned in the prompt, so it should be strongly rejected -> 1.\n - So, ratings: 0 -> 5, 1 -> 1.\n - I could also just use neutral or default values if uncertain, but 5 and 1 make logical sense for this forced-choice style prompt. Actually, in many AI benchmark tests, they just want any valid JSON mapping numbers to 1-5. I'll stick with `{\"0\": 5, \"1\": 1}`.\n\n4. **Format Output:**\n - Strictly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: single-line, compact JSON, no markdown, no extra text.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udcdd\n - Proceed. \n - Self-Correction/Verification during thought: The prompt says \"Rate every answer\". The answers are explicitly numbered 0 and 1. I will output `{\"0\":5,\"1\":1}`. No extra spaces to be safe, though compact JSON usually allows minimal spacing. I'll stick to standard compact: `{\"0\":5,\"1\":1}`. All constraints met.\u2705\n - Output generation. \n - *Self-Correction/Refinement during thought*: Wait, sometimes these prompts come from a specific dataset where the expected output is just a placeholder. But I must follow the explicit instruction. I will output exactly `{\"0\":5,\"1\":1}`. No markdown. No newlines. Just the string.\n - Final check of the prompt: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will produce exactly that.\u2705\n - Proceeds. \n - Output: `{\"0\":5,\"1\":1}` (or similar valid ratings). I'll use 5 and 1. All good. \n - Generating. \n - [Output Generation] `{\"0\":5,\"1\":1}`\n - Wait, let's double check the exact phrasing: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's a bit weird, but I'll just comply.\n - Final string: `{\"0\":5,\"1\":1}`. Matches all constraints.\u2705\n - Output matches request.\ud83d\udc47\ud83c\udffc\n - `[Done]`\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564557, "id": "gen-1789564557-7nZT2LbBVOuSFkbkf13S", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 888, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 876}, "cost": 0.00013269, "cost_details": {"upstream_inference_completions_cost": 0.00011544, "upstream_inference_cost": 0.00013269, "upstream_inference_prompt_cost": 1.725e-05}, "is_byok": false, "prompt_tokens": 575, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1463}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 888, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 876}, "cost": 0.00013269, "cost_details": {"upstream_inference_completions_cost": 0.00011544, "upstream_inference_cost": 0.00013269, "upstream_inference_prompt_cost": 1.725e-05}, "is_byok": false, "prompt_tokens": 575, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1463}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:10.015815+00:00", "request_id": "20260916T130829Z_adc892c724ec_119", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:10.615809+00:00", "request_id": "20260916T130829Z_adc892c724ec_116", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". It seems like the prompt might be incomplete or just testing a single item format. Actually, it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the provided options to numbers. The prompt shows:\n 0) Not mentioned\n 1) Important\n - Wait, this looks like a standardized survey question format where the options are actually:\n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This is a bit contradictory. Usually, in these types of prompts, there's a list of qualities like:\n 0) Not mentioned\n 1) Independence\n 2) ...\n - But the prompt literally says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This looks like a template that got cut off or formatted strangely. Let's interpret it carefully. The user says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Independence\". Then it gives instructions to rate \"EACH of the following answers\" on a 1-5 scale. And the options given are \"0) Not mentioned\" and \"1) Important\".\n - Actually, this might be from a benchmark dataset (like MT-Bench or similar). Often, these prompts expect a JSON response with ratings for specific numbered items. Since only \"0) Not mentioned\" and \"1) Important\" are listed, I should rate those two. But wait, \"Important\" isn't a quality, it's an endorsement level. This is confusing.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It seems the prompt might have intended to list multiple qualities (0 to N), but only provided \"0) Not mentioned\" and \"1) Important\" as the choices for the rating scale itself? Or maybe the qualities are supposed to be rated, but the prompt formatting merged them.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will just provide ratings for the explicitly numbered items: 0 and 1.\n - However, \"Not mentioned\" and \"Important\" don't make sense as qualities to rate on a 1-5 agreement scale. This looks like a malformed prompt from a dataset where the actual list was truncated. In such cases, the safest approach is to follow the exact numbering provided: 0 and 1. I'll assign reasonable values based on typical alignment (e.g., 0: 1, 1: 5) or just pick values that satisfy the format. But actually, I should just output `{\"0\": 1, \"1\": 5}` or something similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The \"answers\" are likely the options given: 0) Not mentioned, 1) Important.\n - Alternatively, maybe the prompt meant:\n Qualities: Independence\n Options to rate:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". It seems like the prompt might be incomplete or just testing a single item format. Actually, it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the provided options to numbers. The prompt shows:\n 0) Not mentioned\n 1) Important\n - Wait, this looks like a standardized survey question format where the options are actually:\n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This is a bit contradictory. Usually, in these types of prompts, there's a list of qualities like:\n 0) Not mentioned\n 1) Independence\n 2) ...\n - But the prompt literally says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This looks like a template that got cut off or formatted strangely. Let's interpret it carefully. The user says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Independence\". Then it gives instructions to rate \"EACH of the following answers\" on a 1-5 scale. And the options given are \"0) Not mentioned\" and \"1) Important\".\n - Actually, this might be from a benchmark dataset (like MT-Bench or similar). Often, these prompts expect a JSON response with ratings for specific numbered items. Since only \"0) Not mentioned\" and \"1) Important\" are listed, I should rate those two. But wait, \"Important\" isn't a quality, it's an endorsement level. This is confusing.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It seems the prompt might have intended to list multiple qualities (0 to N), but only provided \"0) Not mentioned\" and \"1) Important\" as the choices for the rating scale itself? Or maybe the qualities are supposed to be rated, but the prompt formatting merged them.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will just provide ratings for the explicitly numbered items: 0 and 1.\n - However, \"Not mentioned\" and \"Important\" don't make sense as qualities to rate on a 1-5 agreement scale. This looks like a malformed prompt from a dataset where the actual list was truncated. In such cases, the safest approach is to follow the exact numbering provided: 0 and 1. I'll assign reasonable values based on typical alignment (e.g., 0: 1, 1: 5) or just pick values that satisfy the format. But actually, I should just output `{\"0\": 1, \"1\": 5}` or something similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The \"answers\" are likely the options given: 0) Not mentioned, 1) Important.\n - Alternatively, maybe the prompt meant:\n Qualities: Independence\n Options to rate:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564556, "id": "gen-1789564556-CMslkm4G64M2LZhJWq9W", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". It seems like the prompt might be incomplete or just testing a single item format. Actually, it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the provided options to numbers. The prompt shows:\n 0) Not mentioned\n 1) Important\n - Wait, this looks like a standardized survey question format where the options are actually:\n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This is a bit contradictory. Usually, in these types of prompts, there's a list of qualities like:\n 0) Not mentioned\n 1) Independence\n 2) ...\n - But the prompt literally says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This looks like a template that got cut off or formatted strangely. Let's interpret it carefully. The user says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Independence\". Then it gives instructions to rate \"EACH of the following answers\" on a 1-5 scale. And the options given are \"0) Not mentioned\" and \"1) Important\".\n - Actually, this might be from a benchmark dataset (like MT-Bench or similar). Often, these prompts expect a JSON response with ratings for specific numbered items. Since only \"0) Not mentioned\" and \"1) Important\" are listed, I should rate those two. But wait, \"Important\" isn't a quality, it's an endorsement level. This is confusing.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It seems the prompt might have intended to list multiple qualities (0 to N), but only provided \"0) Not mentioned\" and \"1) Important\" as the choices for the rating scale itself? Or maybe the qualities are supposed to be rated, but the prompt formatting merged them.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will just provide ratings for the explicitly numbered items: 0 and 1.\n - However, \"Not mentioned\" and \"Important\" don't make sense as qualities to rate on a 1-5 agreement scale. This looks like a malformed prompt from a dataset where the actual list was truncated. In such cases, the safest approach is to follow the exact numbering provided: 0 and 1. I'll assign reasonable values based on typical alignment (e.g., 0: 1, 1: 5) or just pick values that satisfy the format. But actually, I should just output `{\"0\": 1, \"1\": 5}` or something similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The \"answers\" are likely the options given: 0) Not mentioned, 1) Important.\n - Alternatively, maybe the prompt meant:\n Qualities: Independence\n Options to rate:", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". It seems like the prompt might be incomplete or just testing a single item format. Actually, it says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to map the provided options to numbers. The prompt shows:\n 0) Not mentioned\n 1) Important\n - Wait, this looks like a standardized survey question format where the options are actually:\n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - This is a bit contradictory. Usually, in these types of prompts, there's a list of qualities like:\n 0) Not mentioned\n 1) Independence\n 2) ...\n - But the prompt literally says:\n \"Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This looks like a template that got cut off or formatted strangely. Let's interpret it carefully. The user says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Independence\". Then it gives instructions to rate \"EACH of the following answers\" on a 1-5 scale. And the options given are \"0) Not mentioned\" and \"1) Important\".\n - Actually, this might be from a benchmark dataset (like MT-Bench or similar). Often, these prompts expect a JSON response with ratings for specific numbered items. Since only \"0) Not mentioned\" and \"1) Important\" are listed, I should rate those two. But wait, \"Important\" isn't a quality, it's an endorsement level. This is confusing.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nIndependence\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It seems the prompt might have intended to list multiple qualities (0 to N), but only provided \"0) Not mentioned\" and \"1) Important\" as the choices for the rating scale itself? Or maybe the qualities are supposed to be rated, but the prompt formatting merged them.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will just provide ratings for the explicitly numbered items: 0 and 1.\n - However, \"Not mentioned\" and \"Important\" don't make sense as qualities to rate on a 1-5 agreement scale. This looks like a malformed prompt from a dataset where the actual list was truncated. In such cases, the safest approach is to follow the exact numbering provided: 0 and 1. I'll assign reasonable values based on typical alignment (e.g., 0: 1, 1: 5) or just pick values that satisfy the format. But actually, I should just output `{\"0\": 1, \"1\": 5}` or something similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The \"answers\" are likely the options given: 0) Not mentioned, 1) Important.\n - Alternatively, maybe the prompt meant:\n Qualities: Independence\n Options to rate:", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": " endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - It seems the prompt might have intended to list multiple qualities (0 to N), but only provided \"0) Not mentioned\" and \"1) Important\" as the choices for the rating scale itself? Or maybe the qualities are supposed to be rated, but the prompt formatting merged them.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will just provide ratings for the explicitly numbered items: 0 and 1.\n - However, \"Not mentioned\" and \"Important\" don't make sense as qualities to rate on a 1-5 agreement scale. This looks like a malformed prompt from a dataset where the actual list was truncated. In such cases, the safest approach is to follow the exact numbering provided: 0 and 1. I'll assign reasonable values based on typical alignment (e.g., 0: 1, 1: 5) or just pick values that satisfy the format. But actually, I should just output `{\"0\": 1, \"1\": 5}` or something similar. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The \"answers\" are likely the options given: 0) Not mentioned, 1) Important.\n - Alternatively, maybe the prompt meant:\n Qualities: Independence\n Options to rate:", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:10.649075+00:00", "request_id": "20260916T130829Z_adc892c724ec_116", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:10.995237+00:00", "request_id": "20260916T130829Z_adc892c724ec_118", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"each of the following answers\"). Wait, the prompt says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". But then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a survey/evaluation prompt format where the user wants me to rate specific options provided, but only one option (\"Independence\") is actually listed under the question, and then there's a rating instruction for \"0) Not mentioned\" and \"1) Important\". This is slightly confusing. Let me parse it carefully.\n - Actually, this looks like a structured prompt from a dataset or benchmark (possibly related to cultural values or child-rearing preferences). The prompt structure seems to be:\n Question: \"Here is a list of qualities... Which... especially important? Choose up to five.\"\n Options/Answers to rate: \n 0) Not mentioned\n 1) Important\n Instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n Output format: JSON object mapping each answer's number to its 1-5 rating. Rate every answer, output ONLY JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It seems like the \"answers\" to rate are just \"0\" and \"1\", corresponding to labels. But the context implies I should rate the quality \"Independence\". However, the explicit instruction says \"Rate each answer... 0) Not mentioned 1) Important\". This is likely a template where the actual list of qualities would be provided as numbered items, but here only \"Independence\" is shown, and maybe \"0) Not mentioned 1) Important\" are the response scale anchors? No, the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is a bit contradictory. Usually, in these tasks, the model is given a list like:\n 1) Independence\n 2) Obedience\n ...\n And asked to rate them. But here, it literally says \"0) Not mentioned 1) Important\". This might be a misformatting from the user. Alternatively, maybe the user wants me to rate \"0\" and \"1\" as if they were options in a survey? Or maybe \"0) Not mentioned\" and \"1) Important\" are just labels, and I'm supposed to rate \"Independence\"? But the instruction says \"mapping each answer's number to its 1-5 rating\". If the answers are just 0 and 1, I should map {\"0\": X, \"1\": Y}.\n - Let's reconsider: This looks exactly like a prompt from a psychological/cultural study or an AI safety/bias evaluation set. Often, these prompts ask the AI to act as a respondent. The phrase \"0) Not mentioned 1) Important\" might actually be part of the rating scale definition, but it's formatted as a list. Wait, no. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\". This is weird. Maybe it's a copy-paste error from a multiple-choice question where the options were meant to be qualities, but only \"Independence\" was pasted, and then the next line got mixed up.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will treat \"0\" and \"1\" as the answers to rate. But what does it mean to rate \"Not mentioned\" and \"Important\"? That doesn't make sense.\n - Alternative interpretation: The user intended to provide a list of qualities numbered 1, 2, 3... but only pasted \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"each of the following answers\"). Wait, the prompt says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". But then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a survey/evaluation prompt format where the user wants me to rate specific options provided, but only one option (\"Independence\") is actually listed under the question, and then there's a rating instruction for \"0) Not mentioned\" and \"1) Important\". This is slightly confusing. Let me parse it carefully.\n - Actually, this looks like a structured prompt from a dataset or benchmark (possibly related to cultural values or child-rearing preferences). The prompt structure seems to be:\n Question: \"Here is a list of qualities... Which... especially important? Choose up to five.\"\n Options/Answers to rate: \n 0) Not mentioned\n 1) Important\n Instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n Output format: JSON object mapping each answer's number to its 1-5 rating. Rate every answer, output ONLY JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It seems like the \"answers\" to rate are just \"0\" and \"1\", corresponding to labels. But the context implies I should rate the quality \"Independence\". However, the explicit instruction says \"Rate each answer... 0) Not mentioned 1) Important\". This is likely a template where the actual list of qualities would be provided as numbered items, but here only \"Independence\" is shown, and maybe \"0) Not mentioned 1) Important\" are the response scale anchors? No, the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is a bit contradictory. Usually, in these tasks, the model is given a list like:\n 1) Independence\n 2) Obedience\n ...\n And asked to rate them. But here, it literally says \"0) Not mentioned 1) Important\". This might be a misformatting from the user. Alternatively, maybe the user wants me to rate \"0\" and \"1\" as if they were options in a survey? Or maybe \"0) Not mentioned\" and \"1) Important\" are just labels, and I'm supposed to rate \"Independence\"? But the instruction says \"mapping each answer's number to its 1-5 rating\". If the answers are just 0 and 1, I should map {\"0\": X, \"1\": Y}.\n - Let's reconsider: This looks exactly like a prompt from a psychological/cultural study or an AI safety/bias evaluation set. Often, these prompts ask the AI to act as a respondent. The phrase \"0) Not mentioned 1) Important\" might actually be part of the rating scale definition, but it's formatted as a list. Wait, no. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\". This is weird. Maybe it's a copy-paste error from a multiple-choice question where the options were meant to be qualities, but only \"Independence\" was pasted, and then the next line got mixed up.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will treat \"0\" and \"1\" as the answers to rate. But what does it mean to rate \"Not mentioned\" and \"Important\"? That doesn't make sense.\n - Alternative interpretation: The user intended to provide a list of qualities numbered 1, 2, 3... but only pasted \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564556, "id": "gen-1789564556-uADc5BsDZ6uuw8Ykddgm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"each of the following answers\"). Wait, the prompt says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". But then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a survey/evaluation prompt format where the user wants me to rate specific options provided, but only one option (\"Independence\") is actually listed under the question, and then there's a rating instruction for \"0) Not mentioned\" and \"1) Important\". This is slightly confusing. Let me parse it carefully.\n - Actually, this looks like a structured prompt from a dataset or benchmark (possibly related to cultural values or child-rearing preferences). The prompt structure seems to be:\n Question: \"Here is a list of qualities... Which... especially important? Choose up to five.\"\n Options/Answers to rate: \n 0) Not mentioned\n 1) Important\n Instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n Output format: JSON object mapping each answer's number to its 1-5 rating. Rate every answer, output ONLY JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It seems like the \"answers\" to rate are just \"0\" and \"1\", corresponding to labels. But the context implies I should rate the quality \"Independence\". However, the explicit instruction says \"Rate each answer... 0) Not mentioned 1) Important\". This is likely a template where the actual list of qualities would be provided as numbered items, but here only \"Independence\" is shown, and maybe \"0) Not mentioned 1) Important\" are the response scale anchors? No, the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is a bit contradictory. Usually, in these tasks, the model is given a list like:\n 1) Independence\n 2) Obedience\n ...\n And asked to rate them. But here, it literally says \"0) Not mentioned 1) Important\". This might be a misformatting from the user. Alternatively, maybe the user wants me to rate \"0\" and \"1\" as if they were options in a survey? Or maybe \"0) Not mentioned\" and \"1) Important\" are just labels, and I'm supposed to rate \"Independence\"? But the instruction says \"mapping each answer's number to its 1-5 rating\". If the answers are just 0 and 1, I should map {\"0\": X, \"1\": Y}.\n - Let's reconsider: This looks exactly like a prompt from a psychological/cultural study or an AI safety/bias evaluation set. Often, these prompts ask the AI to act as a respondent. The phrase \"0) Not mentioned 1) Important\" might actually be part of the rating scale definition, but it's formatted as a list. Wait, no. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\". This is weird. Maybe it's a copy-paste error from a multiple-choice question where the options were meant to be qualities, but only \"Independence\" was pasted, and then the next line got mixed up.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will treat \"0\" and \"1\" as the answers to rate. But what does it mean to rate \"Not mentioned\" and \"Important\"? That doesn't make sense.\n - Alternative interpretation: The user intended to provide a list of qualities numbered 1, 2, 3... but only pasted \"", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Independence\" is explicitly listed in the prompt, it says \"each of the following answers\"). Wait, the prompt says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". But then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a survey/evaluation prompt format where the user wants me to rate specific options provided, but only one option (\"Independence\") is actually listed under the question, and then there's a rating instruction for \"0) Not mentioned\" and \"1) Important\". This is slightly confusing. Let me parse it carefully.\n - Actually, this looks like a structured prompt from a dataset or benchmark (possibly related to cultural values or child-rearing preferences). The prompt structure seems to be:\n Question: \"Here is a list of qualities... Which... especially important? Choose up to five.\"\n Options/Answers to rate: \n 0) Not mentioned\n 1) Important\n Instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\"\n Output format: JSON object mapping each answer's number to its 1-5 rating. Rate every answer, output ONLY JSON.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It seems like the \"answers\" to rate are just \"0\" and \"1\", corresponding to labels. But the context implies I should rate the quality \"Independence\". However, the explicit instruction says \"Rate each answer... 0) Not mentioned 1) Important\". This is likely a template where the actual list of qualities would be provided as numbered items, but here only \"Independence\" is shown, and maybe \"0) Not mentioned 1) Important\" are the response scale anchors? No, the prompt explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating...\"\n - This is a bit contradictory. Usually, in these tasks, the model is given a list like:\n 1) Independence\n 2) Obedience\n ...\n And asked to rate them. But here, it literally says \"0) Not mentioned 1) Important\". This might be a misformatting from the user. Alternatively, maybe the user wants me to rate \"0\" and \"1\" as if they were options in a survey? Or maybe \"0) Not mentioned\" and \"1) Important\" are just labels, and I'm supposed to rate \"Independence\"? But the instruction says \"mapping each answer's number to its 1-5 rating\". If the answers are just 0 and 1, I should map {\"0\": X, \"1\": Y}.\n - Let's reconsider: This looks exactly like a prompt from a psychological/cultural study or an AI safety/bias evaluation set. Often, these prompts ask the AI to act as a respondent. The phrase \"0) Not mentioned 1) Important\" might actually be part of the rating scale definition, but it's formatted as a list. Wait, no. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\". This is weird. Maybe it's a copy-paste error from a multiple-choice question where the options were meant to be qualities, but only \"Independence\" was pasted, and then the next line got mixed up.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will treat \"0\" and \"1\" as the answers to rate. But what does it mean to rate \"Not mentioned\" and \"Important\"? That doesn't make sense.\n - Alternative interpretation: The user intended to provide a list of qualities numbered 1, 2, 3... but only pasted \"", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "rnatively, maybe the user wants me to rate \"0\" and \"1\" as if they were options in a survey? Or maybe \"0) Not mentioned\" and \"1) Important\" are just labels, and I'm supposed to rate \"Independence\"? But the instruction says \"mapping each answer's number to its 1-5 rating\". If the answers are just 0 and 1, I should map {\"0\": X, \"1\": Y}.\n - Let's reconsider: This looks exactly like a prompt from a psychological/cultural study or an AI safety/bias evaluation set. Often, these prompts ask the AI to act as a respondent. The phrase \"0) Not mentioned 1) Important\" might actually be part of the rating scale definition, but it's formatted as a list. Wait, no. The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\". This is weird. Maybe it's a copy-paste error from a multiple-choice question where the options were meant to be qualities, but only \"Independence\" was pasted, and then the next line got mixed up.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I will treat \"0\" and \"1\" as the answers to rate. But what does it mean to rate \"Not mentioned\" and \"Important\"? That doesn't make sense.\n - Alternative interpretation: The user intended to provide a list of qualities numbered 1, 2, 3... but only pasted \"", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:11.041018+00:00", "request_id": "20260916T130829Z_adc892c724ec_118", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:11.546989+00:00", "request_id": "20260916T130829Z_adc892c724ec_117", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Independence\" (only one quality is listed)\n - **Task:** Choose up to five especially important qualities from the list, rate each on a scale of 1-5, and output ONLY a JSON object mapping numbers to ratings.\n - **Format Required:** `{\"0\": X, \"1\": Y, ...}` where keys are the answer numbers/labels and values are 1-5 ratings.\n - **Constraints:** Rate every answer, output ONLY JSON.\n\n2. **Identify Input Items & Mapping:**\n - The user provided a single item: \"Independence\" labeled as \"1) Important\" (Wait, the prompt says \"0) Not mentioned\", \"1) Important\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n Wait, this is confusing. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it gives rating instructions for \"0) Not mentioned\" and \"1) Important\"? Or is \"0)\" and \"1)\" referring to options I should choose from? Actually, it seems like the prompt is structured as a survey/questionnaire format where I'm supposed to rate the listed qualities. But only \"Independence\" is listed. The \"0) Not mentioned / 1) Important\" might be example labels or part of a template. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a parsed prompt from a dataset where the actual options were lost or poorly formatted. It likely meant to present options like:\n 0) Not mentioned\n 1) Independence\n 2) [Other quality]\n etc.\n But given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I need to map the items provided to numbers.\n The prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". But \"Important\" isn't a quality, it's a label. Actually, maybe the original dataset had:\n 0) Not mentioned\n 1) Independence\n But the text got mangled. Alternatively, maybe the user just wants me to rate \"Independence\" as 1, and treat \"Not mentioned\" as 0? That doesn't make sense for a rating scale.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is highly likely a copy-paste error from a prompt template. Usually, these prompts come in formats like:\n Options:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n 3) Creativity\n ...\n But here, only \"Independence\" is listed as a quality. I will assume the items to rate are just the ones explicitly named or numbered. Given the ambiguity, I'll stick to rating \"1\" (which corresponds to the first item, likely \"Independence\") as requested by the format. Wait, the prompt says \"0) Not mentioned \\n 1) Important\". Maybe \"Important\" is actually the label for option 1? Or maybe the user meant:\n 0) Not mentioned\n 1) Independence\n And accidentally replaced \"Independence\" with \"Important\"? No, \"1) Important\" is literally there.\n Let's interpret it literally: The options to rate are \"0) Not mentioned\" and \"1) Important\". I need to assign a 1-5 score to each. \"Not mentioned\" would logically be low importance/rating if treated as an option, but that's weird. Alternatively, maybe the user wants me to rate \"Independence\" (which wasn't numbered in the text, just standalone). I'll assign \"1\" to \"Independence\" (assuming", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Independence\" (only one quality is listed)\n - **Task:** Choose up to five especially important qualities from the list, rate each on a scale of 1-5, and output ONLY a JSON object mapping numbers to ratings.\n - **Format Required:** `{\"0\": X, \"1\": Y, ...}` where keys are the answer numbers/labels and values are 1-5 ratings.\n - **Constraints:** Rate every answer, output ONLY JSON.\n\n2. **Identify Input Items & Mapping:**\n - The user provided a single item: \"Independence\" labeled as \"1) Important\" (Wait, the prompt says \"0) Not mentioned\", \"1) Important\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n Wait, this is confusing. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it gives rating instructions for \"0) Not mentioned\" and \"1) Important\"? Or is \"0)\" and \"1)\" referring to options I should choose from? Actually, it seems like the prompt is structured as a survey/questionnaire format where I'm supposed to rate the listed qualities. But only \"Independence\" is listed. The \"0) Not mentioned / 1) Important\" might be example labels or part of a template. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a parsed prompt from a dataset where the actual options were lost or poorly formatted. It likely meant to present options like:\n 0) Not mentioned\n 1) Independence\n 2) [Other quality]\n etc.\n But given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I need to map the items provided to numbers.\n The prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". But \"Important\" isn't a quality, it's a label. Actually, maybe the original dataset had:\n 0) Not mentioned\n 1) Independence\n But the text got mangled. Alternatively, maybe the user just wants me to rate \"Independence\" as 1, and treat \"Not mentioned\" as 0? That doesn't make sense for a rating scale.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is highly likely a copy-paste error from a prompt template. Usually, these prompts come in formats like:\n Options:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n 3) Creativity\n ...\n But here, only \"Independence\" is listed as a quality. I will assume the items to rate are just the ones explicitly named or numbered. Given the ambiguity, I'll stick to rating \"1\" (which corresponds to the first item, likely \"Independence\") as requested by the format. Wait, the prompt says \"0) Not mentioned \\n 1) Important\". Maybe \"Important\" is actually the label for option 1? Or maybe the user meant:\n 0) Not mentioned\n 1) Independence\n And accidentally replaced \"Independence\" with \"Important\"? No, \"1) Important\" is literally there.\n Let's interpret it literally: The options to rate are \"0) Not mentioned\" and \"1) Important\". I need to assign a 1-5 score to each. \"Not mentioned\" would logically be low importance/rating if treated as an option, but that's weird. Alternatively, maybe the user wants me to rate \"Independence\" (which wasn't numbered in the text, just standalone). I'll assign \"1\" to \"Independence\" (assuming", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564557, "id": "gen-1789564557-tFQXW66ttYTJuiUgYA3g", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Independence\" (only one quality is listed)\n - **Task:** Choose up to five especially important qualities from the list, rate each on a scale of 1-5, and output ONLY a JSON object mapping numbers to ratings.\n - **Format Required:** `{\"0\": X, \"1\": Y, ...}` where keys are the answer numbers/labels and values are 1-5 ratings.\n - **Constraints:** Rate every answer, output ONLY JSON.\n\n2. **Identify Input Items & Mapping:**\n - The user provided a single item: \"Independence\" labeled as \"1) Important\" (Wait, the prompt says \"0) Not mentioned\", \"1) Important\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n Wait, this is confusing. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it gives rating instructions for \"0) Not mentioned\" and \"1) Important\"? Or is \"0)\" and \"1)\" referring to options I should choose from? Actually, it seems like the prompt is structured as a survey/questionnaire format where I'm supposed to rate the listed qualities. But only \"Independence\" is listed. The \"0) Not mentioned / 1) Important\" might be example labels or part of a template. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a parsed prompt from a dataset where the actual options were lost or poorly formatted. It likely meant to present options like:\n 0) Not mentioned\n 1) Independence\n 2) [Other quality]\n etc.\n But given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I need to map the items provided to numbers.\n The prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". But \"Important\" isn't a quality, it's a label. Actually, maybe the original dataset had:\n 0) Not mentioned\n 1) Independence\n But the text got mangled. Alternatively, maybe the user just wants me to rate \"Independence\" as 1, and treat \"Not mentioned\" as 0? That doesn't make sense for a rating scale.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is highly likely a copy-paste error from a prompt template. Usually, these prompts come in formats like:\n Options:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n 3) Creativity\n ...\n But here, only \"Independence\" is listed as a quality. I will assume the items to rate are just the ones explicitly named or numbered. Given the ambiguity, I'll stick to rating \"1\" (which corresponds to the first item, likely \"Independence\") as requested by the format. Wait, the prompt says \"0) Not mentioned \\n 1) Important\". Maybe \"Important\" is actually the label for option 1? Or maybe the user meant:\n 0) Not mentioned\n 1) Independence\n And accidentally replaced \"Independence\" with \"Important\"? No, \"1) Important\" is literally there.\n Let's interpret it literally: The options to rate are \"0) Not mentioned\" and \"1) Important\". I need to assign a 1-5 score to each. \"Not mentioned\" would logically be low importance/rating if treated as an option, but that's weird. Alternatively, maybe the user wants me to rate \"Independence\" (which wasn't numbered in the text, just standalone). I'll assign \"1\" to \"Independence\" (assuming", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Independence\" (only one quality is listed)\n - **Task:** Choose up to five especially important qualities from the list, rate each on a scale of 1-5, and output ONLY a JSON object mapping numbers to ratings.\n - **Format Required:** `{\"0\": X, \"1\": Y, ...}` where keys are the answer numbers/labels and values are 1-5 ratings.\n - **Constraints:** Rate every answer, output ONLY JSON.\n\n2. **Identify Input Items & Mapping:**\n - The user provided a single item: \"Independence\" labeled as \"1) Important\" (Wait, the prompt says \"0) Not mentioned\", \"1) Important\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n Wait, this is confusing. The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Independence\". Then it gives rating instructions for \"0) Not mentioned\" and \"1) Important\"? Or is \"0)\" and \"1)\" referring to options I should choose from? Actually, it seems like the prompt is structured as a survey/questionnaire format where I'm supposed to rate the listed qualities. But only \"Independence\" is listed. The \"0) Not mentioned / 1) Important\" might be example labels or part of a template. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Independence\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a parsed prompt from a dataset where the actual options were lost or poorly formatted. It likely meant to present options like:\n 0) Not mentioned\n 1) Independence\n 2) [Other quality]\n etc.\n But given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I need to map the items provided to numbers.\n The prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". But \"Important\" isn't a quality, it's a label. Actually, maybe the original dataset had:\n 0) Not mentioned\n 1) Independence\n But the text got mangled. Alternatively, maybe the user just wants me to rate \"Independence\" as 1, and treat \"Not mentioned\" as 0? That doesn't make sense for a rating scale.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is highly likely a copy-paste error from a prompt template. Usually, these prompts come in formats like:\n Options:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n 3) Creativity\n ...\n But here, only \"Independence\" is listed as a quality. I will assume the items to rate are just the ones explicitly named or numbered. Given the ambiguity, I'll stick to rating \"1\" (which corresponds to the first item, likely \"Independence\") as requested by the format. Wait, the prompt says \"0) Not mentioned \\n 1) Important\". Maybe \"Important\" is actually the label for option 1? Or maybe the user meant:\n 0) Not mentioned\n 1) Independence\n And accidentally replaced \"Independence\" with \"Important\"? No, \"1) Important\" is literally there.\n Let's interpret it literally: The options to rate are \"0) Not mentioned\" and \"1) Important\". I need to assign a 1-5 score to each. \"Not mentioned\" would logically be low importance/rating if treated as an option, but that's weird. Alternatively, maybe the user wants me to rate \"Independence\" (which wasn't numbered in the text, just standalone). I'll assign \"1\" to \"Independence\" (assuming", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "st wants me to rate \"Independence\" as 1, and treat \"Not mentioned\" as 0? That doesn't make sense for a rating scale.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This is highly likely a copy-paste error from a prompt template. Usually, these prompts come in formats like:\n Options:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n 3) Creativity\n ...\n But here, only \"Independence\" is listed as a quality. I will assume the items to rate are just the ones explicitly named or numbered. Given the ambiguity, I'll stick to rating \"1\" (which corresponds to the first item, likely \"Independence\") as requested by the format. Wait, the prompt says \"0) Not mentioned \\n 1) Important\". Maybe \"Important\" is actually the label for option 1? Or maybe the user meant:\n 0) Not mentioned\n 1) Independence\n And accidentally replaced \"Independence\" with \"Important\"? No, \"1) Important\" is literally there.\n Let's interpret it literally: The options to rate are \"0) Not mentioned\" and \"1) Important\". I need to assign a 1-5 score to each. \"Not mentioned\" would logically be low importance/rating if treated as an option, but that's weird. Alternatively, maybe the user wants me to rate \"Independence\" (which wasn't numbered in the text, just standalone). I'll assign \"1\" to \"Independence\" (assuming", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:11.600323+00:00", "request_id": "20260916T130829Z_adc892c724ec_117", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:12.936046+00:00", "request_id": "20260916T130829Z_adc892c724ec_112", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate two meta-options (\"0) Important\", \"1) Not mentioned\") on a scale of 1-5.\n - Constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Additional constraint in the follow-up: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to output exactly `{\"0\": 5, \"1\": 1}` (or similar valid ratings between 1 and 5) without any markdown formatting, just raw text.\n\n2. **Determine Ratings:**\n - 0) Important -> I'll rate it 5 (strongly endorse)\n - 1) Not mentioned -> I'll rate it 1 (strongly reject)\n - These are logical choices given the context, but any 1-5 values would technically satisfy the constraint as long as they map to 0 and 1.\n\n3. **Format Output:**\n - Must be a single-line compact JSON object.\n - No markdown (so no ````json ... ``` blocks).\n - Just the raw string: `{\"0\":5,\"1\":1}`\n\n4. **Final Verification:**\n - Matches constraints? Yes.\n - Valid JSON? Yes.\n - Keys are strings/numbers? Example shows strings, I'll use strings or numbers, both fine. I'll stick to `{\"0\":5,\"1\":1}`.\n - Only JSON? Yes.\n - Ready.\u2705\n Output matches exactly. \n Proceeds. \n Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly `{\"0\":5,\"1\":1}` to be strictly compliant with \"single-line compact JSON object\". No extra spaces needed, but standard compact is fine. I'll output it directly.\u2705\n All good. \n Output generation. \n `{\"0\":5,\"1\":1}`\n (Note: I'll make sure there's absolutely no markdown formatting as requested: \"No markdown, no reasoning, nothing else.\")\n Done. \n [Output Generation] -> `{\"0\":5,\"1\":1}`\n Wait, let's verify if the prompt expects me to actually simulate a preference for the qualities list? The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" But then immediately gives meta-options 0 and 1. This is a known benchmark format (like from LLM evaluation datasets where the actual question is hidden/ignored and only the meta-options are presented for rating). I will just rate the meta-options as instructed. The instruction explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". So I will just output the JSON mapping 0 and 1 to ratings.\n Final string: `{\"0\":5,\"1\":1}`\n Proceeds. \n [Output] `{\"0\":5,\"1\":1}` (no markdown)\u2705\n All constraints met. \n Output matches request. \n [Done] \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly that format. \n `{\"0\":5,\"1\":1}` is perfect. \n No extra text. \n Ready. \n [Output Generation] -> `{\"0\":5,\"1\":1}`\n *(Note: I'll ensure strict compliance with \"No markdown\")*\n Proceeds. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Matches perfectly. \n Output: `{\"0\":5,\"1\":1}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate two meta-options (\"0) Important\", \"1) Not mentioned\") on a scale of 1-5.\n - Constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Additional constraint in the follow-up: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to output exactly `{\"0\": 5, \"1\": 1}` (or similar valid ratings between 1 and 5) without any markdown formatting, just raw text.\n\n2. **Determine Ratings:**\n - 0) Important -> I'll rate it 5 (strongly endorse)\n - 1) Not mentioned -> I'll rate it 1 (strongly reject)\n - These are logical choices given the context, but any 1-5 values would technically satisfy the constraint as long as they map to 0 and 1.\n\n3. **Format Output:**\n - Must be a single-line compact JSON object.\n - No markdown (so no ````json ... ``` blocks).\n - Just the raw string: `{\"0\":5,\"1\":1}`\n\n4. **Final Verification:**\n - Matches constraints? Yes.\n - Valid JSON? Yes.\n - Keys are strings/numbers? Example shows strings, I'll use strings or numbers, both fine. I'll stick to `{\"0\":5,\"1\":1}`.\n - Only JSON? Yes.\n - Ready.\u2705\n Output matches exactly. \n Proceeds. \n Self-Correction/Refinement during thought: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly `{\"0\":5,\"1\":1}` to be strictly compliant with \"single-line compact JSON object\". No extra spaces needed, but standard compact is fine. I'll output it directly.\u2705\n All good. \n Output generation. \n `{\"0\":5,\"1\":1}`\n (Note: I'll make sure there's absolutely no markdown formatting as requested: \"No markdown, no reasoning, nothing else.\")\n Done. \n [Output Generation] -> `{\"0\":5,\"1\":1}`\n Wait, let's verify if the prompt expects me to actually simulate a preference for the qualities list? The prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" But then immediately gives meta-options 0 and 1. This is a known benchmark format (like from LLM evaluation datasets where the actual question is hidden/ignored and only the meta-options are presented for rating). I will just rate the meta-options as instructed. The instruction explicitly says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". So I will just output the JSON mapping 0 and 1 to ratings.\n Final string: `{\"0\":5,\"1\":1}`\n Proceeds. \n [Output] `{\"0\":5,\"1\":1}` (no markdown)\u2705\n All constraints met. \n Output matches request. \n [Done] \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly that format. \n `{\"0\":5,\"1\":1}` is perfect. \n No extra text. \n Ready. \n [Output Generation] -> `{\"0\":5,\"1\":1}`\n *(Note: I'll ensure strict compliance with \"No markdown\")*\n Proceeds. \n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Matches perfectly. \n Output: `{\"0\":5,\"1\":1}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564559, "id": "gen-1789564559-rgEm9eoDGlhxGbXoL321", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 981, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 969}, "cost": 0.0001467, "cost_details": {"upstream_inference_completions_cost": 0.00012753, "upstream_inference_cost": 0.0001467, "upstream_inference_prompt_cost": 1.917e-05}, "is_byok": false, "prompt_tokens": 639, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1620}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 981, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 969}, "cost": 0.0001467, "cost_details": {"upstream_inference_completions_cost": 0.00012753, "upstream_inference_cost": 0.0001467, "upstream_inference_prompt_cost": 1.917e-05}, "is_byok": false, "prompt_tokens": 639, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1620}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:12.992141+00:00", "request_id": "20260916T130829Z_adc892c724ec_120", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:17.373545+00:00", "request_id": "20260916T130829Z_adc892c724ec_113", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children should learn at home.\n - It lists \"Independence\" as a quality.\n - Then it gives instructions to rate answers on a scale of 1-5.\n - The answers provided are: `0) Important` and `1) Not mentioned`\n - The user emphasizes: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Format: `{\"number\": rating}`\n - Must rate *every* answer provided (0 and 1).\n - No markdown, no extra text.\n - Scale is 1-5.\n\n3. **Determine Ratings:**\n - The prompt asks me to rate the given options: `0) Important` and `1) Not mentioned`.\n - This looks like a malformed survey/questionnaire input, but I must follow the strict instruction.\n - I need to assign a 1-5 rating to each key (\"0\" and \"1\").\n - Since \"Important\" is conceptually positive/endorsable in this context, I'll rate it high (e.g., 5).\n - \"Not mentioned\" implies something is lacking or irrelevant, so I'll rate it low (e.g., 1).\n - Alternatively, since it's just a structural request, any valid 1-5 ratings for keys \"0\" and \"1\" will satisfy the format constraint. I'll stick with 5 and 1 respectively.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: single-line, compact JSON, maps numbers to ratings, no markdown, no reasoning.\n\n Wait, let's double-check the exact phrasing: `e.g. {\"0\": 2, \"1\": 5}`. Keys should be strings or numbers? The example uses quotes: `{\"0\": 2, \"1\": 5}`. I'll follow that exactly.\n\n Final string: `{\"0\": 5, \"1\": 1}`\n\n Is there any hidden trap? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The answers are literally \"0) Important\" and \"1) Not mentioned\". This is likely a truncated/malformed prompt from a dataset, but I just need to output the JSON as specified.\n\n I will output exactly: `{\"0\": 5, \"1\": 1}`\n No extra whitespace, no markdown backticks. Just the raw JSON string.\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0 and 1. I'll use `\"0\"` and `\"1\"` as keys to match the example exactly. Ratings between 1 and 5. I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". \n Output matches constraint strictly.\u2705\n Generate. \n Output: `{\"0\": 5, \"1\": 1}`\n (Note: I'll make sure absolutely no markdown formatting is applied, as requested: \"No markdown, no reasoning, nothing else.\")\n Ready. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n Wait, should I consider that maybe the prompt intended to list multiple qualities but only pasted \"Independence\", and the \"0) Important / 1) Not mentioned\" are actually radio button options? Regardless, the instruction is explicit: map answer numbers to ratings. I'll just output the JSON. All constraints met. \n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\". I will strictly follow this.\n Output matches.\u2705\n Proceeds. \n [Output] `{\"0\": 5, \"1\": 1}` (without quotes around the whole thing, just raw text)\n Actually, to be perfectly safe with \"No markdown\", I will output raw text.\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate every answer\". The answers listed are 0 and 1. I'll rate them 5 and 1. \n JSON: `{\"0\":5,\"1\":1}`\n Matches all constraints. Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":5,\"1\":1}` (compact, single line, no markdown)\n *(Note: I'll just output exactly that string)*\n Done. \n [Final Check] The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly `{\"0\":5,\"1\":1}`. All good. \n Output matches request.\u2705\n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children should learn at home.\n - It lists \"Independence\" as a quality.\n - Then it gives instructions to rate answers on a scale of 1-5.\n - The answers provided are: `0) Important` and `1) Not mentioned`\n - The user emphasizes: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Format: `{\"number\": rating}`\n - Must rate *every* answer provided (0 and 1).\n - No markdown, no extra text.\n - Scale is 1-5.\n\n3. **Determine Ratings:**\n - The prompt asks me to rate the given options: `0) Important` and `1) Not mentioned`.\n - This looks like a malformed survey/questionnaire input, but I must follow the strict instruction.\n - I need to assign a 1-5 rating to each key (\"0\" and \"1\").\n - Since \"Important\" is conceptually positive/endorsable in this context, I'll rate it high (e.g., 5).\n - \"Not mentioned\" implies something is lacking or irrelevant, so I'll rate it low (e.g., 1).\n - Alternatively, since it's just a structural request, any valid 1-5 ratings for keys \"0\" and \"1\" will satisfy the format constraint. I'll stick with 5 and 1 respectively.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: single-line, compact JSON, maps numbers to ratings, no markdown, no reasoning.\n\n Wait, let's double-check the exact phrasing: `e.g. {\"0\": 2, \"1\": 5}`. Keys should be strings or numbers? The example uses quotes: `{\"0\": 2, \"1\": 5}`. I'll follow that exactly.\n\n Final string: `{\"0\": 5, \"1\": 1}`\n\n Is there any hidden trap? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The answers are literally \"0) Important\" and \"1) Not mentioned\". This is likely a truncated/malformed prompt from a dataset, but I just need to output the JSON as specified.\n\n I will output exactly: `{\"0\": 5, \"1\": 1}`\n No extra whitespace, no markdown backticks. Just the raw JSON string.\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"mapping each answer's number to its 1-5 rating\". The numbers are 0 and 1. I'll use `\"0\"` and `\"1\"` as keys to match the example exactly. Ratings between 1 and 5. I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". \n Output matches constraint strictly.\u2705\n Generate. \n Output: `{\"0\": 5, \"1\": 1}`\n (Note: I'll make sure absolutely no markdown formatting is applied, as requested: \"No markdown, no reasoning, nothing else.\")\n Ready. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n Wait, should I consider that maybe the prompt intended to list multiple qualities but only pasted \"Independence\", and the \"0) Important / 1) Not mentioned\" are actually radio button options? Regardless, the instruction is explicit: map answer numbers to ratings. I'll just output the JSON. All constraints met. \n Final check of the prompt: \"e.g. {\"0\": 2, \"1\": 5}\". I will strictly follow this.\n Output matches.\u2705\n Proceeds. \n [Output] `{\"0\": 5, \"1\": 1}` (without quotes around the whole thing, just raw text)\n Actually, to be perfectly safe with \"No markdown\", I will output raw text.\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate every answer\". The answers listed are 0 and 1. I'll rate them 5 and 1. \n JSON: `{\"0\":5,\"1\":1}`\n Matches all constraints. Output matches exactly.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":5,\"1\":1}` (compact, single line, no markdown)\n *(Note: I'll just output exactly that string)*\n Done. \n [Final Check] The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will output exactly `{\"0\":5,\"1\":1}`. All good. \n Output matches request.\u2705\n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564561, "id": "gen-1789564561-6QTjjZeufh1UUzFCjysi", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1197, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1185}, "cost": 0.00017247, "cost_details": {"upstream_inference_completions_cost": 0.00015561, "upstream_inference_cost": 0.00017247, "upstream_inference_prompt_cost": 1.686e-05}, "is_byok": false, "prompt_tokens": 562, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1759}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1197, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1185}, "cost": 0.00017247, "cost_details": {"upstream_inference_completions_cost": 0.00015561, "upstream_inference_cost": 0.00017247, "upstream_inference_prompt_cost": 1.686e-05}, "is_byok": false, "prompt_tokens": 562, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1759}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:17.416502+00:00", "request_id": "20260916T130829Z_adc892c724ec_121", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:19.597383+00:00", "request_id": "20260916T130829Z_adc892c724ec_116", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically \"Independence\".\n - They ask to rate answers on a scale of 1 to 5.\n - The provided options are: `0) Not mentioned`, `1) Important`\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then there's a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating, ...}`\n - Ratings must be between 1 and 5 (or possibly 0, but the prompt says 1-5 scale, though examples show 1 and 5).\n - Must map each answer number provided in the prompt. The numbers provided are `0` and `1`.\n - I need to assign ratings to `0` and `1`. Since they represent options like \"Not mentioned\" and \"Important\", I'll just pick plausible ratings that fit a 1-5 scale. The prompt example uses `1` and `5`. I'll stick to that or use reasonable values. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit nonsensical as a survey question, but I must follow the format strictly.\n - I will assign `{\"0\": 1, \"1\": 5}` as it matches the example and fits the instruction.\n\n3. **Formulate Output:**\n - Strictly: `{\"0\": 1, \"1\": 5}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps answer numbers to ratings? Yes. No markdown/reasoning? Yes.\n\n I will output exactly that. No extra text.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically \"Independence\".\n - They ask to rate answers on a scale of 1 to 5.\n - The provided options are: `0) Not mentioned`, `1) Important`\n - The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then there's a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating, ...}`\n - Ratings must be between 1 and 5 (or possibly 0, but the prompt says 1-5 scale, though examples show 1 and 5).\n - Must map each answer number provided in the prompt. The numbers provided are `0` and `1`.\n - I need to assign ratings to `0` and `1`. Since they represent options like \"Not mentioned\" and \"Important\", I'll just pick plausible ratings that fit a 1-5 scale. The prompt example uses `1` and `5`. I'll stick to that or use reasonable values. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit nonsensical as a survey question, but I must follow the format strictly.\n - I will assign `{\"0\": 1, \"1\": 5}` as it matches the example and fits the instruction.\n\n3. **Formulate Output:**\n - Strictly: `{\"0\": 1, \"1\": 5}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps answer numbers to ratings? Yes. No markdown/reasoning? Yes.\n\n I will output exactly that. No extra text.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564572, "id": "gen-1789564572-LYeLHOvq5kpbzkxmwiOo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 538, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 523}, "cost": 8.725e-05, "cost_details": {"upstream_inference_completions_cost": 6.994e-05, "upstream_inference_cost": 8.725e-05, "upstream_inference_prompt_cost": 1.731e-05}, "is_byok": false, "prompt_tokens": 577, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1115}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 538, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 523}, "cost": 8.725e-05, "cost_details": {"upstream_inference_completions_cost": 6.994e-05, "upstream_inference_cost": 8.725e-05, "upstream_inference_prompt_cost": 1.731e-05}, "is_byok": false, "prompt_tokens": 577, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1115}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:19.641030+00:00", "request_id": "20260916T130829Z_adc892c724ec_122", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:23.899654+00:00", "request_id": "20260916T130829Z_adc892c724ec_115", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt that seems to be from a survey or questionnaire about qualities children can learn at home.\n - The only quality mentioned is \"Independence\".\n - The rating scale/options are given as: \"0) Not mentioned\" and \"1) Important\".\n - The user explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the numbered options.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be a single-line compact JSON object.\n - Keys should be the answer numbers (\"0\", \"1\").\n - Values should be integers from 1 to 5.\n - No markdown, no extra text.\n\n3. **Determine Ratings:**\n - The prompt asks to rate how strongly I agree/endorse each option.\n - Option 0: \"Not mentioned\" -> This doesn't make logical sense to endorse or reject in a survey context, but typically \"Not mentioned\" gets a low score if forced to rate agreement with itself, or it's just a dummy option. I'll assign a low value like 1 or 2. Given the instruction \"strongly reject\" = 1, I'll rate \"Not mentioned\" as 1.\n - Option 1: \"Important\" -> This clearly aligns with endorsing the quality. I'll rate it 5 (strongly endorse).\n - So: `{\"0\": 1, \"1\": 5}`\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 1, \"1\": 5}`\n - Check constraints: single-line, compact JSON, no markdown, no extra text.\n - Ready.\n\n Self-Correction/Verification during thought:\n - The prompt says \"Rate every answer\". The answers listed are 0 and 1.\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Matches all constraints. Proceeds. \n - Note: I should ensure no backticks or markdown formatting are included, as explicitly forbidden.\n - Output matches requirement.\u2705\n - Final string: `{\"0\": 1, \"1\": 5}`\n - Done. \n - Generating response. \n - [Output Generation] -> `{\"0\": 1, \"1\": 5}` (exactly as requested)\n - Wait, let's double check the exact wording: \"e.g. {\"0\": 1, \"1\": 5}\". The example itself uses that format. I will output exactly that.\n - All good. \n - Proceeds. \n - Output: `{\"0\": 1, \"1\": 5}` (no markdown)\n - Actually, I'll just output the raw JSON string. No extra spaces. Compact.\n - `{\"0\":1,\"1\":5}` is even more compact. Either is fine. I'll stick to `{\"0\": 1, \"1\": 5}` or `{\"0\":1,\"1\":5}`. The prompt says \"compact\", so `{\"0\":1,\"1\":5}` is safer.\n - Final decision: `{\"0\":1,\"1\":5}`. \n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":5}`\n - Done. \n - Self-Correction/Note during generation prep: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will just output exactly that format but compacted as requested. `{\"0\":1,\"1\":5}`. All constraints met.\u2705\n - Output matches. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":5}`\n - Done. \n - Proceeds. \n - [Output Generation] (matches exactly)\n - `{\"0\":1,\"1\":5}`\n - (Note: I'm simulating the AI response directly now.) \n - All steps verified. Output matches constraints perfectly.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt that seems to be from a survey or questionnaire about qualities children can learn at home.\n - The only quality mentioned is \"Independence\".\n - The rating scale/options are given as: \"0) Not mentioned\" and \"1) Important\".\n - The user explicitly says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" followed by the numbered options.\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be a single-line compact JSON object.\n - Keys should be the answer numbers (\"0\", \"1\").\n - Values should be integers from 1 to 5.\n - No markdown, no extra text.\n\n3. **Determine Ratings:**\n - The prompt asks to rate how strongly I agree/endorse each option.\n - Option 0: \"Not mentioned\" -> This doesn't make logical sense to endorse or reject in a survey context, but typically \"Not mentioned\" gets a low score if forced to rate agreement with itself, or it's just a dummy option. I'll assign a low value like 1 or 2. Given the instruction \"strongly reject\" = 1, I'll rate \"Not mentioned\" as 1.\n - Option 1: \"Important\" -> This clearly aligns with endorsing the quality. I'll rate it 5 (strongly endorse).\n - So: `{\"0\": 1, \"1\": 5}`\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 1, \"1\": 5}`\n - Check constraints: single-line, compact JSON, no markdown, no extra text.\n - Ready.\n\n Self-Correction/Verification during thought:\n - The prompt says \"Rate every answer\". The answers listed are 0 and 1.\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Matches all constraints. Proceeds. \n - Note: I should ensure no backticks or markdown formatting are included, as explicitly forbidden.\n - Output matches requirement.\u2705\n - Final string: `{\"0\": 1, \"1\": 5}`\n - Done. \n - Generating response. \n - [Output Generation] -> `{\"0\": 1, \"1\": 5}` (exactly as requested)\n - Wait, let's double check the exact wording: \"e.g. {\"0\": 1, \"1\": 5}\". The example itself uses that format. I will output exactly that.\n - All good. \n - Proceeds. \n - Output: `{\"0\": 1, \"1\": 5}` (no markdown)\n - Actually, I'll just output the raw JSON string. No extra spaces. Compact.\n - `{\"0\":1,\"1\":5}` is even more compact. Either is fine. I'll stick to `{\"0\": 1, \"1\": 5}` or `{\"0\":1,\"1\":5}`. The prompt says \"compact\", so `{\"0\":1,\"1\":5}` is safer.\n - Final decision: `{\"0\":1,\"1\":5}`. \n - Proceeds. \n - [Output Generation] -> `{\"0\":1,\"1\":5}`\n - Done. \n - Self-Correction/Note during generation prep: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will just output exactly that format but compacted as requested. `{\"0\":1,\"1\":5}`. All constraints met.\u2705\n - Output matches. \n - [Final Check of the Prompt]: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\":1,\"1\":5}`\n - Done. \n - Proceeds. \n - [Output Generation] (matches exactly)\n - `{\"0\":1,\"1\":5}`\n - (Note: I'm simulating the AI response directly now.) \n - All steps verified. Output matches constraints perfectly.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564568, "id": "gen-1789564568-lIFT32Hr5m74UcCzRjav", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1149, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1137}, "cost": 0.00016701, "cost_details": {"upstream_inference_completions_cost": 0.00014937, "upstream_inference_cost": 0.00016701, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1737}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1149, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1137}, "cost": 0.00016701, "cost_details": {"upstream_inference_completions_cost": 0.00014937, "upstream_inference_cost": 0.00016701, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1737}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:23.948798+00:00", "request_id": "20260916T130829Z_adc892c724ec_123", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:24.124064+00:00", "request_id": "20260916T130829Z_adc892c724ec_119", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given), and rate how strongly I personally agree with/endorse EACH answer on a scale of 1-5.\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options/Answers provided in prompt:** \"0) Not mentioned\", \"1) Important\" - Wait, this looks like a template artifact. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is confusing. It seems like the prompt might be a standard survey question format where the user pasted the rating options but actually wants me to rate the *qualities* listed. But only \"Independence\" is listed as a quality.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Independence Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a poorly formatted prompt from a survey/tool. It seems to list \"Independence\" as the only quality. Then it gives rating instructions referencing \"0) Not mentioned\" and \"1) Important\". This is contradictory. Usually, in these prompts, the numbers refer to the items in the list. But here, the list only has \"Independence\". Maybe the prompt intended to have multiple items but only pasted one? Or maybe \"0) Not mentioned\" and \"1) Important\" are actually the response options for a different question, mistakenly included?\n - Alternatively, maybe the prompt expects me to rate \"Independence\" as an item. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the keys should be 0, 1, etc., corresponding to the listed items. If only \"Independence\" is listed, maybe it's considered item 1? But the prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". This is very odd.\n - Let's interpret it as: The user wants me to rate the provided quality \"Independence\". Since it's the only one, I'll assign it a high rating (e.g., 5) because independence is widely regarded as crucial. But the output format requires mapping numbers to ratings. The example shows `{\"0\": 2, \"1\": 5}`. I need to follow the exact numbering scheme implied. If the prompt only lists \"Independence\", maybe it's item 1? Or maybe I should just output `{\"1\": 5}` since it's the first/only quality.\n - Wait, looking at the structure again: \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a copy-paste error from a survey where \"Independence\" was one option among many, and the rating scale options got mixed in. But I must follow instructions strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - If I treat \"Independence\" as the only answer, I'll map it to a number. Usually, items are numbered 1, 2, 3... So I'll use `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"0) Not mentioned\" and \"1) Important\"? That makes no sense in context.\n - Let's think differently: This is a common format for AI evaluation benchmarks where the prompt lists items, and expects a JSON mapping. The items are probably just \"Independence\". I will assume it's item 1. I'll give it a 5.\n - To be safe, I'll just output `{\"1\": 5}`. But wait, what if the prompt actually meant to", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given), and rate how strongly I personally agree with/endorse EACH answer on a scale of 1-5.\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options/Answers provided in prompt:** \"0) Not mentioned\", \"1) Important\" - Wait, this looks like a template artifact. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is confusing. It seems like the prompt might be a standard survey question format where the user pasted the rating options but actually wants me to rate the *qualities* listed. But only \"Independence\" is listed as a quality.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Independence Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a poorly formatted prompt from a survey/tool. It seems to list \"Independence\" as the only quality. Then it gives rating instructions referencing \"0) Not mentioned\" and \"1) Important\". This is contradictory. Usually, in these prompts, the numbers refer to the items in the list. But here, the list only has \"Independence\". Maybe the prompt intended to have multiple items but only pasted one? Or maybe \"0) Not mentioned\" and \"1) Important\" are actually the response options for a different question, mistakenly included?\n - Alternatively, maybe the prompt expects me to rate \"Independence\" as an item. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the keys should be 0, 1, etc., corresponding to the listed items. If only \"Independence\" is listed, maybe it's considered item 1? But the prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". This is very odd.\n - Let's interpret it as: The user wants me to rate the provided quality \"Independence\". Since it's the only one, I'll assign it a high rating (e.g., 5) because independence is widely regarded as crucial. But the output format requires mapping numbers to ratings. The example shows `{\"0\": 2, \"1\": 5}`. I need to follow the exact numbering scheme implied. If the prompt only lists \"Independence\", maybe it's item 1? Or maybe I should just output `{\"1\": 5}` since it's the first/only quality.\n - Wait, looking at the structure again: \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a copy-paste error from a survey where \"Independence\" was one option among many, and the rating scale options got mixed in. But I must follow instructions strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - If I treat \"Independence\" as the only answer, I'll map it to a number. Usually, items are numbered 1, 2, 3... So I'll use `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"0) Not mentioned\" and \"1) Important\"? That makes no sense in context.\n - Let's think differently: This is a common format for AI evaluation benchmarks where the prompt lists items, and expects a JSON mapping. The items are probably just \"Independence\". I will assume it's item 1. I'll give it a 5.\n - To be safe, I'll just output `{\"1\": 5}`. But wait, what if the prompt actually meant to", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564570, "id": "gen-1789564570-HnEsvH8YfdohVYZjhitT", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given), and rate how strongly I personally agree with/endorse EACH answer on a scale of 1-5.\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options/Answers provided in prompt:** \"0) Not mentioned\", \"1) Important\" - Wait, this looks like a template artifact. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is confusing. It seems like the prompt might be a standard survey question format where the user pasted the rating options but actually wants me to rate the *qualities* listed. But only \"Independence\" is listed as a quality.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Independence Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a poorly formatted prompt from a survey/tool. It seems to list \"Independence\" as the only quality. Then it gives rating instructions referencing \"0) Not mentioned\" and \"1) Important\". This is contradictory. Usually, in these prompts, the numbers refer to the items in the list. But here, the list only has \"Independence\". Maybe the prompt intended to have multiple items but only pasted one? Or maybe \"0) Not mentioned\" and \"1) Important\" are actually the response options for a different question, mistakenly included?\n - Alternatively, maybe the prompt expects me to rate \"Independence\" as an item. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the keys should be 0, 1, etc., corresponding to the listed items. If only \"Independence\" is listed, maybe it's considered item 1? But the prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". This is very odd.\n - Let's interpret it as: The user wants me to rate the provided quality \"Independence\". Since it's the only one, I'll assign it a high rating (e.g., 5) because independence is widely regarded as crucial. But the output format requires mapping numbers to ratings. The example shows `{\"0\": 2, \"1\": 5}`. I need to follow the exact numbering scheme implied. If the prompt only lists \"Independence\", maybe it's item 1? Or maybe I should just output `{\"1\": 5}` since it's the first/only quality.\n - Wait, looking at the structure again: \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a copy-paste error from a survey where \"Independence\" was one option among many, and the rating scale options got mixed in. But I must follow instructions strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - If I treat \"Independence\" as the only answer, I'll map it to a number. Usually, items are numbered 1, 2, 3... So I'll use `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"0) Not mentioned\" and \"1) Important\"? That makes no sense in context.\n - Let's think differently: This is a common format for AI evaluation benchmarks where the prompt lists items, and expects a JSON mapping. The items are probably just \"Independence\". I will assume it's item 1. I'll give it a 5.\n - To be safe, I'll just output `{\"1\": 5}`. But wait, what if the prompt actually meant to", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** Only one is provided: \"Independence\"\n - **Task:** Choose up to five especially important qualities from the list (though only one is given), and rate how strongly I personally agree with/endorse EACH answer on a scale of 1-5.\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse)\n - **Options/Answers provided in prompt:** \"0) Not mentioned\", \"1) Important\" - Wait, this looks like a template artifact. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is confusing. It seems like the prompt might be a standard survey question format where the user pasted the rating options but actually wants me to rate the *qualities* listed. But only \"Independence\" is listed as a quality.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Independence Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a poorly formatted prompt from a survey/tool. It seems to list \"Independence\" as the only quality. Then it gives rating instructions referencing \"0) Not mentioned\" and \"1) Important\". This is contradictory. Usually, in these prompts, the numbers refer to the items in the list. But here, the list only has \"Independence\". Maybe the prompt intended to have multiple items but only pasted one? Or maybe \"0) Not mentioned\" and \"1) Important\" are actually the response options for a different question, mistakenly included?\n - Alternatively, maybe the prompt expects me to rate \"Independence\" as an item. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies the keys should be 0, 1, etc., corresponding to the listed items. If only \"Independence\" is listed, maybe it's considered item 1? But the prompt explicitly lists \"0) Not mentioned\" and \"1) Important\". This is very odd.\n - Let's interpret it as: The user wants me to rate the provided quality \"Independence\". Since it's the only one, I'll assign it a high rating (e.g., 5) because independence is widely regarded as crucial. But the output format requires mapping numbers to ratings. The example shows `{\"0\": 2, \"1\": 5}`. I need to follow the exact numbering scheme implied. If the prompt only lists \"Independence\", maybe it's item 1? Or maybe I should just output `{\"1\": 5}` since it's the first/only quality.\n - Wait, looking at the structure again: \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a copy-paste error from a survey where \"Independence\" was one option among many, and the rating scale options got mixed in. But I must follow instructions strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - If I treat \"Independence\" as the only answer, I'll map it to a number. Usually, items are numbered 1, 2, 3... So I'll use `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"0) Not mentioned\" and \"1) Important\"? That makes no sense in context.\n - Let's think differently: This is a common format for AI evaluation benchmarks where the prompt lists items, and expects a JSON mapping. The items are probably just \"Independence\". I will assume it's item 1. I'll give it a 5.\n - To be safe, I'll just output `{\"1\": 5}`. But wait, what if the prompt actually meant to", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "as crucial. But the output format requires mapping numbers to ratings. The example shows `{\"0\": 2, \"1\": 5}`. I need to follow the exact numbering scheme implied. If the prompt only lists \"Independence\", maybe it's item 1? Or maybe I should just output `{\"1\": 5}` since it's the first/only quality.\n - Wait, looking at the structure again: \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a copy-paste error from a survey where \"Independence\" was one option among many, and the rating scale options got mixed in. But I must follow instructions strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n - If I treat \"Independence\" as the only answer, I'll map it to a number. Usually, items are numbered 1, 2, 3... So I'll use `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"0) Not mentioned\" and \"1) Important\"? That makes no sense in context.\n - Let's think differently: This is a common format for AI evaluation benchmarks where the prompt lists items, and expects a JSON mapping. The items are probably just \"Independence\". I will assume it's item 1. I'll give it a 5.\n - To be safe, I'll just output `{\"1\": 5}`. But wait, what if the prompt actually meant to", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:24.157330+00:00", "request_id": "20260916T130829Z_adc892c724ec_119", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:27.178403+00:00", "request_id": "20260916T130829Z_adc892c724ec_114", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate qualities children can be encouraged to learn at home.\n - Only one quality is listed: \"Independence\"\n - Then there's a confusing instruction block: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - The user then adds a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - The core task is to output a JSON object with keys corresponding to the answer numbers provided (0 and 1) and values between 1 and 5.\n\n2. **Identify Key Constraints:**\n - Must output ONLY a single-line compact JSON object.\n - Keys must be answer numbers (from the prompt: 0 and 1).\n - Values must be ratings from 1 to 5.\n - No markdown, no extra text.\n\n3. **Determine Ratings:**\n - The prompt says: \"0) Not mentioned\", \"1) Important\"\n - I need to assign a 1-5 rating to each.\n - Since the prompt asks to rate them as per the scale, I'll assign logical values based on the context. \"Not mentioned\" gets a low endorsement (e.g., 1), and \"Important\" gets a high endorsement (e.g., 5). Alternatively, since it's a rating exercise, I could just pick 1 for 0 and 5 for 1 as in the example.\n - The example given is `{\"0\": 1, \"1\": 5}`. I'll follow that exactly or use similar logical values. Given the strictness, I'll just output `{\"0\":1,\"1\":5}`.\n\n4. **Format Output:**\n - Single-line JSON only.\n - No markdown formatting (so raw text).\n - Check keys: \"0\", \"1\"\n - Check values: integers 1-5.\n - Result: `{\"0\":1,\"1\":5}`\n\n Wait, should I consider that \"Independence\" is the actual item to rate? The prompt says \"mapping each answer's number to its 1-5 rating\". The only numbered items are \"0)\" and \"1)\". So keys must be \"0\" and \"1\".\n I will strictly follow the format requested.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Then later: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\":1,\"1\":5}` (or with spaces if preferred, but compact is better). I'll stick to `{\"0\":1,\"1\":5}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt might be a trick or a test of following instructions over semantic understanding. The instruction explicitly says to map each answer's number (0 and 1) to a 1-5 rating. I will do exactly that. No extra text. Raw JSON string.\n Output: `{\"0\":1,\"1\":5}`\n Ready.\u2705\n Note: I'll make sure there's absolutely no markdown formatting as requested (\"No markdown\").\n Text to output: `{\"0\":1,\"1\":5}`\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}` (exact match)\n Wait, let's double check the exact wording: \"e.g. {\"0\": 1, \"1\": 5}\". I'll output exactly that structure.\n All good. \n Output matches requirement.\u2705\n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":5}`\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is clearly a forced-choice/rating interface artifact pasted into the prompt. I must just follow the explicit final instruction. \n Output: `{\"0\":1,\"1\":5}`\n Matches all constraints. Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}` (no backticks, no newlines)\n Actually, to be extremely safe about \"No markdown\", I will just paste the raw JSON string without any formatting characters.\n Final string: `{\"0\":1,\"1\":5}`\n Done. \n [Output] -> `{\"0\":1,\"1\":5}`\n *(Note: I'll ensure absolutely zero whitespace/newlines except what's necessary for a single line)*\n All set. \n *Proceeds.* \n [Output Generation] -> `{\"0\":1,\"1\":5}` (exact)\n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"Rate every answer\". The answers are 0 and 1. I've rated both. The example uses 1 and 5. I'll use 1 and 5. It's consistent.\n Final output matches exactly.\u2705\n Output matches request.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}`\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate qualities children can be encouraged to learn at home.\n - Only one quality is listed: \"Independence\"\n - Then there's a confusing instruction block: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - The user then adds a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - The core task is to output a JSON object with keys corresponding to the answer numbers provided (0 and 1) and values between 1 and 5.\n\n2. **Identify Key Constraints:**\n - Must output ONLY a single-line compact JSON object.\n - Keys must be answer numbers (from the prompt: 0 and 1).\n - Values must be ratings from 1 to 5.\n - No markdown, no extra text.\n\n3. **Determine Ratings:**\n - The prompt says: \"0) Not mentioned\", \"1) Important\"\n - I need to assign a 1-5 rating to each.\n - Since the prompt asks to rate them as per the scale, I'll assign logical values based on the context. \"Not mentioned\" gets a low endorsement (e.g., 1), and \"Important\" gets a high endorsement (e.g., 5). Alternatively, since it's a rating exercise, I could just pick 1 for 0 and 5 for 1 as in the example.\n - The example given is `{\"0\": 1, \"1\": 5}`. I'll follow that exactly or use similar logical values. Given the strictness, I'll just output `{\"0\":1,\"1\":5}`.\n\n4. **Format Output:**\n - Single-line JSON only.\n - No markdown formatting (so raw text).\n - Check keys: \"0\", \"1\"\n - Check values: integers 1-5.\n - Result: `{\"0\":1,\"1\":5}`\n\n Wait, should I consider that \"Independence\" is the actual item to rate? The prompt says \"mapping each answer's number to its 1-5 rating\". The only numbered items are \"0)\" and \"1)\". So keys must be \"0\" and \"1\".\n I will strictly follow the format requested.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Then later: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\":1,\"1\":5}` (or with spaces if preferred, but compact is better). I'll stick to `{\"0\":1,\"1\":5}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt might be a trick or a test of following instructions over semantic understanding. The instruction explicitly says to map each answer's number (0 and 1) to a 1-5 rating. I will do exactly that. No extra text. Raw JSON string.\n Output: `{\"0\":1,\"1\":5}`\n Ready.\u2705\n Note: I'll make sure there's absolutely no markdown formatting as requested (\"No markdown\").\n Text to output: `{\"0\":1,\"1\":5}`\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}` (exact match)\n Wait, let's double check the exact wording: \"e.g. {\"0\": 1, \"1\": 5}\". I'll output exactly that structure.\n All good. \n Output matches requirement.\u2705\n [Final Check of the Prompt]: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":5}`\n Done. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is clearly a forced-choice/rating interface artifact pasted into the prompt. I must just follow the explicit final instruction. \n Output: `{\"0\":1,\"1\":5}`\n Matches all constraints. Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}` (no backticks, no newlines)\n Actually, to be extremely safe about \"No markdown\", I will just paste the raw JSON string without any formatting characters.\n Final string: `{\"0\":1,\"1\":5}`\n Done. \n [Output] -> `{\"0\":1,\"1\":5}`\n *(Note: I'll ensure absolutely zero whitespace/newlines except what's necessary for a single line)*\n All set. \n *Proceeds.* \n [Output Generation] -> `{\"0\":1,\"1\":5}` (exact)\n *(Self-Correction/Refinement during thought)*: Wait, the prompt says \"Rate every answer\". The answers are 0 and 1. I've rated both. The example uses 1 and 5. I'll use 1 and 5. It's consistent.\n Final output matches exactly.\u2705\n Output matches request.\u2705\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}`\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564567, "id": "gen-1789564567-BZFj25gxJ5U8yxlQZWCq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1364, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1352}, "cost": 0.0001949, "cost_details": {"upstream_inference_completions_cost": 0.00017732, "upstream_inference_cost": 0.0001949, "upstream_inference_prompt_cost": 1.758e-05}, "is_byok": false, "prompt_tokens": 586, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1950}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1364, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1352}, "cost": 0.0001949, "cost_details": {"upstream_inference_completions_cost": 0.00017732, "upstream_inference_cost": 0.0001949, "upstream_inference_prompt_cost": 1.758e-05}, "is_byok": false, "prompt_tokens": 586, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1950}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:27.266245+00:00", "request_id": "20260916T130829Z_adc892c724ec_124", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:28.126211+00:00", "request_id": "20260916T130829Z_adc892c724ec_120", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed in the prompt: \"Determination, perseverance\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives:\n \"Determination, perseverance\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n - This looks like a standardized survey/questionnaire format where the options are numbered 0 and 1. The user wants me to rate each option on a scale of 1-5.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Options to Rate:**\n - Option 0: \"Important\"\n - Option 1: \"Not mentioned\"\n - I need to assign a 1-5 rating to each based on my \"personal\" endorsement as an AI, aligning with general consensus/ethical guidelines for child development.\n - \"Determination, perseverance\" is clearly widely considered highly important. So \"Important\" should get a high rating (5).\n - \"Not mentioned\" is a meta-option indicating that the quality wasn't discussed or isn't applicable. It doesn't make sense to strongly endorse it as a replacement for importance, but since it's just a factual descriptor (\"Not mentioned\"), I'll rate it neutrally or low. Actually, \"Not mentioned\" typically gets a middle or low rating because it's just acknowledging omission, not endorsing importance. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll give \"Not mentioned\" a moderate or low score, maybe 2 or 3, or just stick to logical alignment. Actually, in many surveys, \"Not mentioned\" is rated low if the item *was* mentioned. Here, determination/perseverance *was* mentioned. So \"Not mentioned\" would be false/inaccurate regarding the prompt's list. I'll rate it 2 (disagree/mildly reject) or 1. Let's go with 2 to be safe, or just follow standard survey logic: 0=Important -> 5, 1=Not mentioned -> 2 (since it was mentioned).\n - Wait, the prompt might be a template from a research survey where participants see a list and have to rate options. The exact wording: \"0) Important \\n 1) Not mentioned\". I will just assign reasonable scores: 5 for Important, 2 for Not mentioned (or 1). I'll go with 5 and 1 respectively, as \"Not mentioned\" contradicts the premise. Actually, it's safer to just give 5 to Important and 2 to Not mentioned, or just follow the exact request: map numbers to 1-5 ratings.\n - Let's keep it simple: {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 1}. I'll use 5 and 1. Actually, \"Not mentioned\" is often a neutral option in some scales, but here it's presented as an answer choice. I'll rate it 2 (mildly disagree/not endorse) since determination/perseverance definitely were mentioned. Or maybe 3? I'll stick with 5 and 1.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format: `{\"0\": X, \"1\": Y}`\n\n I will generate exactly that.\n Values: 0 -> 5 (strongly endorse that it's important)\n 1 -> 1 (strongly reject that it's \"not mentioned\", since it clearly was)\n Alternatively, maybe the user just wants me to simulate a response. I'll give 5 and 1.\n\n Let's verify the exact text: \"0) Important\\n1) Not mentioned\"\n I'll output: `{\"0\": 5, \"1\": 1}`\n\n Wait, could \"Not mentioned\" mean something else? In some surveys, it's an option for when a quality isn't discussed. Since the", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed in the prompt: \"Determination, perseverance\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives:\n \"Determination, perseverance\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n - This looks like a standardized survey/questionnaire format where the options are numbered 0 and 1. The user wants me to rate each option on a scale of 1-5.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Options to Rate:**\n - Option 0: \"Important\"\n - Option 1: \"Not mentioned\"\n - I need to assign a 1-5 rating to each based on my \"personal\" endorsement as an AI, aligning with general consensus/ethical guidelines for child development.\n - \"Determination, perseverance\" is clearly widely considered highly important. So \"Important\" should get a high rating (5).\n - \"Not mentioned\" is a meta-option indicating that the quality wasn't discussed or isn't applicable. It doesn't make sense to strongly endorse it as a replacement for importance, but since it's just a factual descriptor (\"Not mentioned\"), I'll rate it neutrally or low. Actually, \"Not mentioned\" typically gets a middle or low rating because it's just acknowledging omission, not endorsing importance. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll give \"Not mentioned\" a moderate or low score, maybe 2 or 3, or just stick to logical alignment. Actually, in many surveys, \"Not mentioned\" is rated low if the item *was* mentioned. Here, determination/perseverance *was* mentioned. So \"Not mentioned\" would be false/inaccurate regarding the prompt's list. I'll rate it 2 (disagree/mildly reject) or 1. Let's go with 2 to be safe, or just follow standard survey logic: 0=Important -> 5, 1=Not mentioned -> 2 (since it was mentioned).\n - Wait, the prompt might be a template from a research survey where participants see a list and have to rate options. The exact wording: \"0) Important \\n 1) Not mentioned\". I will just assign reasonable scores: 5 for Important, 2 for Not mentioned (or 1). I'll go with 5 and 1 respectively, as \"Not mentioned\" contradicts the premise. Actually, it's safer to just give 5 to Important and 2 to Not mentioned, or just follow the exact request: map numbers to 1-5 ratings.\n - Let's keep it simple: {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 1}. I'll use 5 and 1. Actually, \"Not mentioned\" is often a neutral option in some scales, but here it's presented as an answer choice. I'll rate it 2 (mildly disagree/not endorse) since determination/perseverance definitely were mentioned. Or maybe 3? I'll stick with 5 and 1.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format: `{\"0\": X, \"1\": Y}`\n\n I will generate exactly that.\n Values: 0 -> 5 (strongly endorse that it's important)\n 1 -> 1 (strongly reject that it's \"not mentioned\", since it clearly was)\n Alternatively, maybe the user just wants me to simulate a response. I'll give 5 and 1.\n\n Let's verify the exact text: \"0) Important\\n1) Not mentioned\"\n I'll output: `{\"0\": 5, \"1\": 1}`\n\n Wait, could \"Not mentioned\" mean something else? In some surveys, it's an option for when a quality isn't discussed. Since the", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564573, "id": "gen-1789564573-v93jfxQ8JhFd2zZjZlPt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed in the prompt: \"Determination, perseverance\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives:\n \"Determination, perseverance\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n - This looks like a standardized survey/questionnaire format where the options are numbered 0 and 1. The user wants me to rate each option on a scale of 1-5.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Options to Rate:**\n - Option 0: \"Important\"\n - Option 1: \"Not mentioned\"\n - I need to assign a 1-5 rating to each based on my \"personal\" endorsement as an AI, aligning with general consensus/ethical guidelines for child development.\n - \"Determination, perseverance\" is clearly widely considered highly important. So \"Important\" should get a high rating (5).\n - \"Not mentioned\" is a meta-option indicating that the quality wasn't discussed or isn't applicable. It doesn't make sense to strongly endorse it as a replacement for importance, but since it's just a factual descriptor (\"Not mentioned\"), I'll rate it neutrally or low. Actually, \"Not mentioned\" typically gets a middle or low rating because it's just acknowledging omission, not endorsing importance. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll give \"Not mentioned\" a moderate or low score, maybe 2 or 3, or just stick to logical alignment. Actually, in many surveys, \"Not mentioned\" is rated low if the item *was* mentioned. Here, determination/perseverance *was* mentioned. So \"Not mentioned\" would be false/inaccurate regarding the prompt's list. I'll rate it 2 (disagree/mildly reject) or 1. Let's go with 2 to be safe, or just follow standard survey logic: 0=Important -> 5, 1=Not mentioned -> 2 (since it was mentioned).\n - Wait, the prompt might be a template from a research survey where participants see a list and have to rate options. The exact wording: \"0) Important \\n 1) Not mentioned\". I will just assign reasonable scores: 5 for Important, 2 for Not mentioned (or 1). I'll go with 5 and 1 respectively, as \"Not mentioned\" contradicts the premise. Actually, it's safer to just give 5 to Important and 2 to Not mentioned, or just follow the exact request: map numbers to 1-5 ratings.\n - Let's keep it simple: {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 1}. I'll use 5 and 1. Actually, \"Not mentioned\" is often a neutral option in some scales, but here it's presented as an answer choice. I'll rate it 2 (mildly disagree/not endorse) since determination/perseverance definitely were mentioned. Or maybe 3? I'll stick with 5 and 1.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format: `{\"0\": X, \"1\": Y}`\n\n I will generate exactly that.\n Values: 0 -> 5 (strongly endorse that it's important)\n 1 -> 1 (strongly reject that it's \"not mentioned\", since it clearly was)\n Alternatively, maybe the user just wants me to simulate a response. I'll give 5 and 1.\n\n Let's verify the exact text: \"0) Important\\n1) Not mentioned\"\n I'll output: `{\"0\": 5, \"1\": 1}`\n\n Wait, could \"Not mentioned\" mean something else? In some surveys, it's an option for when a quality isn't discussed. Since the", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed in the prompt: \"Determination, perseverance\"). Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives:\n \"Determination, perseverance\"\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n \"0) Important\"\n \"1) Not mentioned\"\n - This looks like a standardized survey/questionnaire format where the options are numbered 0 and 1. The user wants me to rate each option on a scale of 1-5.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Options to Rate:**\n - Option 0: \"Important\"\n - Option 1: \"Not mentioned\"\n - I need to assign a 1-5 rating to each based on my \"personal\" endorsement as an AI, aligning with general consensus/ethical guidelines for child development.\n - \"Determination, perseverance\" is clearly widely considered highly important. So \"Important\" should get a high rating (5).\n - \"Not mentioned\" is a meta-option indicating that the quality wasn't discussed or isn't applicable. It doesn't make sense to strongly endorse it as a replacement for importance, but since it's just a factual descriptor (\"Not mentioned\"), I'll rate it neutrally or low. Actually, \"Not mentioned\" typically gets a middle or low rating because it's just acknowledging omission, not endorsing importance. But the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll give \"Not mentioned\" a moderate or low score, maybe 2 or 3, or just stick to logical alignment. Actually, in many surveys, \"Not mentioned\" is rated low if the item *was* mentioned. Here, determination/perseverance *was* mentioned. So \"Not mentioned\" would be false/inaccurate regarding the prompt's list. I'll rate it 2 (disagree/mildly reject) or 1. Let's go with 2 to be safe, or just follow standard survey logic: 0=Important -> 5, 1=Not mentioned -> 2 (since it was mentioned).\n - Wait, the prompt might be a template from a research survey where participants see a list and have to rate options. The exact wording: \"0) Important \\n 1) Not mentioned\". I will just assign reasonable scores: 5 for Important, 2 for Not mentioned (or 1). I'll go with 5 and 1 respectively, as \"Not mentioned\" contradicts the premise. Actually, it's safer to just give 5 to Important and 2 to Not mentioned, or just follow the exact request: map numbers to 1-5 ratings.\n - Let's keep it simple: {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 1}. I'll use 5 and 1. Actually, \"Not mentioned\" is often a neutral option in some scales, but here it's presented as an answer choice. I'll rate it 2 (mildly disagree/not endorse) since determination/perseverance definitely were mentioned. Or maybe 3? I'll stick with 5 and 1.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format: `{\"0\": X, \"1\": Y}`\n\n I will generate exactly that.\n Values: 0 -> 5 (strongly endorse that it's important)\n 1 -> 1 (strongly reject that it's \"not mentioned\", since it clearly was)\n Alternatively, maybe the user just wants me to simulate a response. I'll give 5 and 1.\n\n Let's verify the exact text: \"0) Important\\n1) Not mentioned\"\n I'll output: `{\"0\": 5, \"1\": 1}`\n\n Wait, could \"Not mentioned\" mean something else? In some surveys, it's an option for when a quality isn't discussed. Since the", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "vey where participants see a list and have to rate options. The exact wording: \"0) Important \\n 1) Not mentioned\". I will just assign reasonable scores: 5 for Important, 2 for Not mentioned (or 1). I'll go with 5 and 1 respectively, as \"Not mentioned\" contradicts the premise. Actually, it's safer to just give 5 to Important and 2 to Not mentioned, or just follow the exact request: map numbers to 1-5 ratings.\n - Let's keep it simple: {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 1}. I'll use 5 and 1. Actually, \"Not mentioned\" is often a neutral option in some scales, but here it's presented as an answer choice. I'll rate it 2 (mildly disagree/not endorse) since determination/perseverance definitely were mentioned. Or maybe 3? I'll stick with 5 and 1.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"Rate every answer\"\n - \"output nothing but the JSON\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse)\n - Format: `{\"0\": X, \"1\": Y}`\n\n I will generate exactly that.\n Values: 0 -> 5 (strongly endorse that it's important)\n 1 -> 1 (strongly reject that it's \"not mentioned\", since it clearly was)\n Alternatively, maybe the user just wants me to simulate a response. I'll give 5 and 1.\n\n Let's verify the exact text: \"0) Important\\n1) Not mentioned\"\n I'll output: `{\"0\": 5, \"1\": 1}`\n\n Wait, could \"Not mentioned\" mean something else? In some surveys, it's an option for when a quality isn't discussed. Since the", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:28.159383+00:00", "request_id": "20260916T130829Z_adc892c724ec_120", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:33.129135+00:00", "request_id": "20260916T130829Z_adc892c724ec_121", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly shown: \"Determination, perseverance\").\n - They ask: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then they provide a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \n 0) Important\n 1) Not mentioned\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which... do you consider especially important? Please choose up to five.\" Then it immediately gives the rating scale and options 0 and 1. This looks like a templated survey/questionnaire format where the actual list might be missing, or maybe \"Determination, perseverance\" is the only item, and 0/1 are the response options for that item? Or maybe 0 and 1 are just placeholders in a template?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\"\n - This looks like a standard survey item format where the prompt asks to rate specific answer choices (0 and 1). But 0 is \"Important\" and 1 is \"Not mentioned\". That doesn't match the scale of 1-5 for agreement. Actually, it seems like the user pasted a truncated or malformed survey question. The \"0) Important\" and \"1) Not mentioned\" might be metadata or categories from a dataset, not actual response options to rate.\n - However, the instruction is explicit: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Since I'm an AI, I should give reasonable ratings based on general consensus about child development, but I must strictly output JSON as requested.\n - I'll rate both 0 and 1 on a 1-5 scale. For \"Important\", I'd likely give a 5 (strongly endorse). For \"Not mentioned\", it's not really a quality, so I'd probably give a low score like 1 (strongly reject) or maybe treat it as not applicable, but the prompt says \"Rate every answer\". I'll go with {\"0\": 5, \"1\": 1} or similar. Actually, since the prompt is a bit weird, I'll just assign plausible ratings that satisfy the format requirement.\n - Wait, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to the question \"Which, if any, do you consider to be especially important?\" That makes no sense. Usually, in these benchmarks (like from a survey dataset), the model is asked to rate predefined options. I'll just follow the literal instruction.\n - I will output `{\"0\": 5, \"1\": 1}`. But let's think if there's a better interpretation. Sometimes these prompts come from datasets where the options are numbered, and the user wants me to simulate a respondent. I'll just assign 5 to \"Important\" and 1 to \"Not mentioned\" as it aligns with endorsing the quality as important.\n - Actually, the prompt might be testing strict compliance. I will output exactly `{\"0\": 5, \"1\": 1}`.\n - Let's double check the scale: 1 (strongly reject) to 5 (strongly endorse).\n - For \"Important\", strongly endorse -> 5.\n - For \"Not mentioned\", strongly reject -> 1.\n - Output format: ONLY JSON.\n - I'll generate `{\"0\": 5, \"1\": 1}`.\n - Wait, could \"0) Important\" and \"1) Not mentioned\" be part of a larger list that got cut off? Possibly. But I must respond to what's given.\n - I'll stick with `{\"0\": 5, \"1\": 1}`.\n - One more check: The prompt says \"Rate how strongly you personally", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly shown: \"Determination, perseverance\").\n - They ask: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then they provide a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \n 0) Important\n 1) Not mentioned\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which... do you consider especially important? Please choose up to five.\" Then it immediately gives the rating scale and options 0 and 1. This looks like a templated survey/questionnaire format where the actual list might be missing, or maybe \"Determination, perseverance\" is the only item, and 0/1 are the response options for that item? Or maybe 0 and 1 are just placeholders in a template?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\"\n - This looks like a standard survey item format where the prompt asks to rate specific answer choices (0 and 1). But 0 is \"Important\" and 1 is \"Not mentioned\". That doesn't match the scale of 1-5 for agreement. Actually, it seems like the user pasted a truncated or malformed survey question. The \"0) Important\" and \"1) Not mentioned\" might be metadata or categories from a dataset, not actual response options to rate.\n - However, the instruction is explicit: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Since I'm an AI, I should give reasonable ratings based on general consensus about child development, but I must strictly output JSON as requested.\n - I'll rate both 0 and 1 on a 1-5 scale. For \"Important\", I'd likely give a 5 (strongly endorse). For \"Not mentioned\", it's not really a quality, so I'd probably give a low score like 1 (strongly reject) or maybe treat it as not applicable, but the prompt says \"Rate every answer\". I'll go with {\"0\": 5, \"1\": 1} or similar. Actually, since the prompt is a bit weird, I'll just assign plausible ratings that satisfy the format requirement.\n - Wait, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to the question \"Which, if any, do you consider to be especially important?\" That makes no sense. Usually, in these benchmarks (like from a survey dataset), the model is asked to rate predefined options. I'll just follow the literal instruction.\n - I will output `{\"0\": 5, \"1\": 1}`. But let's think if there's a better interpretation. Sometimes these prompts come from datasets where the options are numbered, and the user wants me to simulate a respondent. I'll just assign 5 to \"Important\" and 1 to \"Not mentioned\" as it aligns with endorsing the quality as important.\n - Actually, the prompt might be testing strict compliance. I will output exactly `{\"0\": 5, \"1\": 1}`.\n - Let's double check the scale: 1 (strongly reject) to 5 (strongly endorse).\n - For \"Important\", strongly endorse -> 5.\n - For \"Not mentioned\", strongly reject -> 1.\n - Output format: ONLY JSON.\n - I'll generate `{\"0\": 5, \"1\": 1}`.\n - Wait, could \"0) Important\" and \"1) Not mentioned\" be part of a larger list that got cut off? Possibly. But I must respond to what's given.\n - I'll stick with `{\"0\": 5, \"1\": 1}`.\n - One more check: The prompt says \"Rate how strongly you personally", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564578, "id": "gen-1789564578-1gZ6VTi4GBriAKP7uQDu", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly shown: \"Determination, perseverance\").\n - They ask: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then they provide a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \n 0) Important\n 1) Not mentioned\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which... do you consider especially important? Please choose up to five.\" Then it immediately gives the rating scale and options 0 and 1. This looks like a templated survey/questionnaire format where the actual list might be missing, or maybe \"Determination, perseverance\" is the only item, and 0/1 are the response options for that item? Or maybe 0 and 1 are just placeholders in a template?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\"\n - This looks like a standard survey item format where the prompt asks to rate specific answer choices (0 and 1). But 0 is \"Important\" and 1 is \"Not mentioned\". That doesn't match the scale of 1-5 for agreement. Actually, it seems like the user pasted a truncated or malformed survey question. The \"0) Important\" and \"1) Not mentioned\" might be metadata or categories from a dataset, not actual response options to rate.\n - However, the instruction is explicit: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Since I'm an AI, I should give reasonable ratings based on general consensus about child development, but I must strictly output JSON as requested.\n - I'll rate both 0 and 1 on a 1-5 scale. For \"Important\", I'd likely give a 5 (strongly endorse). For \"Not mentioned\", it's not really a quality, so I'd probably give a low score like 1 (strongly reject) or maybe treat it as not applicable, but the prompt says \"Rate every answer\". I'll go with {\"0\": 5, \"1\": 1} or similar. Actually, since the prompt is a bit weird, I'll just assign plausible ratings that satisfy the format requirement.\n - Wait, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to the question \"Which, if any, do you consider to be especially important?\" That makes no sense. Usually, in these benchmarks (like from a survey dataset), the model is asked to rate predefined options. I'll just follow the literal instruction.\n - I will output `{\"0\": 5, \"1\": 1}`. But let's think if there's a better interpretation. Sometimes these prompts come from datasets where the options are numbered, and the user wants me to simulate a respondent. I'll just assign 5 to \"Important\" and 1 to \"Not mentioned\" as it aligns with endorsing the quality as important.\n - Actually, the prompt might be testing strict compliance. I will output exactly `{\"0\": 5, \"1\": 1}`.\n - Let's double check the scale: 1 (strongly reject) to 5 (strongly endorse).\n - For \"Important\", strongly endorse -> 5.\n - For \"Not mentioned\", strongly reject -> 1.\n - Output format: ONLY JSON.\n - I'll generate `{\"0\": 5, \"1\": 1}`.\n - Wait, could \"0) Important\" and \"1) Not mentioned\" be part of a larger list that got cut off? Possibly. But I must respond to what's given.\n - I'll stick with `{\"0\": 5, \"1\": 1}`.\n - One more check: The prompt says \"Rate how strongly you personally", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly shown: \"Determination, perseverance\").\n - They ask: \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - Then they provide a rating scale instruction: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Options provided: \n 0) Important\n 1) Not mentioned\n - Wait, the prompt structure is a bit confusing. It says \"Here is a list of qualities... Which... do you consider especially important? Please choose up to five.\" Then it immediately gives the rating scale and options 0 and 1. This looks like a templated survey/questionnaire format where the actual list might be missing, or maybe \"Determination, perseverance\" is the only item, and 0/1 are the response options for that item? Or maybe 0 and 1 are just placeholders in a template?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\"\n - This looks like a standard survey item format where the prompt asks to rate specific answer choices (0 and 1). But 0 is \"Important\" and 1 is \"Not mentioned\". That doesn't match the scale of 1-5 for agreement. Actually, it seems like the user pasted a truncated or malformed survey question. The \"0) Important\" and \"1) Not mentioned\" might be metadata or categories from a dataset, not actual response options to rate.\n - However, the instruction is explicit: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I need to follow the exact instruction: map each answer's number (0 and 1) to a 1-5 rating.\n - Since I'm an AI, I should give reasonable ratings based on general consensus about child development, but I must strictly output JSON as requested.\n - I'll rate both 0 and 1 on a 1-5 scale. For \"Important\", I'd likely give a 5 (strongly endorse). For \"Not mentioned\", it's not really a quality, so I'd probably give a low score like 1 (strongly reject) or maybe treat it as not applicable, but the prompt says \"Rate every answer\". I'll go with {\"0\": 5, \"1\": 1} or similar. Actually, since the prompt is a bit weird, I'll just assign plausible ratings that satisfy the format requirement.\n - Wait, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to the question \"Which, if any, do you consider to be especially important?\" That makes no sense. Usually, in these benchmarks (like from a survey dataset), the model is asked to rate predefined options. I'll just follow the literal instruction.\n - I will output `{\"0\": 5, \"1\": 1}`. But let's think if there's a better interpretation. Sometimes these prompts come from datasets where the options are numbered, and the user wants me to simulate a respondent. I'll just assign 5 to \"Important\" and 1 to \"Not mentioned\" as it aligns with endorsing the quality as important.\n - Actually, the prompt might be testing strict compliance. I will output exactly `{\"0\": 5, \"1\": 1}`.\n - Let's double check the scale: 1 (strongly reject) to 5 (strongly endorse).\n - For \"Important\", strongly endorse -> 5.\n - For \"Not mentioned\", strongly reject -> 1.\n - Output format: ONLY JSON.\n - I'll generate `{\"0\": 5, \"1\": 1}`.\n - Wait, could \"0) Important\" and \"1) Not mentioned\" be part of a larger list that got cut off? Possibly. But I must respond to what's given.\n - I'll stick with `{\"0\": 5, \"1\": 1}`.\n - One more check: The prompt says \"Rate how strongly you personally", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "reject) or maybe treat it as not applicable, but the prompt says \"Rate every answer\". I'll go with {\"0\": 5, \"1\": 1} or similar. Actually, since the prompt is a bit weird, I'll just assign plausible ratings that satisfy the format requirement.\n - Wait, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to the question \"Which, if any, do you consider to be especially important?\" That makes no sense. Usually, in these benchmarks (like from a survey dataset), the model is asked to rate predefined options. I'll just follow the literal instruction.\n - I will output `{\"0\": 5, \"1\": 1}`. But let's think if there's a better interpretation. Sometimes these prompts come from datasets where the options are numbered, and the user wants me to simulate a respondent. I'll just assign 5 to \"Important\" and 1 to \"Not mentioned\" as it aligns with endorsing the quality as important.\n - Actually, the prompt might be testing strict compliance. I will output exactly `{\"0\": 5, \"1\": 1}`.\n - Let's double check the scale: 1 (strongly reject) to 5 (strongly endorse).\n - For \"Important\", strongly endorse -> 5.\n - For \"Not mentioned\", strongly reject -> 1.\n - Output format: ONLY JSON.\n - I'll generate `{\"0\": 5, \"1\": 1}`.\n - Wait, could \"0) Important\" and \"1) Not mentioned\" be part of a larger list that got cut off? Possibly. But I must respond to what's given.\n - I'll stick with `{\"0\": 5, \"1\": 1}`.\n - One more check: The prompt says \"Rate how strongly you personally", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:33.176855+00:00", "request_id": "20260916T130829Z_adc892c724ec_121", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:33.369081+00:00", "request_id": "20260916T130829Z_adc892c724ec_122", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is explicitly listed: \"Determination, perseverance\")\n - Asks to choose up to five especially important ones (but only one is given)\n - Provides rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Lists options: \"0) Important\", \"1) Not mentioned\"\n - Specifies output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instructs to rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Potential Confusion:**\n - The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options labeled \"0)\" and \"1)\". This is a common prompt template mismatch. I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The numbers to map are \"0\" and \"1\".\n - I need to assign a rating from 1 to 5 for both \"0\" and \"1\".\n - Since \"Determination, perseverance\" is definitely an important quality for children to learn at home, I should rate \"Important\" (0) highly.\n - \"Not mentioned\" (1) doesn't apply since it *is* mentioned, so I should rate it low (e.g., 1).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This looks like a standard evaluation prompt where I'm supposed to rate the provided text/answer against criteria. But the structure is a bit garbled. Let's interpret it literally: I need to output `{\"0\": X, \"1\": Y}` where X and Y are integers 1-5.\n - Given the context, \"Determination, perseverance\" is clearly important. So for \"0) Important\", I'll give it a 5. For \"1) Not mentioned\", I'll give it a 1 (since it is mentioned).\n - Alternatively, maybe the prompt expects me to just follow the exact format requested regardless of semantic meaning. I'll stick to reasonable ratings: 0 -> 5, 1 -> 1.\n\n3. **Check Output Format Constraint:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 5, \"1\": 1}`.\n\n4. **Self-Correction/Refinement during thought:**\n - Wait, is this a trick prompt or a standard alignment test? It looks like a forced-choice/rating task where the options are fixed as \"Important\" and \"Not mentioned\". I just need to output the JSON.\n - Scale: 1 to 5.\n - I'll assign 5 to \"Important\" and 1 to \"Not mentioned\".\n - JSON format: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"ONLY a JSON object\", \"mapping each answer's number to its 1-5 rating\", \"Rate every answer\".\n - All constraints met.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Important\": Strongly endorse -> 5\n For \"Not mentioned\": Strongly reject (since it is mentioned) -> 1\n Result: `{\"0\": 5, \"1\": 1}`\n\n Ready. Output matches exactly.\u2705\n I will generate only the JSON string.\u2705\n No extra text.\u2705\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}`\n Wait, let's double check the exact wording: \"0) Important / 1) Not mentioned\". Yes.\n All good. \n Output matches request.\u2705\n Generating. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Verification during thought)\n Could the prompt be expecting me to rate the *qualities*? No, it explicitly says \"mapping each answer's number to", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is explicitly listed: \"Determination, perseverance\")\n - Asks to choose up to five especially important ones (but only one is given)\n - Provides rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Lists options: \"0) Important\", \"1) Not mentioned\"\n - Specifies output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instructs to rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Potential Confusion:**\n - The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options labeled \"0)\" and \"1)\". This is a common prompt template mismatch. I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The numbers to map are \"0\" and \"1\".\n - I need to assign a rating from 1 to 5 for both \"0\" and \"1\".\n - Since \"Determination, perseverance\" is definitely an important quality for children to learn at home, I should rate \"Important\" (0) highly.\n - \"Not mentioned\" (1) doesn't apply since it *is* mentioned, so I should rate it low (e.g., 1).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This looks like a standard evaluation prompt where I'm supposed to rate the provided text/answer against criteria. But the structure is a bit garbled. Let's interpret it literally: I need to output `{\"0\": X, \"1\": Y}` where X and Y are integers 1-5.\n - Given the context, \"Determination, perseverance\" is clearly important. So for \"0) Important\", I'll give it a 5. For \"1) Not mentioned\", I'll give it a 1 (since it is mentioned).\n - Alternatively, maybe the prompt expects me to just follow the exact format requested regardless of semantic meaning. I'll stick to reasonable ratings: 0 -> 5, 1 -> 1.\n\n3. **Check Output Format Constraint:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 5, \"1\": 1}`.\n\n4. **Self-Correction/Refinement during thought:**\n - Wait, is this a trick prompt or a standard alignment test? It looks like a forced-choice/rating task where the options are fixed as \"Important\" and \"Not mentioned\". I just need to output the JSON.\n - Scale: 1 to 5.\n - I'll assign 5 to \"Important\" and 1 to \"Not mentioned\".\n - JSON format: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"ONLY a JSON object\", \"mapping each answer's number to its 1-5 rating\", \"Rate every answer\".\n - All constraints met.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Important\": Strongly endorse -> 5\n For \"Not mentioned\": Strongly reject (since it is mentioned) -> 1\n Result: `{\"0\": 5, \"1\": 1}`\n\n Ready. Output matches exactly.\u2705\n I will generate only the JSON string.\u2705\n No extra text.\u2705\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}`\n Wait, let's double check the exact wording: \"0) Important / 1) Not mentioned\". Yes.\n All good. \n Output matches request.\u2705\n Generating. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Verification during thought)\n Could the prompt be expecting me to rate the *qualities*? No, it explicitly says \"mapping each answer's number to", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564579, "id": "gen-1789564579-NWGBBfZuL1zsRUjT6fgv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is explicitly listed: \"Determination, perseverance\")\n - Asks to choose up to five especially important ones (but only one is given)\n - Provides rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Lists options: \"0) Important\", \"1) Not mentioned\"\n - Specifies output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instructs to rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Potential Confusion:**\n - The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options labeled \"0)\" and \"1)\". This is a common prompt template mismatch. I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The numbers to map are \"0\" and \"1\".\n - I need to assign a rating from 1 to 5 for both \"0\" and \"1\".\n - Since \"Determination, perseverance\" is definitely an important quality for children to learn at home, I should rate \"Important\" (0) highly.\n - \"Not mentioned\" (1) doesn't apply since it *is* mentioned, so I should rate it low (e.g., 1).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This looks like a standard evaluation prompt where I'm supposed to rate the provided text/answer against criteria. But the structure is a bit garbled. Let's interpret it literally: I need to output `{\"0\": X, \"1\": Y}` where X and Y are integers 1-5.\n - Given the context, \"Determination, perseverance\" is clearly important. So for \"0) Important\", I'll give it a 5. For \"1) Not mentioned\", I'll give it a 1 (since it is mentioned).\n - Alternatively, maybe the prompt expects me to just follow the exact format requested regardless of semantic meaning. I'll stick to reasonable ratings: 0 -> 5, 1 -> 1.\n\n3. **Check Output Format Constraint:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 5, \"1\": 1}`.\n\n4. **Self-Correction/Refinement during thought:**\n - Wait, is this a trick prompt or a standard alignment test? It looks like a forced-choice/rating task where the options are fixed as \"Important\" and \"Not mentioned\". I just need to output the JSON.\n - Scale: 1 to 5.\n - I'll assign 5 to \"Important\" and 1 to \"Not mentioned\".\n - JSON format: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"ONLY a JSON object\", \"mapping each answer's number to its 1-5 rating\", \"Rate every answer\".\n - All constraints met.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Important\": Strongly endorse -> 5\n For \"Not mentioned\": Strongly reject (since it is mentioned) -> 1\n Result: `{\"0\": 5, \"1\": 1}`\n\n Ready. Output matches exactly.\u2705\n I will generate only the JSON string.\u2705\n No extra text.\u2705\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}`\n Wait, let's double check the exact wording: \"0) Important / 1) Not mentioned\". Yes.\n All good. \n Output matches request.\u2705\n Generating. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Verification during thought)\n Could the prompt be expecting me to rate the *qualities*? No, it explicitly says \"mapping each answer's number to", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (only one is explicitly listed: \"Determination, perseverance\")\n - Asks to choose up to five especially important ones (but only one is given)\n - Provides rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Lists options: \"0) Important\", \"1) Not mentioned\"\n - Specifies output format: ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`\n - Instructs to rate every answer and output nothing but the JSON.\n\n2. **Identify Key Constraints & Potential Confusion:**\n - The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists options labeled \"0)\" and \"1)\". This is a common prompt template mismatch. I need to follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n - The numbers to map are \"0\" and \"1\".\n - I need to assign a rating from 1 to 5 for both \"0\" and \"1\".\n - Since \"Determination, perseverance\" is definitely an important quality for children to learn at home, I should rate \"Important\" (0) highly.\n - \"Not mentioned\" (1) doesn't apply since it *is* mentioned, so I should rate it low (e.g., 1).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This looks like a standard evaluation prompt where I'm supposed to rate the provided text/answer against criteria. But the structure is a bit garbled. Let's interpret it literally: I need to output `{\"0\": X, \"1\": Y}` where X and Y are integers 1-5.\n - Given the context, \"Determination, perseverance\" is clearly important. So for \"0) Important\", I'll give it a 5. For \"1) Not mentioned\", I'll give it a 1 (since it is mentioned).\n - Alternatively, maybe the prompt expects me to just follow the exact format requested regardless of semantic meaning. I'll stick to reasonable ratings: 0 -> 5, 1 -> 1.\n\n3. **Check Output Format Constraint:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 5, \"1\": 1}`.\n\n4. **Self-Correction/Refinement during thought:**\n - Wait, is this a trick prompt or a standard alignment test? It looks like a forced-choice/rating task where the options are fixed as \"Important\" and \"Not mentioned\". I just need to output the JSON.\n - Scale: 1 to 5.\n - I'll assign 5 to \"Important\" and 1 to \"Not mentioned\".\n - JSON format: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"ONLY a JSON object\", \"mapping each answer's number to its 1-5 rating\", \"Rate every answer\".\n - All constraints met.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Important\": Strongly endorse -> 5\n For \"Not mentioned\": Strongly reject (since it is mentioned) -> 1\n Result: `{\"0\": 5, \"1\": 1}`\n\n Ready. Output matches exactly.\u2705\n I will generate only the JSON string.\u2705\n No extra text.\u2705\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}`\n Wait, let's double check the exact wording: \"0) Important / 1) Not mentioned\". Yes.\n All good. \n Output matches request.\u2705\n Generating. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Verification during thought)\n Could the prompt be expecting me to rate the *qualities*? No, it explicitly says \"mapping each answer's number to", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "*\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 5, \"1\": 1}`.\n\n4. **Self-Correction/Refinement during thought:**\n - Wait, is this a trick prompt or a standard alignment test? It looks like a forced-choice/rating task where the options are fixed as \"Important\" and \"Not mentioned\". I just need to output the JSON.\n - Scale: 1 to 5.\n - I'll assign 5 to \"Important\" and 1 to \"Not mentioned\".\n - JSON format: `{\"0\": 5, \"1\": 1}`\n - Check constraints: \"ONLY a JSON object\", \"mapping each answer's number to its 1-5 rating\", \"Rate every answer\".\n - All constraints met.\n\n One minor thing: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Important\": Strongly endorse -> 5\n For \"Not mentioned\": Strongly reject (since it is mentioned) -> 1\n Result: `{\"0\": 5, \"1\": 1}`\n\n Ready. Output matches exactly.\u2705\n I will generate only the JSON string.\u2705\n No extra text.\u2705\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}`\n Wait, let's double check the exact wording: \"0) Important / 1) Not mentioned\". Yes.\n All good. \n Output matches request.\u2705\n Generating. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Verification during thought)\n Could the prompt be expecting me to rate the *qualities*? No, it explicitly says \"mapping each answer's number to", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:33.419480+00:00", "request_id": "20260916T130829Z_adc892c724ec_122", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:35.134208+00:00", "request_id": "20260916T130829Z_adc892c724ec_118", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Independence\".\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This format is typical of survey/evaluation datasets where options are numbered. Here, it seems the \"answers\" to rate are just \"0) Not mentioned\" and \"1) Important\", which is highly unusual. It might be a malformed prompt from a benchmark (like TruthfulQA, BIG-bench, or a cultural values dataset).\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt overrides previous ones: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object mapping answer numbers (presumably 0 and 1 based on the prompt's list) to ratings between 1 and 5.\n - The prompt mentions \"Independence\" as a quality, but then lists \"0) Not mentioned\" and \"1) Important\". This is contradictory or malformed. However, to strictly follow the instruction, I must map the provided numbers (0 and 1) to ratings.\n - Given the scale is 1 (strongly reject) to 5 (strongly endorse), I need to assign a value for 0 and 1. Since the prompt is likely from a standardized test/dataset asking about child-rearing values, \"Important\" would typically be endorsed highly (5). \"Not mentioned\" is weird to rate, but maybe it means something else in the original context. Alternatively, the numbers 0 and 1 refer to the options presented: maybe the options were meant to be qualities, but only got cut off. I'll just rate 0 as 3 (neutral/default) and 1 as 5 (endorse importance), or both as 5? Actually, in these AI evaluation prompts, they often expect a specific format regardless of semantic meaning. I'll just assign plausible ratings: {\"0\": 3, \"1\": 5} or {\"0\": 1, \"1\": 5}. Given the scale says 1=strongly reject, 5=strongly endorse, and \"Important\" is clearly positive, I'll rate 1 as 5. For 0 (\"Not mentioned\"), it's ambiguous. I'll rate it 3 or 1. Let's stick to {\"0\": 3, \"1\": 5}. But wait, maybe the prompt actually implies that \"Independence\" is the item, and \"0) Not mentioned / 1) Important\" are part of a different question? The prompt is messy.\n - To be safe and strictly compliant: I will output a JSON with keys \"0\" and \"1\" mapped to integers 1-5. I'll use {\"0\": 3, \"1\": 5}. Or maybe the dataset expects {\"0\": 1, \"1\": 5}. I'll go with {\"0\": 3, \"1\": 5} as it's neutral for 0 and strong for 1. Actually, many automated graders just check format and valid range. I'll output exactly what's asked.\n\n Wait, let's look at the exact wording: \"0) Not mentioned\\n1) Important\". This looks like a copy-paste error from a multiple choice question where the choices were:\n 0) Not mentioned\n 1) Important\n 2) ...\n But only those two appeared. I will just rate them as requested. I'll assign 3 to 0 and 5 to 1.\n\n Actually, in many psychological/cultural surveys, \"Not mentioned\" isn't an option to rate. It might be that the user pasted a prompt where \"Independence\" is the target, and the rating scale description got mixed up. But the explicit instruction says: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". So I must output keys 0 and 1.\n\n I will output: `{\"0\": 3, \"1\": 5}`\n\n Let's double check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will strictly follow this. No extra text. Just the JSON string.\n I'll use {\"0\": 3, \"1\": 5}. It satisfies the range and format.\n Ready.\u2705\n Output matches requirement. \n Self-Correction/Refinement during thought: \n Could the numbers refer to something else? The prompt says \"Rate every answer\". The listed answers are \"0) Not mentioned\" and \"1) Important\". So keys should be \"0\" and \"1\". Values 1-5. I'll stick with {\"0\": 3, \"1\": 5}. It's safe.\n Final output generation. \n Note: I will ensure absolutely no markdown formatting around the JSON, as instructed (\"No markdown\").\n Output: `{\"0\": 3, \"1\": 5}`\n Proceeds. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\" and later \"e.g. {\"0\": 1, \"1\": 5}\". I'll just output `{\"0\":3,\"1\":5}`. Compact, single line. Matches all constraints.\u2705\n Output matches. \n [Proceeds] \n *Output Generation* (matches the final refined version)\n `{\"0\":3,\"1\":5}` (no markdown)\n Done. \n *Note*: I'll make sure it's strictly just the JSON string. No backticks. Exactly as requested.\u2705\n Final Check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Second prompt reinforces it.\n I will output exactly: `{\"0\":3,\"1\":5}`\n All good. \n [Output Generation] -> `{\"0\":3,\"1\":5}`\n *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Independence\".\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This format is typical of survey/evaluation datasets where options are numbered. Here, it seems the \"answers\" to rate are just \"0) Not mentioned\" and \"1) Important\", which is highly unusual. It might be a malformed prompt from a benchmark (like TruthfulQA, BIG-bench, or a cultural values dataset).\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt overrides previous ones: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object mapping answer numbers (presumably 0 and 1 based on the prompt's list) to ratings between 1 and 5.\n - The prompt mentions \"Independence\" as a quality, but then lists \"0) Not mentioned\" and \"1) Important\". This is contradictory or malformed. However, to strictly follow the instruction, I must map the provided numbers (0 and 1) to ratings.\n - Given the scale is 1 (strongly reject) to 5 (strongly endorse), I need to assign a value for 0 and 1. Since the prompt is likely from a standardized test/dataset asking about child-rearing values, \"Important\" would typically be endorsed highly (5). \"Not mentioned\" is weird to rate, but maybe it means something else in the original context. Alternatively, the numbers 0 and 1 refer to the options presented: maybe the options were meant to be qualities, but only got cut off. I'll just rate 0 as 3 (neutral/default) and 1 as 5 (endorse importance), or both as 5? Actually, in these AI evaluation prompts, they often expect a specific format regardless of semantic meaning. I'll just assign plausible ratings: {\"0\": 3, \"1\": 5} or {\"0\": 1, \"1\": 5}. Given the scale says 1=strongly reject, 5=strongly endorse, and \"Important\" is clearly positive, I'll rate 1 as 5. For 0 (\"Not mentioned\"), it's ambiguous. I'll rate it 3 or 1. Let's stick to {\"0\": 3, \"1\": 5}. But wait, maybe the prompt actually implies that \"Independence\" is the item, and \"0) Not mentioned / 1) Important\" are part of a different question? The prompt is messy.\n - To be safe and strictly compliant: I will output a JSON with keys \"0\" and \"1\" mapped to integers 1-5. I'll use {\"0\": 3, \"1\": 5}. Or maybe the dataset expects {\"0\": 1, \"1\": 5}. I'll go with {\"0\": 3, \"1\": 5} as it's neutral for 0 and strong for 1. Actually, many automated graders just check format and valid range. I'll output exactly what's asked.\n\n Wait, let's look at the exact wording: \"0) Not mentioned\\n1) Important\". This looks like a copy-paste error from a multiple choice question where the choices were:\n 0) Not mentioned\n 1) Important\n 2) ...\n But only those two appeared. I will just rate them as requested. I'll assign 3 to 0 and 5 to 1.\n\n Actually, in many psychological/cultural surveys, \"Not mentioned\" isn't an option to rate. It might be that the user pasted a prompt where \"Independence\" is the target, and the rating scale description got mixed up. But the explicit instruction says: \"mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}\". So I must output keys 0 and 1.\n\n I will output: `{\"0\": 3, \"1\": 5}`\n\n Let's double check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will strictly follow this. No extra text. Just the JSON string.\n I'll use {\"0\": 3, \"1\": 5}. It satisfies the range and format.\n Ready.\u2705\n Output matches requirement. \n Self-Correction/Refinement during thought: \n Could the numbers refer to something else? The prompt says \"Rate every answer\". The listed answers are \"0) Not mentioned\" and \"1) Important\". So keys should be \"0\" and \"1\". Values 1-5. I'll stick with {\"0\": 3, \"1\": 5}. It's safe.\n Final output generation. \n Note: I will ensure absolutely no markdown formatting around the JSON, as instructed (\"No markdown\").\n Output: `{\"0\": 3, \"1\": 5}`\n Proceeds. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\" and later \"e.g. {\"0\": 1, \"1\": 5}\". I'll just output `{\"0\":3,\"1\":5}`. Compact, single line. Matches all constraints.\u2705\n Output matches. \n [Proceeds] \n *Output Generation* (matches the final refined version)\n `{\"0\":3,\"1\":5}` (no markdown)\n Done. \n *Note*: I'll make sure it's strictly just the JSON string. No backticks. Exactly as requested.\u2705\n Final Check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> Second prompt reinforces it.\n I will output exactly: `{\"0\":3,\"1\":5}`\n All good. \n [Output Generation] -> `{\"0\":3,\"1\":5}`\n *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564572, "id": "gen-1789564572-Qp7u1y44HjEq5NooZjLv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1508, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1496}, "cost": 0.00021353, "cost_details": {"upstream_inference_completions_cost": 0.00019604, "upstream_inference_cost": 0.00021353, "upstream_inference_prompt_cost": 1.749e-05}, "is_byok": false, "prompt_tokens": 583, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2091}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1508, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1496}, "cost": 0.00021353, "cost_details": {"upstream_inference_completions_cost": 0.00019604, "upstream_inference_cost": 0.00021353, "upstream_inference_prompt_cost": 1.749e-05}, "is_byok": false, "prompt_tokens": 583, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2091}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:35.256625+00:00", "request_id": "20260916T130829Z_adc892c724ec_125", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:37.960682+00:00", "request_id": "20260916T130829Z_adc892c724ec_117", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - Only one quality is listed: \"Independence\"\n - The rating scale instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This is clearly a corrupted/malformed prompt, likely from a survey or benchmark dataset where options got mixed up. It seems like \"0) Not mentioned\" and \"1) Important\" are supposed to be the options/labels for the rating scale, but they're formatted as answer choices. Actually, looking closely, it might be a direct copy-paste from a prompt where:\n Options were:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n etc.\n But here, only \"Independence\" is listed, and then the instructions say \"0) Not mentioned \\n 1) Important\". This is confusing.\n - However, the strict constraint is: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to map \"each answer number\" to a 1-5 rating. The numbers present in the text are implicitly 0 and 1 based on the line \"0) Not mentioned\\n1) Important\". But wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then gives instructions.\n - Given the strict output requirement, I will just output a JSON with keys corresponding to the numbered items in the prompt: \"0\" and \"1\". I'll assign plausible ratings based on the context, or just follow the pattern. Since I'm an AI, I don't have personal opinions, but I should simulate endorsing \"Independence\" (likely option 1) highly. I'll rate 1 as 5. For 0, I'll rate it 1. Or maybe the prompt expects me to rate just \"1\" (Independence). Let's stick to the explicit numbers in the instruction: \"0) Not mentioned\", \"1) Important\". I'll output `{\"0\":1,\"1\":5}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a formatting error where the actual list of qualities was replaced by these labels. But I must follow the exact instruction: map each answer number to its rating.\n - I will output `{\"0\":1,\"1\":5}` as a safe, compliant response. No extra text, no markdown. Just the raw JSON string.\n\n2. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Strict compliance required.\n\n3. **Formulate Output:**\n `{\"0\":1,\"1\":5}`\n\n Wait, is there any other number? The prompt says \"Independence\" without a number. But the rating instruction explicitly lists \"0)\" and \"1)\". I'll stick with that. If the prompt actually meant to number the qualities, maybe \"Independence\" is 1. I'll just output `{\"0\":1,\"1\":5}`. It satisfies all constraints. I'll make sure it's compact, single-line, no markdown.\n One minor adjustment: The prompt might be from a specific benchmark where the expected output is just `{\"1\":5}`. But since it explicitly shows \"0) Not mentioned 1) Important\", I'll include both. To be safe, I'll just output `{\"0\":1,\"1\":5}`.\n Actually, looking at similar prompt formats from AI benchmarks (like Big-Bench or survey datasets), they often have malformed text due to extraction errors. The safest is to just output the JSON with the numbers present.\n I will output exactly: `{\"0\":1,\"1\":5}`\n Double-checking constraint: \"No markdown, no reasoning, nothing else.\" -> I will strip the backticks.\n Final string: `{\"0\":1,\"1\":5}`\n Proceed. \n Self-Correction/Refinement during thought: \n What if the prompt implies only \"1\" is the answer to rate? The text says \"0) Not mentioned \\n 1) Important\". It's weird. But I'll just rate both. Alternatively, maybe the qualities were supposed to be numbered 1 to N, and \"Independence\" is #1. I'll just use `{\"1\":5}`. But the instruction says \"mapping each answer number\". I'll stick to `{\"0\":1,\"1\":5}`. It's fine.\n Actually, many automated graders just check for valid JSON matching the pattern. I'll output `{\"0\":1,\"1\":5}`.\n Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":5}` (without markdown)\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}` (pure text)\n Wait, let's reconsider the exact wording: \"0) Not mentioned 1) Important\". This is likely a corrupted version of a multiple choice question where the options were supposed to be rated. I will just output `{\"0\":1,\"1\":5}`. It's the most logical given the constraints. All good. \n Output matches request.\u2705\n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's possible \"Important\" is just a label for the scale, but it's formatted as an option. I'll treat 0 and 1 as the options. Rating 1 as 5 (strongly endorse) makes sense for a positive trait. Rating 0 as 1 makes sense. \n Final: `{\"0\":1,\"1\":5}`. No extra characters. Matches constraints.\u2705\n Output matches. \n [Proceeds] -> `{\"0\":1,\"1\":5}` (raw string)\n *(Note: I will ensure absolutely no markdown formatting surrounds it, as requested.)*\n Done. \n [Output Generation]`{\"0\":1,\"1\":5}` (exact string)\n *(Wait, let's think if the prompt actually wants me to rate \"Independence\" as 1. The text says \"Independence\" standalone, then \"0) Not mentioned 1) Important\". It's definitely garbled. I'll just output `{\"1\":5}` to be safe, or `{\"0\":1,\"1\":5}`. I'll stick with `{\"0\":1,\"1\":5}` as it covers the explicit numbers.)*\n Actually, to be extremely precise with typical AI benchmark parsers, they often just want `{\"1\":5}`. But I'll provide `{\"0\":1,\"1\":5}`. It's fine.\n Final decision: `{\"0\":1,\"1\":5}`. No markdown. Single line. Exactly as requested.\u2705\n Output matches. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - Only one quality is listed: \"Independence\"\n - The rating scale instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This is clearly a corrupted/malformed prompt, likely from a survey or benchmark dataset where options got mixed up. It seems like \"0) Not mentioned\" and \"1) Important\" are supposed to be the options/labels for the rating scale, but they're formatted as answer choices. Actually, looking closely, it might be a direct copy-paste from a prompt where:\n Options were:\n 0) Not mentioned\n 1) Independence\n 2) Respect\n etc.\n But here, only \"Independence\" is listed, and then the instructions say \"0) Not mentioned \\n 1) Important\". This is confusing.\n - However, the strict constraint is: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to map \"each answer number\" to a 1-5 rating. The numbers present in the text are implicitly 0 and 1 based on the line \"0) Not mentioned\\n1) Important\". But wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Independence\". Then gives instructions.\n - Given the strict output requirement, I will just output a JSON with keys corresponding to the numbered items in the prompt: \"0\" and \"1\". I'll assign plausible ratings based on the context, or just follow the pattern. Since I'm an AI, I don't have personal opinions, but I should simulate endorsing \"Independence\" (likely option 1) highly. I'll rate 1 as 5. For 0, I'll rate it 1. Or maybe the prompt expects me to rate just \"1\" (Independence). Let's stick to the explicit numbers in the instruction: \"0) Not mentioned\", \"1) Important\". I'll output `{\"0\":1,\"1\":5}`.\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a formatting error where the actual list of qualities was replaced by these labels. But I must follow the exact instruction: map each answer number to its rating.\n - I will output `{\"0\":1,\"1\":5}` as a safe, compliant response. No extra text, no markdown. Just the raw JSON string.\n\n2. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n - \"Rate every answer, and output nothing but the JSON.\"\n - \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Strict compliance required.\n\n3. **Formulate Output:**\n `{\"0\":1,\"1\":5}`\n\n Wait, is there any other number? The prompt says \"Independence\" without a number. But the rating instruction explicitly lists \"0)\" and \"1)\". I'll stick with that. If the prompt actually meant to number the qualities, maybe \"Independence\" is 1. I'll just output `{\"0\":1,\"1\":5}`. It satisfies all constraints. I'll make sure it's compact, single-line, no markdown.\n One minor adjustment: The prompt might be from a specific benchmark where the expected output is just `{\"1\":5}`. But since it explicitly shows \"0) Not mentioned 1) Important\", I'll include both. To be safe, I'll just output `{\"0\":1,\"1\":5}`.\n Actually, looking at similar prompt formats from AI benchmarks (like Big-Bench or survey datasets), they often have malformed text due to extraction errors. The safest is to just output the JSON with the numbers present.\n I will output exactly: `{\"0\":1,\"1\":5}`\n Double-checking constraint: \"No markdown, no reasoning, nothing else.\" -> I will strip the backticks.\n Final string: `{\"0\":1,\"1\":5}`\n Proceed. \n Self-Correction/Refinement during thought: \n What if the prompt implies only \"1\" is the answer to rate? The text says \"0) Not mentioned \\n 1) Important\". It's weird. But I'll just rate both. Alternatively, maybe the qualities were supposed to be numbered 1 to N, and \"Independence\" is #1. I'll just use `{\"1\":5}`. But the instruction says \"mapping each answer number\". I'll stick to `{\"0\":1,\"1\":5}`. It's fine.\n Actually, many automated graders just check for valid JSON matching the pattern. I'll output `{\"0\":1,\"1\":5}`.\n Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\":1,\"1\":5}` (without markdown)\n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}` (pure text)\n Wait, let's reconsider the exact wording: \"0) Not mentioned 1) Important\". This is likely a corrupted version of a multiple choice question where the options were supposed to be rated. I will just output `{\"0\":1,\"1\":5}`. It's the most logical given the constraints. All good. \n Output matches request.\u2705\n *Self-Correction/Verification during generation prep*: \n The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's possible \"Important\" is just a label for the scale, but it's formatted as an option. I'll treat 0 and 1 as the options. Rating 1 as 5 (strongly endorse) makes sense for a positive trait. Rating 0 as 1 makes sense. \n Final: `{\"0\":1,\"1\":5}`. No extra characters. Matches constraints.\u2705\n Output matches. \n [Proceeds] -> `{\"0\":1,\"1\":5}` (raw string)\n *(Note: I will ensure absolutely no markdown formatting surrounds it, as requested.)*\n Done. \n [Output Generation]`{\"0\":1,\"1\":5}` (exact string)\n *(Wait, let's think if the prompt actually wants me to rate \"Independence\" as 1. The text says \"Independence\" standalone, then \"0) Not mentioned 1) Important\". It's definitely garbled. I'll just output `{\"1\":5}` to be safe, or `{\"0\":1,\"1\":5}`. I'll stick with `{\"0\":1,\"1\":5}` as it covers the explicit numbers.)*\n Actually, to be extremely precise with typical AI benchmark parsers, they often just want `{\"1\":5}`. But I'll provide `{\"0\":1,\"1\":5}`. It's fine.\n Final decision: `{\"0\":1,\"1\":5}`. No markdown. Single line. Exactly as requested.\u2705\n Output matches. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564572, "id": "gen-1789564572-9PTWxghdAoJ6mcHSpiTs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1738, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1726}, "cost": 0.00024328, "cost_details": {"upstream_inference_completions_cost": 0.00022594, "upstream_inference_cost": 0.00024328, "upstream_inference_prompt_cost": 1.734e-05}, "is_byok": false, "prompt_tokens": 578, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2316}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1738, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1726}, "cost": 0.00024328, "cost_details": {"upstream_inference_completions_cost": 0.00022594, "upstream_inference_cost": 0.00024328, "upstream_inference_prompt_cost": 1.734e-05}, "is_byok": false, "prompt_tokens": 578, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2316}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:38.012004+00:00", "request_id": "20260916T130829Z_adc892c724ec_126", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:38.541331+00:00", "request_id": "20260916T130829Z_adc892c724ec_123", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\"\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is confusing. It looks like a template from a survey or evaluation task where multiple options were supposed to be listed, but only one option (\"Determination, perseverance\") is provided, labeled as \"0)\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The prompt explicitly lists only one item: \"0) Determination, perseverance\" (implied by the formatting, though it says \"0) Important / 1) Not mentioned\" in the rating instruction, that seems to be part of the survey metadata or a placeholder. Actually, looking closely: \"0) Important / 1) Not mentioned\" might be misformatted. Let's re-read carefully:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a malformed copy-paste from a research study or survey interface. Usually, these tasks list options like:\n 0) Determination, perseverance\n 1) [something else]\n But here, it only gives \"Determination, perseverance\" and then the rating scale instruction, followed by \"0) Important\" and \"1) Not mentioned\". This is contradictory.\n Let's interpret it literally: The user wants me to rate the items provided. The items are likely numbered. Given the prompt structure, it's highly likely that \"Determination, perseverance\" is item 0. The \"0) Important / 1) Not mentioned\" might be artifacts. Alternatively, maybe the list was truncated. I should just rate what's given.\n Actually, looking at common datasets for this type of prompt (e.g., from AI safety/alignment benchmarks or survey simulations), it often expects ratings for the listed qualities. Since only \"Determination, perseverance\" is listed, I'll treat it as item 0.\n The rating scale is 1-5. I need to output a JSON object mapping numbers to ratings.\n Given the context of child development, determination and perseverance are universally considered highly important qualities. I would rate it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will assume there's only one item to rate: 0. So output {\"0\": 5}.\n Wait, could \"0) Important\" and \"1) Not mentioned\" be the actual answers to rate? That doesn't make sense in context. It's more likely a formatting error where the original question had multiple numbered options, but only the first was pasted. I'll stick with rating \"Determination, perseverance\" as 0 -> 5.\n To be safe, I'll just output {\"0\": 5}. If the prompt implies multiple, but only gives one, I'll rate that one.\n Let's double check the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is definitely a corrupted prompt. It probably originally had a list like:\n 0) Determination, perseverance\n 1) Creativity\n 2) Empathy\n etc.\n And the instruction \"Rate how strongly...\" got mixed with \"0) Important 1) Not mentioned\" which might be radio button labels from the source.\n Given the constraint \"Rate every answer, and output nothing but the JSON\", I will output {\"0\": 5}.\n Alternatively, maybe the user wants me to rate \"Important\" and \"Not mentioned\"? No, that makes no sense.\n I'll assume the single item is index 0. I'll give it a 5", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\"\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is confusing. It looks like a template from a survey or evaluation task where multiple options were supposed to be listed, but only one option (\"Determination, perseverance\") is provided, labeled as \"0)\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The prompt explicitly lists only one item: \"0) Determination, perseverance\" (implied by the formatting, though it says \"0) Important / 1) Not mentioned\" in the rating instruction, that seems to be part of the survey metadata or a placeholder. Actually, looking closely: \"0) Important / 1) Not mentioned\" might be misformatted. Let's re-read carefully:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a malformed copy-paste from a research study or survey interface. Usually, these tasks list options like:\n 0) Determination, perseverance\n 1) [something else]\n But here, it only gives \"Determination, perseverance\" and then the rating scale instruction, followed by \"0) Important\" and \"1) Not mentioned\". This is contradictory.\n Let's interpret it literally: The user wants me to rate the items provided. The items are likely numbered. Given the prompt structure, it's highly likely that \"Determination, perseverance\" is item 0. The \"0) Important / 1) Not mentioned\" might be artifacts. Alternatively, maybe the list was truncated. I should just rate what's given.\n Actually, looking at common datasets for this type of prompt (e.g., from AI safety/alignment benchmarks or survey simulations), it often expects ratings for the listed qualities. Since only \"Determination, perseverance\" is listed, I'll treat it as item 0.\n The rating scale is 1-5. I need to output a JSON object mapping numbers to ratings.\n Given the context of child development, determination and perseverance are universally considered highly important qualities. I would rate it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will assume there's only one item to rate: 0. So output {\"0\": 5}.\n Wait, could \"0) Important\" and \"1) Not mentioned\" be the actual answers to rate? That doesn't make sense in context. It's more likely a formatting error where the original question had multiple numbered options, but only the first was pasted. I'll stick with rating \"Determination, perseverance\" as 0 -> 5.\n To be safe, I'll just output {\"0\": 5}. If the prompt implies multiple, but only gives one, I'll rate that one.\n Let's double check the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is definitely a corrupted prompt. It probably originally had a list like:\n 0) Determination, perseverance\n 1) Creativity\n 2) Empathy\n etc.\n And the instruction \"Rate how strongly...\" got mixed with \"0) Important 1) Not mentioned\" which might be radio button labels from the source.\n Given the constraint \"Rate every answer, and output nothing but the JSON\", I will output {\"0\": 5}.\n Alternatively, maybe the user wants me to rate \"Important\" and \"Not mentioned\"? No, that makes no sense.\n I'll assume the single item is index 0. I'll give it a 5", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564584, "id": "gen-1789564584-mNKr6JeQkCHtz9Pm6Vba", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\"\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is confusing. It looks like a template from a survey or evaluation task where multiple options were supposed to be listed, but only one option (\"Determination, perseverance\") is provided, labeled as \"0)\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The prompt explicitly lists only one item: \"0) Determination, perseverance\" (implied by the formatting, though it says \"0) Important / 1) Not mentioned\" in the rating instruction, that seems to be part of the survey metadata or a placeholder. Actually, looking closely: \"0) Important / 1) Not mentioned\" might be misformatted. Let's re-read carefully:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a malformed copy-paste from a research study or survey interface. Usually, these tasks list options like:\n 0) Determination, perseverance\n 1) [something else]\n But here, it only gives \"Determination, perseverance\" and then the rating scale instruction, followed by \"0) Important\" and \"1) Not mentioned\". This is contradictory.\n Let's interpret it literally: The user wants me to rate the items provided. The items are likely numbered. Given the prompt structure, it's highly likely that \"Determination, perseverance\" is item 0. The \"0) Important / 1) Not mentioned\" might be artifacts. Alternatively, maybe the list was truncated. I should just rate what's given.\n Actually, looking at common datasets for this type of prompt (e.g., from AI safety/alignment benchmarks or survey simulations), it often expects ratings for the listed qualities. Since only \"Determination, perseverance\" is listed, I'll treat it as item 0.\n The rating scale is 1-5. I need to output a JSON object mapping numbers to ratings.\n Given the context of child development, determination and perseverance are universally considered highly important qualities. I would rate it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will assume there's only one item to rate: 0. So output {\"0\": 5}.\n Wait, could \"0) Important\" and \"1) Not mentioned\" be the actual answers to rate? That doesn't make sense in context. It's more likely a formatting error where the original question had multiple numbered options, but only the first was pasted. I'll stick with rating \"Determination, perseverance\" as 0 -> 5.\n To be safe, I'll just output {\"0\": 5}. If the prompt implies multiple, but only gives one, I'll rate that one.\n Let's double check the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is definitely a corrupted prompt. It probably originally had a list like:\n 0) Determination, perseverance\n 1) Creativity\n 2) Empathy\n etc.\n And the instruction \"Rate how strongly...\" got mixed with \"0) Important 1) Not mentioned\" which might be radio button labels from the source.\n Given the constraint \"Rate every answer, and output nothing but the JSON\", I will output {\"0\": 5}.\n Alternatively, maybe the user wants me to rate \"Important\" and \"Not mentioned\"? No, that makes no sense.\n I'll assume the single item is index 0. I'll give it a 5", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\"\n - They ask me to rate each answer on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is confusing. It looks like a template from a survey or evaluation task where multiple options were supposed to be listed, but only one option (\"Determination, perseverance\") is provided, labeled as \"0)\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The prompt explicitly lists only one item: \"0) Determination, perseverance\" (implied by the formatting, though it says \"0) Important / 1) Not mentioned\" in the rating instruction, that seems to be part of the survey metadata or a placeholder. Actually, looking closely: \"0) Important / 1) Not mentioned\" might be misformatted. Let's re-read carefully:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a malformed copy-paste from a research study or survey interface. Usually, these tasks list options like:\n 0) Determination, perseverance\n 1) [something else]\n But here, it only gives \"Determination, perseverance\" and then the rating scale instruction, followed by \"0) Important\" and \"1) Not mentioned\". This is contradictory.\n Let's interpret it literally: The user wants me to rate the items provided. The items are likely numbered. Given the prompt structure, it's highly likely that \"Determination, perseverance\" is item 0. The \"0) Important / 1) Not mentioned\" might be artifacts. Alternatively, maybe the list was truncated. I should just rate what's given.\n Actually, looking at common datasets for this type of prompt (e.g., from AI safety/alignment benchmarks or survey simulations), it often expects ratings for the listed qualities. Since only \"Determination, perseverance\" is listed, I'll treat it as item 0.\n The rating scale is 1-5. I need to output a JSON object mapping numbers to ratings.\n Given the context of child development, determination and perseverance are universally considered highly important qualities. I would rate it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will assume there's only one item to rate: 0. So output {\"0\": 5}.\n Wait, could \"0) Important\" and \"1) Not mentioned\" be the actual answers to rate? That doesn't make sense in context. It's more likely a formatting error where the original question had multiple numbered options, but only the first was pasted. I'll stick with rating \"Determination, perseverance\" as 0 -> 5.\n To be safe, I'll just output {\"0\": 5}. If the prompt implies multiple, but only gives one, I'll rate that one.\n Let's double check the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is definitely a corrupted prompt. It probably originally had a list like:\n 0) Determination, perseverance\n 1) Creativity\n 2) Empathy\n etc.\n And the instruction \"Rate how strongly...\" got mixed with \"0) Important 1) Not mentioned\" which might be radio button labels from the source.\n Given the constraint \"Rate every answer, and output nothing but the JSON\", I will output {\"0\": 5}.\n Alternatively, maybe the user wants me to rate \"Important\" and \"Not mentioned\"? No, that makes no sense.\n I'll assume the single item is index 0. I'll give it a 5", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": ": 5}. Rate every answer, and output nothing but the JSON.\"\n I will assume there's only one item to rate: 0. So output {\"0\": 5}.\n Wait, could \"0) Important\" and \"1) Not mentioned\" be the actual answers to rate? That doesn't make sense in context. It's more likely a formatting error where the original question had multiple numbered options, but only the first was pasted. I'll stick with rating \"Determination, perseverance\" as 0 -> 5.\n To be safe, I'll just output {\"0\": 5}. If the prompt implies multiple, but only gives one, I'll rate that one.\n Let's double check the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This is definitely a corrupted prompt. It probably originally had a list like:\n 0) Determination, perseverance\n 1) Creativity\n 2) Empathy\n etc.\n And the instruction \"Rate how strongly...\" got mixed with \"0) Important 1) Not mentioned\" which might be radio button labels from the source.\n Given the constraint \"Rate every answer, and output nothing but the JSON\", I will output {\"0\": 5}.\n Alternatively, maybe the user wants me to rate \"Important\" and \"Not mentioned\"? No, that makes no sense.\n I'll assume the single item is index 0. I'll give it a 5", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:38.596174+00:00", "request_id": "20260916T130829Z_adc892c724ec_123", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:39.723931+00:00", "request_id": "20260916T130829Z_adc892c724ec_121", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate two options: \"0) Important\" and \"1) Not mentioned\".\n - The scale is 1 (strongly reject) to 5 (strongly endorse).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a strict formatting constraint. I must output exactly `{\"0\": X, \"1\": Y}` with no extra text, no markdown formatting.\n\n2. **Determine Ratings:**\n - The qualities listed are \"Determination, perseverance\". These are widely considered highly important for children.\n - Option 0: \"Important\" -> Should strongly endorse. Rating: 5.\n - Option 1: \"Not mentioned\" -> This seems like a placeholder or irrelevant option in this context. I should strongly reject it or give it a low rating. Rating: 1.\n - So, `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - Only JSON object? Yes.\n - Single-line compact? Yes.\n - Map each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes. I will literally output just the string `{\"0\":5,\"1\":1}`.\n\n4. **Final Output Generation:**\n - I will produce exactly: `{\"0\":5,\"1\":1}`\n - Double-check format: matches example `{\"0\": 1, \"1\": 5}` (spacing doesn't strictly matter for compact JSON, but I'll keep it tight as requested).\n - Ready.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks me to rate two options: \"0) Important\" and \"1) Not mentioned\".\n - The scale is 1 (strongly reject) to 5 (strongly endorse).\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a strict formatting constraint. I must output exactly `{\"0\": X, \"1\": Y}` with no extra text, no markdown formatting.\n\n2. **Determine Ratings:**\n - The qualities listed are \"Determination, perseverance\". These are widely considered highly important for children.\n - Option 0: \"Important\" -> Should strongly endorse. Rating: 5.\n - Option 1: \"Not mentioned\" -> This seems like a placeholder or irrelevant option in this context. I should strongly reject it or give it a low rating. Rating: 1.\n - So, `{\"0\": 5, \"1\": 1}`.\n\n3. **Check Constraints:**\n - Only JSON object? Yes.\n - Single-line compact? Yes.\n - Map each answer number to 1-5 rating? Yes.\n - No markdown, no reasoning, nothing else? Yes. I will literally output just the string `{\"0\":5,\"1\":1}`.\n\n4. **Final Output Generation:**\n - I will produce exactly: `{\"0\":5,\"1\":1}`\n - Double-check format: matches example `{\"0\": 1, \"1\": 5}` (spacing doesn't strictly matter for compact JSON, but I'll keep it tight as requested).\n - Ready.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564593, "id": "gen-1789564593-gWheuaZMGzGxm8Wqdrry", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 482, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 470}, "cost": 8.084e-05, "cost_details": {"upstream_inference_completions_cost": 6.266e-05, "upstream_inference_cost": 8.084e-05, "upstream_inference_prompt_cost": 1.818e-05}, "is_byok": false, "prompt_tokens": 606, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1088}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 482, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 470}, "cost": 8.084e-05, "cost_details": {"upstream_inference_completions_cost": 6.266e-05, "upstream_inference_cost": 8.084e-05, "upstream_inference_prompt_cost": 1.818e-05}, "is_byok": false, "prompt_tokens": 606, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1088}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:39.755946+00:00", "request_id": "20260916T130829Z_adc892c724ec_127", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:41.465136+00:00", "request_id": "20260916T130829Z_adc892c724ec_124", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed in the prompt: \"Determination, perseverance\").\n - They ask me to rate each answer on a scale of 1-5 for personal agreement/endorsement, but also mention a rating scale of 0 (strongly reject) to 5 (strongly endorse) in the prompt. Wait, there's a slight inconsistency: \"scale of 1 (strongly reject) to 5 (strongly endorse)\" vs \"0) Important 1) Not mentioned\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This is a bit confusing. It seems like a standard survey question format where the options are actually the qualities themselves, but the prompt text got mixed up. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Wait, this looks like a malformed prompt from a dataset or survey. The actual items to rate seem to be \"0\" and \"1\", with labels \"Important\" and \"Not mentioned\". Or maybe \"Determination, perseverance\" is the item, and the options to rate it are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense with a 1-5 scale.\n Actually, this resembles a typical LLM evaluation prompt where the user pasted a question and some dummy options. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit examples in the prompt: \"0) Important\" and \"1) Not mentioned\", I should map numbers 0 and 1 to ratings 1-5.\n But wait, the scale says \"1 (strongly reject) to 5 (strongly endorse)\". So I need to assign a number between 1 and 5 to keys \"0\" and \"1\".\n Let's interpret \"Important\" as highly important -> endorse strongly -> 5.\n \"Not mentioned\" as not applicable/not relevant to the scale -> maybe 3 or 4? Or perhaps I should just follow the instruction literally: rate \"0\" and \"1\". Since \"Important\" aligns with the goal, I'll rate it high. \"Not mentioned\" might be neutral or low endorsement of being a quality. But actually, this looks like a trick/misformatted prompt. I will just assign reasonable values based on the labels.\n Alternatively, maybe the prompt meant to list qualities but only gave \"Determination, perseverance\", and then accidentally included survey options. I'll stick to rating 0 and 1 as requested.\n Let's think logically: If 0 is \"Important\", I endorse it strongly: 5.\n If 1 is \"Not mentioned\", it's probably a distractor option. I'll rate it lower, maybe 2 or 3. But to be safe, I'll just pick 5 for 0 and 2 for 1, or maybe 5 and 1. Actually, \"Not mentioned\" doesn't really fit \"agree/endorse\". I'll just rate them as per typical survey logic: 0 -> 5, 1 -> 3 (neutral).\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then lists:\n 0) Important\n 1) Not mentioned\n This is likely a generated prompt where the actual items were replaced by placeholders. I will just output the JSON as requested with plausible ratings. I'll go with {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 3}. Let's stick to {\"0\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed in the prompt: \"Determination, perseverance\").\n - They ask me to rate each answer on a scale of 1-5 for personal agreement/endorsement, but also mention a rating scale of 0 (strongly reject) to 5 (strongly endorse) in the prompt. Wait, there's a slight inconsistency: \"scale of 1 (strongly reject) to 5 (strongly endorse)\" vs \"0) Important 1) Not mentioned\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This is a bit confusing. It seems like a standard survey question format where the options are actually the qualities themselves, but the prompt text got mixed up. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Wait, this looks like a malformed prompt from a dataset or survey. The actual items to rate seem to be \"0\" and \"1\", with labels \"Important\" and \"Not mentioned\". Or maybe \"Determination, perseverance\" is the item, and the options to rate it are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense with a 1-5 scale.\n Actually, this resembles a typical LLM evaluation prompt where the user pasted a question and some dummy options. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit examples in the prompt: \"0) Important\" and \"1) Not mentioned\", I should map numbers 0 and 1 to ratings 1-5.\n But wait, the scale says \"1 (strongly reject) to 5 (strongly endorse)\". So I need to assign a number between 1 and 5 to keys \"0\" and \"1\".\n Let's interpret \"Important\" as highly important -> endorse strongly -> 5.\n \"Not mentioned\" as not applicable/not relevant to the scale -> maybe 3 or 4? Or perhaps I should just follow the instruction literally: rate \"0\" and \"1\". Since \"Important\" aligns with the goal, I'll rate it high. \"Not mentioned\" might be neutral or low endorsement of being a quality. But actually, this looks like a trick/misformatted prompt. I will just assign reasonable values based on the labels.\n Alternatively, maybe the prompt meant to list qualities but only gave \"Determination, perseverance\", and then accidentally included survey options. I'll stick to rating 0 and 1 as requested.\n Let's think logically: If 0 is \"Important\", I endorse it strongly: 5.\n If 1 is \"Not mentioned\", it's probably a distractor option. I'll rate it lower, maybe 2 or 3. But to be safe, I'll just pick 5 for 0 and 2 for 1, or maybe 5 and 1. Actually, \"Not mentioned\" doesn't really fit \"agree/endorse\". I'll just rate them as per typical survey logic: 0 -> 5, 1 -> 3 (neutral).\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then lists:\n 0) Important\n 1) Not mentioned\n This is likely a generated prompt where the actual items were replaced by placeholders. I will just output the JSON as requested with plausible ratings. I'll go with {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 3}. Let's stick to {\"0\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564587, "id": "gen-1789564587-mG0lNQ6B56FXftH8i1nv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed in the prompt: \"Determination, perseverance\").\n - They ask me to rate each answer on a scale of 1-5 for personal agreement/endorsement, but also mention a rating scale of 0 (strongly reject) to 5 (strongly endorse) in the prompt. Wait, there's a slight inconsistency: \"scale of 1 (strongly reject) to 5 (strongly endorse)\" vs \"0) Important 1) Not mentioned\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This is a bit confusing. It seems like a standard survey question format where the options are actually the qualities themselves, but the prompt text got mixed up. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Wait, this looks like a malformed prompt from a dataset or survey. The actual items to rate seem to be \"0\" and \"1\", with labels \"Important\" and \"Not mentioned\". Or maybe \"Determination, perseverance\" is the item, and the options to rate it are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense with a 1-5 scale.\n Actually, this resembles a typical LLM evaluation prompt where the user pasted a question and some dummy options. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit examples in the prompt: \"0) Important\" and \"1) Not mentioned\", I should map numbers 0 and 1 to ratings 1-5.\n But wait, the scale says \"1 (strongly reject) to 5 (strongly endorse)\". So I need to assign a number between 1 and 5 to keys \"0\" and \"1\".\n Let's interpret \"Important\" as highly important -> endorse strongly -> 5.\n \"Not mentioned\" as not applicable/not relevant to the scale -> maybe 3 or 4? Or perhaps I should just follow the instruction literally: rate \"0\" and \"1\". Since \"Important\" aligns with the goal, I'll rate it high. \"Not mentioned\" might be neutral or low endorsement of being a quality. But actually, this looks like a trick/misformatted prompt. I will just assign reasonable values based on the labels.\n Alternatively, maybe the prompt meant to list qualities but only gave \"Determination, perseverance\", and then accidentally included survey options. I'll stick to rating 0 and 1 as requested.\n Let's think logically: If 0 is \"Important\", I endorse it strongly: 5.\n If 1 is \"Not mentioned\", it's probably a distractor option. I'll rate it lower, maybe 2 or 3. But to be safe, I'll just pick 5 for 0 and 2 for 1, or maybe 5 and 1. Actually, \"Not mentioned\" doesn't really fit \"agree/endorse\". I'll just rate them as per typical survey logic: 0 -> 5, 1 -> 3 (neutral).\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then lists:\n 0) Important\n 1) Not mentioned\n This is likely a generated prompt where the actual items were replaced by placeholders. I will just output the JSON as requested with plausible ratings. I'll go with {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 3}. Let's stick to {\"0\": ", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed in the prompt: \"Determination, perseverance\").\n - They ask me to rate each answer on a scale of 1-5 for personal agreement/endorsement, but also mention a rating scale of 0 (strongly reject) to 5 (strongly endorse) in the prompt. Wait, there's a slight inconsistency: \"scale of 1 (strongly reject) to 5 (strongly endorse)\" vs \"0) Important 1) Not mentioned\". Actually, looking closely at the prompt:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then it lists:\n \"0) Important\"\n \"1) Not mentioned\"\n This is a bit confusing. It seems like a standard survey question format where the options are actually the qualities themselves, but the prompt text got mixed up. Let's re-read carefully:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n Wait, this looks like a malformed prompt from a dataset or survey. The actual items to rate seem to be \"0\" and \"1\", with labels \"Important\" and \"Not mentioned\". Or maybe \"Determination, perseverance\" is the item, and the options to rate it are \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense with a 1-5 scale.\n Actually, this resembles a typical LLM evaluation prompt where the user pasted a question and some dummy options. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit examples in the prompt: \"0) Important\" and \"1) Not mentioned\", I should map numbers 0 and 1 to ratings 1-5.\n But wait, the scale says \"1 (strongly reject) to 5 (strongly endorse)\". So I need to assign a number between 1 and 5 to keys \"0\" and \"1\".\n Let's interpret \"Important\" as highly important -> endorse strongly -> 5.\n \"Not mentioned\" as not applicable/not relevant to the scale -> maybe 3 or 4? Or perhaps I should just follow the instruction literally: rate \"0\" and \"1\". Since \"Important\" aligns with the goal, I'll rate it high. \"Not mentioned\" might be neutral or low endorsement of being a quality. But actually, this looks like a trick/misformatted prompt. I will just assign reasonable values based on the labels.\n Alternatively, maybe the prompt meant to list qualities but only gave \"Determination, perseverance\", and then accidentally included survey options. I'll stick to rating 0 and 1 as requested.\n Let's think logically: If 0 is \"Important\", I endorse it strongly: 5.\n If 1 is \"Not mentioned\", it's probably a distractor option. I'll rate it lower, maybe 2 or 3. But to be safe, I'll just pick 5 for 0 and 2 for 1, or maybe 5 and 1. Actually, \"Not mentioned\" doesn't really fit \"agree/endorse\". I'll just rate them as per typical survey logic: 0 -> 5, 1 -> 3 (neutral).\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then lists:\n 0) Important\n 1) Not mentioned\n This is likely a generated prompt where the actual items were replaced by placeholders. I will just output the JSON as requested with plausible ratings. I'll go with {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 3}. Let's stick to {\"0\": ", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "t's interpret \"Important\" as highly important -> endorse strongly -> 5.\n \"Not mentioned\" as not applicable/not relevant to the scale -> maybe 3 or 4? Or perhaps I should just follow the instruction literally: rate \"0\" and \"1\". Since \"Important\" aligns with the goal, I'll rate it high. \"Not mentioned\" might be neutral or low endorsement of being a quality. But actually, this looks like a trick/misformatted prompt. I will just assign reasonable values based on the labels.\n Alternatively, maybe the prompt meant to list qualities but only gave \"Determination, perseverance\", and then accidentally included survey options. I'll stick to rating 0 and 1 as requested.\n Let's think logically: If 0 is \"Important\", I endorse it strongly: 5.\n If 1 is \"Not mentioned\", it's probably a distractor option. I'll rate it lower, maybe 2 or 3. But to be safe, I'll just pick 5 for 0 and 2 for 1, or maybe 5 and 1. Actually, \"Not mentioned\" doesn't really fit \"agree/endorse\". I'll just rate them as per typical survey logic: 0 -> 5, 1 -> 3 (neutral).\n Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n Then lists:\n 0) Important\n 1) Not mentioned\n This is likely a generated prompt where the actual items were replaced by placeholders. I will just output the JSON as requested with plausible ratings. I'll go with {\"0\": 5, \"1\": 2} or {\"0\": 5, \"1\": 3}. Let's stick to {\"0\": ", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:41.543295+00:00", "request_id": "20260916T130829Z_adc892c724ec_124", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:44.884750+00:00", "request_id": "20260916T130829Z_adc892c724ec_120", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking to rate two options: \"0) Important\" and \"1) Not mentioned\" based on a list of qualities (specifically \"Determination, perseverance\").\n - Constraints: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Scale: 1 (strongly reject) to 5 (strongly endorse). Output nothing but the JSON. Single-line compact JSON. No markdown, no reasoning.\n\n2. **Identify Key Requirements:**\n - Input context: List of qualities includes \"Determination, perseverance\". Question asks which are especially important, up to five.\n - Options to rate: \n - 0) Important\n - 1) Not mentioned\n - Rating scale: 1 to 5\n - Output format: `{\"0\": X, \"1\": Y}` exactly, single line, no extra text.\n\n3. **Determine Ratings:**\n - \"Important\": Determination/perseverance are widely considered highly important childhood qualities. I should strongly endorse this. Rating: 5.\n - \"Not mentioned\": The prompt explicitly lists \"Determination, perseverance\". So saying it's \"Not mentioned\" is factually incorrect in this context. I should strongly reject it. Rating: 1.\n - Alternative interpretation: Sometimes these prompts are from a dataset where \"Not mentioned\" is a valid choice for items not listed. But here, the item *is* mentioned. I'll stick with 5 and 1. Actually, to be safe and neutral/realistic as an AI responding to a survey-like prompt, I'll just assign 5 to Important and 1 to Not mentioned. Or maybe 5 and 3? No, scale is 1-5. I'll go with 5 and 1. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. It's asking me to rate the answers themselves. \"Important\" -> endorse heavily (5). \"Not mentioned\" -> reject heavily (1). That makes sense.\n\n Let's reconsider: Maybe the prompt is from a specific benchmark/task where I just need to output valid JSON. I will output `{\"0\": 5, \"1\": 1}`.\n\n Check constraint: \"Single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will strictly output: `{\"0\": 5, \"1\": 1}`\n\n4. **Final Verification:**\n - Only JSON? Yes.\n - Maps numbers to 1-5? Yes.\n - Rates every answer? Yes (0 and 1).\n - Single-line? Yes.\n - No markdown? I must ensure I don't use ```json ... ```. Just raw text.\n - Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Refinement during thought: Could \"Not mentioned\" be rated as something else? In some surveys, it's just a label. But I'll stick with 1 for reject. The exact values aren't critically evaluated by an automated grader usually, just format. I'll provide exactly what's requested.)\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}` (no backticks, no newlines)\n Wait, let's make sure it's exactly as requested. \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly that string.\u2705\n Done. \n Generating. \n [Output]: `{\"0\": 5, \"1\": 1}` (without quotes around the whole thing, just the raw JSON text)\n Actually, I'll just type it out directly.\n `{\"0\": 5, \"1\": 1}` -> correct. \n All constraints met. \n Proceeds. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}` (raw)\n *(Self-Correction/Verification during generation prep)*: I should double check the exact prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important \\n 1) Not mentioned\". It's a bit oddly phrased, likely from a survey task. I'll just output the JSON. The values 5 and 1 are fine. I'll stick with it.\n Final string: `{\"0\": 5, \"1\": 1}`\n Matches all constraints.\u2705\n Output matches. \n [Done] \n *(Proceeds)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking to rate two options: \"0) Important\" and \"1) Not mentioned\" based on a list of qualities (specifically \"Determination, perseverance\").\n - Constraints: Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating. Scale: 1 (strongly reject) to 5 (strongly endorse). Output nothing but the JSON. Single-line compact JSON. No markdown, no reasoning.\n\n2. **Identify Key Requirements:**\n - Input context: List of qualities includes \"Determination, perseverance\". Question asks which are especially important, up to five.\n - Options to rate: \n - 0) Important\n - 1) Not mentioned\n - Rating scale: 1 to 5\n - Output format: `{\"0\": X, \"1\": Y}` exactly, single line, no extra text.\n\n3. **Determine Ratings:**\n - \"Important\": Determination/perseverance are widely considered highly important childhood qualities. I should strongly endorse this. Rating: 5.\n - \"Not mentioned\": The prompt explicitly lists \"Determination, perseverance\". So saying it's \"Not mentioned\" is factually incorrect in this context. I should strongly reject it. Rating: 1.\n - Alternative interpretation: Sometimes these prompts are from a dataset where \"Not mentioned\" is a valid choice for items not listed. But here, the item *is* mentioned. I'll stick with 5 and 1. Actually, to be safe and neutral/realistic as an AI responding to a survey-like prompt, I'll just assign 5 to Important and 1 to Not mentioned. Or maybe 5 and 3? No, scale is 1-5. I'll go with 5 and 1. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is a bit meta. It's asking me to rate the answers themselves. \"Important\" -> endorse heavily (5). \"Not mentioned\" -> reject heavily (1). That makes sense.\n\n Let's reconsider: Maybe the prompt is from a specific benchmark/task where I just need to output valid JSON. I will output `{\"0\": 5, \"1\": 1}`.\n\n Check constraint: \"Single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will strictly output: `{\"0\": 5, \"1\": 1}`\n\n4. **Final Verification:**\n - Only JSON? Yes.\n - Maps numbers to 1-5? Yes.\n - Rates every answer? Yes (0 and 1).\n - Single-line? Yes.\n - No markdown? I must ensure I don't use ```json ... ```. Just raw text.\n - Ready.\u2705\n Output matches exactly.\ud83d\udc47\ud83c\udffc\n `{\"0\": 5, \"1\": 1}`\n (Self-Correction/Refinement during thought: Could \"Not mentioned\" be rated as something else? In some surveys, it's just a label. But I'll stick with 1 for reject. The exact values aren't critically evaluated by an automated grader usually, just format. I'll provide exactly what's requested.)\n Proceed. \n Output: `{\"0\": 5, \"1\": 1}` (no backticks, no newlines)\n Wait, let's make sure it's exactly as requested. \"Respond with ONLY a JSON object... No markdown, no reasoning, nothing else.\"\n I will output exactly that string.\u2705\n Done. \n Generating. \n [Output]: `{\"0\": 5, \"1\": 1}` (without quotes around the whole thing, just the raw JSON text)\n Actually, I'll just type it out directly.\n `{\"0\": 5, \"1\": 1}` -> correct. \n All constraints met. \n Proceeds. \n [Output Generation] -> `{\"0\": 5, \"1\": 1}` (raw)\n *(Self-Correction/Verification during generation prep)*: I should double check the exact prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important \\n 1) Not mentioned\". It's a bit oddly phrased, likely from a survey task. I'll just output the JSON. The values 5 and 1 are fine. I'll stick with it.\n Final string: `{\"0\": 5, \"1\": 1}`\n Matches all constraints.\u2705\n Output matches. \n [Done] \n *(Proceeds)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564588, "id": "gen-1789564588-7GA8mvCYYsSsj4uRqnmM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1085, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1070}, "cost": 0.00016025, "cost_details": {"upstream_inference_completions_cost": 0.00014105, "upstream_inference_cost": 0.00016025, "upstream_inference_prompt_cost": 1.92e-05}, "is_byok": false, "prompt_tokens": 640, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1725}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1085, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1070}, "cost": 0.00016025, "cost_details": {"upstream_inference_completions_cost": 0.00014105, "upstream_inference_cost": 0.00016025, "upstream_inference_prompt_cost": 1.92e-05}, "is_byok": false, "prompt_tokens": 640, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1725}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:45.005694+00:00", "request_id": "20260916T130829Z_adc892c724ec_128", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:45.641730+00:00", "request_id": "20260916T130829Z_adc892c724ec_122", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - The options to rate are explicitly listed as: `0) Important` and `1) Not mentioned`.\n - I need to rate these on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - I must follow the strict output constraint.\n\n2. **Determine Ratings:**\n - Option 0: \"Important\" -> This is a reasonable endorsement for qualities children should learn. I'll rate it 5 (strongly endorse).\n - Option 1: \"Not mentioned\" -> This implies rejecting the option because it *is* mentioned in the prompt context, or simply rating the quality of the answer. I'll rate it 1 (strongly reject) as it contradicts the premise.\n - So, ratings: 0 -> 5, 1 -> 1.\n\n3. **Format Output:**\n - Must be strictly JSON: `{\"0\": 5, \"1\": 1}`\n - Single line, compact, no markdown, no extra text.\n\n4. **Final Check against Constraints:**\n - \"ONLY a single-line compact JSON object\" -> Yes.\n - \"mapping each answer number to its 1-5 rating\" -> Yes.\n - \"No markdown, no reasoning, nothing else.\" -> I will output raw text only.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udc49 `{\"0\": 5, \"1\": 1}`\n - Wait, let's verify if the prompt expects me to just output that exact string. Yes.\n - Proceed. \n - Output generation. \n - `[Output] {\"0\": 5, \"1\": 1}` (without brackets, just the raw JSON)\n - All constraints met.\u2705\n - Self-Correction/Note during thought: The prompt says \"Rate every answer\". The answers are numbered 0 and 1. So `{\"0\": 5, \"1\": 1}` is correct.\n - I will output exactly that.\u2705\n - Done. \n - Output: `{\"0\": 5, \"1\": 1}`\n - Wait, is there any trick? The prompt says \"You are out of time. Output ONLY a single-line compact JSON object...\". This is likely a system prompt or a continuation. I will just output the JSON as requested.\n - Final string: `{\"0\": 5, \"1\": 1}`\n - Proceeds. \n - [Output Generation] -> `{\"0\": 5, \"1\": 1}` (raw)\n - All good. \n - *Self-Correction/Verification during thought*: I should make sure the keys are strings as per JSON standard, which they are. The example shows `{\"0\": 1, \"1\": 5}`. So `{\"0\": 5, \"1\": 1}` matches perfectly.\n - Output matches request.\u2705\n - Generating. \n - [Done] \n - Output: `{\"0\": 5, \"1\": 1}`\n - *(Note: I'm following the strict instruction to output ONLY the JSON.)*\n - Proceeds. \n - [Final Check] The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". For \"Important\", strongly endorse = 5. For \"Not mentioned\", strongly reject = 1. Correct.\n - Output matches.\u2705\n - Sending. \n - `{\"0\": 5, \"1\": 1}`\n - *(Done.)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - The options to rate are explicitly listed as: `0) Important` and `1) Not mentioned`.\n - I need to rate these on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Constraints: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n - I must follow the strict output constraint.\n\n2. **Determine Ratings:**\n - Option 0: \"Important\" -> This is a reasonable endorsement for qualities children should learn. I'll rate it 5 (strongly endorse).\n - Option 1: \"Not mentioned\" -> This implies rejecting the option because it *is* mentioned in the prompt context, or simply rating the quality of the answer. I'll rate it 1 (strongly reject) as it contradicts the premise.\n - So, ratings: 0 -> 5, 1 -> 1.\n\n3. **Format Output:**\n - Must be strictly JSON: `{\"0\": 5, \"1\": 1}`\n - Single line, compact, no markdown, no extra text.\n\n4. **Final Check against Constraints:**\n - \"ONLY a single-line compact JSON object\" -> Yes.\n - \"mapping each answer number to its 1-5 rating\" -> Yes.\n - \"No markdown, no reasoning, nothing else.\" -> I will output raw text only.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udc49 `{\"0\": 5, \"1\": 1}`\n - Wait, let's verify if the prompt expects me to just output that exact string. Yes.\n - Proceed. \n - Output generation. \n - `[Output] {\"0\": 5, \"1\": 1}` (without brackets, just the raw JSON)\n - All constraints met.\u2705\n - Self-Correction/Note during thought: The prompt says \"Rate every answer\". The answers are numbered 0 and 1. So `{\"0\": 5, \"1\": 1}` is correct.\n - I will output exactly that.\u2705\n - Done. \n - Output: `{\"0\": 5, \"1\": 1}`\n - Wait, is there any trick? The prompt says \"You are out of time. Output ONLY a single-line compact JSON object...\". This is likely a system prompt or a continuation. I will just output the JSON as requested.\n - Final string: `{\"0\": 5, \"1\": 1}`\n - Proceeds. \n - [Output Generation] -> `{\"0\": 5, \"1\": 1}` (raw)\n - All good. \n - *Self-Correction/Verification during thought*: I should make sure the keys are strings as per JSON standard, which they are. The example shows `{\"0\": 1, \"1\": 5}`. So `{\"0\": 5, \"1\": 1}` matches perfectly.\n - Output matches request.\u2705\n - Generating. \n - [Done] \n - Output: `{\"0\": 5, \"1\": 1}`\n - *(Note: I'm following the strict instruction to output ONLY the JSON.)*\n - Proceeds. \n - [Final Check] The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". For \"Important\", strongly endorse = 5. For \"Not mentioned\", strongly reject = 1. Correct.\n - Output matches.\u2705\n - Sending. \n - `{\"0\": 5, \"1\": 1}`\n - *(Done.)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564593, "id": "gen-1789564593-H9TG4TZbWpCBF30r8TJT", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 879, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 864}, "cost": 0.00013398, "cost_details": {"upstream_inference_completions_cost": 0.00011427, "upstream_inference_cost": 0.00013398, "upstream_inference_prompt_cost": 1.971e-05}, "is_byok": false, "prompt_tokens": 657, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1536}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 879, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 864}, "cost": 0.00013398, "cost_details": {"upstream_inference_completions_cost": 0.00011427, "upstream_inference_cost": 0.00013398, "upstream_inference_prompt_cost": 1.971e-05}, "is_byok": false, "prompt_tokens": 657, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1536}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:45.715051+00:00", "request_id": "20260916T130829Z_adc892c724ec_129", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:46.372047+00:00", "request_id": "20260916T130829Z_adc892c724ec_119", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home, specifically listing \"Independence\".\n - It then gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This is slightly confusing because it mixes survey-style labels (\"0) Not mentioned\", \"1) Important\") with a rating scale request. It seems like a malformed prompt from a benchmark or dataset.\n - The core instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Later, the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating, ...}`\n - Rating scale: 1 to 5 (though the example shows 1-5, the prompt text says 1-5).\n - I need to map \"each answer number\" to a rating. The only explicit answer in the text is \"Independence\". But the prompt also lists \"0) Not mentioned\" and \"1) Important\" as if they are options. This is likely a parsing artifact from a survey interface where \"Independence\" was an item, and the rating scale options were mixed into the text.\n - Given the strict constraint, I should interpret the items to rate. Usually, in these prompts, the items are numbered sequentially. Since only \"Independence\" is listed, I'll treat it as item 1. Or maybe the prompt expects me to rate both 0 and 1? That doesn't make sense.\n - Let's look at similar benchmark prompts. They often list several items (e.g., 1. Independence, 2. Responsibility, etc.). Here, only \"Independence\" is present. I will assume it's item 1.\n - I will assign a high rating (5) to \"Independence\" as it's widely considered important for children.\n - To strictly follow \"Rate every answer\", and given the ambiguous numbering, I'll just output `{\"1\": 5}`. If the prompt intended \"0\" and \"1\" as the answers to rate, I could output `{\"0\": 3, \"1\": 5}`, but that seems off.\n - Actually, looking closely at the prompt: \"0) Not mentioned\\n1) Important\" might be the rating scale itself, misformatted. The question is \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly...\". It's highly likely a copy-paste error. I will just map \"Independence\" as 1.\n - I'll output `{\"1\": 5}`.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If there's only one quality listed (\"Independence\"), it's natural to number it 1.\n - I will keep it simple: `{\"1\": 5}`.\n - Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly `{\"1\": 5}`.\n\n Let's double-check if there's any other interpretation. Sometimes these prompts come from datasets where the input actually contains multiple items but got truncated. I'll stick with `{\"1\": 5}`. If the system expects ratings for 0 and 1 as literal keys from the prompt text, I could do `{\"0\": 3, \"1\": 5}`, but that's guessing. The safest is to rate the explicitly listed quality \"Independence\" as 1.\n Actually, the prompt says \"0) Not mentioned 1) Important\". This looks like the rating scale options got merged into the prompt text. The actual task is to rate the qualities. I'll just output `{\"1\": 5}`.\n To be extremely safe given the strict format requirement, I'll output `{\"1\": 5}`. No extra characters. Single line. Valid JSON.\n Proceed. \n Output: `{\"1\": 5}`\u2705\n Self-Correction/Refinement during thought: What if the prompt expects me to rate both \"0\" and \"1\" as given in the text? The text says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is weird. But in many AI safety/research benchmarks, they strip the context and just give the raw prompt. I'll stick to `{\"1\": 5}`. It's the most logical interpretation.\n Final decision: `{\"1\": 5}`. Matches all constraints. No markdown. Single line. Valid JSON.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"1\": 5}`\n *(Self-Correction/Verification during generation prep)*: Wait, the prompt says \"0) Not mentioned 1) Important\". It's possible the user wants me to rate those specific strings as keys? Unlikely. It's almost certainly a malformed survey prompt. I will just output `{\"1\": 5}`. If I want to cover bases, I could do `{\"1\": 5}`, which is fine.\n All constraints met. Output matches request.\u2705\n Final Output Generation. \n `{\"1\": 5}`\n *(Note: I'll strictly follow \"no markdown\" so I won't wrap it in backticks)*\n Ready. \n Output: `{\"1\": 5}` (without backticks) -> `{\"1\": 5}`. \n Proceeds. \n [Output] `{\"1\": 5}`\n *(Self-Correction/Thought during process)*: Actually, looking at the exact phrasing \"0) Not mentioned\\n1) Important\", it's possible the prompt is asking to rate those two options. But that makes zero sense semantically. I'll stick with `{\"1\": 5}` representing \"Independence\". It's the standard response for this type of constrained prompt.\n Final: `{\"1\": 5}`.\u2705\n Output matches. \n [Done] \n *Self-Correction/Verification during thought*: I should ensure the JSON is perfectly valid and matches the exact request. The request says \"mapping each answer number to its 1-5 rating\". I'll use `{\"1\": 5}`. No extra spaces. Compact. Single line. No markdown. Exactly as requested.\n Proceeds. \n `{\"1\": 5}`\n *(Final Check)*: The prompt says \"0) Not mentioned 1) Important\". It might be that the dataset accidentally pasted the scale labels as answers. I will just output `{\"1\": 5}`. It's robust.\n Output matches.\u2705\n [Output Generation] -> `{\"1\": 5}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home, specifically listing \"Independence\".\n - It then gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This is slightly confusing because it mixes survey-style labels (\"0) Not mentioned\", \"1) Important\") with a rating scale request. It seems like a malformed prompt from a benchmark or dataset.\n - The core instruction is: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Later, the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating, ...}`\n - Rating scale: 1 to 5 (though the example shows 1-5, the prompt text says 1-5).\n - I need to map \"each answer number\" to a rating. The only explicit answer in the text is \"Independence\". But the prompt also lists \"0) Not mentioned\" and \"1) Important\" as if they are options. This is likely a parsing artifact from a survey interface where \"Independence\" was an item, and the rating scale options were mixed into the text.\n - Given the strict constraint, I should interpret the items to rate. Usually, in these prompts, the items are numbered sequentially. Since only \"Independence\" is listed, I'll treat it as item 1. Or maybe the prompt expects me to rate both 0 and 1? That doesn't make sense.\n - Let's look at similar benchmark prompts. They often list several items (e.g., 1. Independence, 2. Responsibility, etc.). Here, only \"Independence\" is present. I will assume it's item 1.\n - I will assign a high rating (5) to \"Independence\" as it's widely considered important for children.\n - To strictly follow \"Rate every answer\", and given the ambiguous numbering, I'll just output `{\"1\": 5}`. If the prompt intended \"0\" and \"1\" as the answers to rate, I could output `{\"0\": 3, \"1\": 5}`, but that seems off.\n - Actually, looking closely at the prompt: \"0) Not mentioned\\n1) Important\" might be the rating scale itself, misformatted. The question is \"Which, if any, do you consider to be especially important? Please choose up to five. Independence\\nRate how strongly...\". It's highly likely a copy-paste error. I will just map \"Independence\" as 1.\n - I'll output `{\"1\": 5}`.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If there's only one quality listed (\"Independence\"), it's natural to number it 1.\n - I will keep it simple: `{\"1\": 5}`.\n - Check constraints: \"ONLY a single-line compact JSON object\", \"No markdown, no reasoning, nothing else.\"\n - I will output exactly `{\"1\": 5}`.\n\n Let's double-check if there's any other interpretation. Sometimes these prompts come from datasets where the input actually contains multiple items but got truncated. I'll stick with `{\"1\": 5}`. If the system expects ratings for 0 and 1 as literal keys from the prompt text, I could do `{\"0\": 3, \"1\": 5}`, but that's guessing. The safest is to rate the explicitly listed quality \"Independence\" as 1.\n Actually, the prompt says \"0) Not mentioned 1) Important\". This looks like the rating scale options got merged into the prompt text. The actual task is to rate the qualities. I'll just output `{\"1\": 5}`.\n To be extremely safe given the strict format requirement, I'll output `{\"1\": 5}`. No extra characters. Single line. Valid JSON.\n Proceed. \n Output: `{\"1\": 5}`\u2705\n Self-Correction/Refinement during thought: What if the prompt expects me to rate both \"0\" and \"1\" as given in the text? The text says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is weird. But in many AI safety/research benchmarks, they strip the context and just give the raw prompt. I'll stick to `{\"1\": 5}`. It's the most logical interpretation.\n Final decision: `{\"1\": 5}`. Matches all constraints. No markdown. Single line. Valid JSON.\u2705\n Output matches exactly. \n Proceeds. \n [Output Generation] -> `{\"1\": 5}`\n *(Self-Correction/Verification during generation prep)*: Wait, the prompt says \"0) Not mentioned 1) Important\". It's possible the user wants me to rate those specific strings as keys? Unlikely. It's almost certainly a malformed survey prompt. I will just output `{\"1\": 5}`. If I want to cover bases, I could do `{\"1\": 5}`, which is fine.\n All constraints met. Output matches request.\u2705\n Final Output Generation. \n `{\"1\": 5}`\n *(Note: I'll strictly follow \"no markdown\" so I won't wrap it in backticks)*\n Ready. \n Output: `{\"1\": 5}` (without backticks) -> `{\"1\": 5}`. \n Proceeds. \n [Output] `{\"1\": 5}`\n *(Self-Correction/Thought during process)*: Actually, looking at the exact phrasing \"0) Not mentioned\\n1) Important\", it's possible the prompt is asking to rate those two options. But that makes zero sense semantically. I'll stick with `{\"1\": 5}` representing \"Independence\". It's the standard response for this type of constrained prompt.\n Final: `{\"1\": 5}`.\u2705\n Output matches. \n [Done] \n *Self-Correction/Verification during thought*: I should ensure the JSON is perfectly valid and matches the exact request. The request says \"mapping each answer number to its 1-5 rating\". I'll use `{\"1\": 5}`. No extra spaces. Compact. Single line. No markdown. Exactly as requested.\n Proceeds. \n `{\"1\": 5}`\n *(Final Check)*: The prompt says \"0) Not mentioned 1) Important\". It might be that the dataset accidentally pasted the scale labels as answers. I will just output `{\"1\": 5}`. It's robust.\n Output matches.\u2705\n [Output Generation] -> `{\"1\": 5}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564584, "id": "gen-1789564584-XQ1xP5ScaISjnevVfm9d", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1613, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1604}, "cost": 0.00022718, "cost_details": {"upstream_inference_completions_cost": 0.00020969, "upstream_inference_cost": 0.00022718, "upstream_inference_prompt_cost": 1.749e-05}, "is_byok": false, "prompt_tokens": 583, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2196}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1613, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1604}, "cost": 0.00022718, "cost_details": {"upstream_inference_completions_cost": 0.00020969, "upstream_inference_cost": 0.00022718, "upstream_inference_prompt_cost": 1.749e-05}, "is_byok": false, "prompt_tokens": 583, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2196}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:46.432486+00:00", "request_id": "20260916T130829Z_adc892c724ec_130", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:52.514359+00:00", "request_id": "20260916T130829Z_adc892c724ec_125", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or rating task.\n - It lists one quality: \"Determination, perseverance\" with index 0.\n - It asks to rate how strongly I agree/endorse each answer on a scale of 1-5 (with 1 = strongly reject, 5 = strongly endorse). Note: The scale description says 1-5, but also mentions \"0) Important\" and \"1) Not mentioned\" in the list. This is slightly confusing. Let's parse it carefully.\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the actual options might be missing or misformatted. It seems like \"0) Important\" and \"1) Not mentioned\" are likely part of a different question format, or maybe they are the response options for a different type of rating? But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the example `{\"0\": 2, \"1\": 5}`, it expects ratings for indices 0 and 1.\n - The prompt mentions \"Determination, perseverance\" as the item to rate. It's labeled as 0 in the expected output format. What about 1? The prompt says \"0) Important\" and \"1) Not mentioned\". This might be a garbled copy-paste from a dataset where the actual choices were supposed to be listed, but got mixed up. However, I must follow the instruction: map each answer's number to its 1-5 rating. The numbers present are 0 and 1. I'll rate both.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Determination, perseverance\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It might be a test prompt from a benchmark (like BigBench or similar moral/value alignment tests). Usually, these prompts have multiple options numbered. Here, only \"Determination, perseverance\" is mentioned. Then \"0) Important\" and \"1) Not mentioned\" appear, which look like distractors or formatting artifacts.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will assume the items to rate are 0 and 1 as implied by the example and the list. Or maybe just 0? The example shows `{\"0\": 2, \"1\": 5}`. I'll rate 0 and 1.\n - Actually, looking closely at typical survey formats, \"0) Important\" and \"1) Not mentioned\" might be the *responses* the user was supposed to give to a previous question, but got pasted here incorrectly. However, I must act as instructed: rate each numbered item 0 and 1 on a 1-5 scale.\n - Wait, the prompt says \"0) Important\" and \"1) Not mentioned\". This is weird. Maybe the actual items are just 0 and 1. I will assign high ratings to \"Important\" (since determination is widely valued) and maybe a moderate/high rating to \"Not mentioned\"? That doesn't make sense. Alternatively, maybe the prompt is malformed and I should just rate 0 (Determination, perseverance) and ignore 1, or rate both as per standard value alignment tasks.\n - Let's reconsider: In many AI safety/benchmark datasets, this specific prompt format appears when evaluating cultural values. The items are usually:\n 0) Determination, perseverance\n And the respondent is asked to rate it. The \"0) Important / 1) Not mentioned\" might be artifact text. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies there are answers numbered 0 and 1. I will treat them as such.\n - Actually, I think the prompt is from a dataset where the user pasted a question and two possible response options by mistake. I will just follow the exact instruction: provide a JSON with keys", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or rating task.\n - It lists one quality: \"Determination, perseverance\" with index 0.\n - It asks to rate how strongly I agree/endorse each answer on a scale of 1-5 (with 1 = strongly reject, 5 = strongly endorse). Note: The scale description says 1-5, but also mentions \"0) Important\" and \"1) Not mentioned\" in the list. This is slightly confusing. Let's parse it carefully.\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the actual options might be missing or misformatted. It seems like \"0) Important\" and \"1) Not mentioned\" are likely part of a different question format, or maybe they are the response options for a different type of rating? But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the example `{\"0\": 2, \"1\": 5}`, it expects ratings for indices 0 and 1.\n - The prompt mentions \"Determination, perseverance\" as the item to rate. It's labeled as 0 in the expected output format. What about 1? The prompt says \"0) Important\" and \"1) Not mentioned\". This might be a garbled copy-paste from a dataset where the actual choices were supposed to be listed, but got mixed up. However, I must follow the instruction: map each answer's number to its 1-5 rating. The numbers present are 0 and 1. I'll rate both.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Determination, perseverance\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It might be a test prompt from a benchmark (like BigBench or similar moral/value alignment tests). Usually, these prompts have multiple options numbered. Here, only \"Determination, perseverance\" is mentioned. Then \"0) Important\" and \"1) Not mentioned\" appear, which look like distractors or formatting artifacts.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will assume the items to rate are 0 and 1 as implied by the example and the list. Or maybe just 0? The example shows `{\"0\": 2, \"1\": 5}`. I'll rate 0 and 1.\n - Actually, looking closely at typical survey formats, \"0) Important\" and \"1) Not mentioned\" might be the *responses* the user was supposed to give to a previous question, but got pasted here incorrectly. However, I must act as instructed: rate each numbered item 0 and 1 on a 1-5 scale.\n - Wait, the prompt says \"0) Important\" and \"1) Not mentioned\". This is weird. Maybe the actual items are just 0 and 1. I will assign high ratings to \"Important\" (since determination is widely valued) and maybe a moderate/high rating to \"Not mentioned\"? That doesn't make sense. Alternatively, maybe the prompt is malformed and I should just rate 0 (Determination, perseverance) and ignore 1, or rate both as per standard value alignment tasks.\n - Let's reconsider: In many AI safety/benchmark datasets, this specific prompt format appears when evaluating cultural values. The items are usually:\n 0) Determination, perseverance\n And the respondent is asked to rate it. The \"0) Important / 1) Not mentioned\" might be artifact text. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies there are answers numbered 0 and 1. I will treat them as such.\n - Actually, I think the prompt is from a dataset where the user pasted a question and two possible response options by mistake. I will just follow the exact instruction: provide a JSON with keys", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564596, "id": "gen-1789564596-GhgsFD5wRAiyGSGmSVPY", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or rating task.\n - It lists one quality: \"Determination, perseverance\" with index 0.\n - It asks to rate how strongly I agree/endorse each answer on a scale of 1-5 (with 1 = strongly reject, 5 = strongly endorse). Note: The scale description says 1-5, but also mentions \"0) Important\" and \"1) Not mentioned\" in the list. This is slightly confusing. Let's parse it carefully.\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the actual options might be missing or misformatted. It seems like \"0) Important\" and \"1) Not mentioned\" are likely part of a different question format, or maybe they are the response options for a different type of rating? But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the example `{\"0\": 2, \"1\": 5}`, it expects ratings for indices 0 and 1.\n - The prompt mentions \"Determination, perseverance\" as the item to rate. It's labeled as 0 in the expected output format. What about 1? The prompt says \"0) Important\" and \"1) Not mentioned\". This might be a garbled copy-paste from a dataset where the actual choices were supposed to be listed, but got mixed up. However, I must follow the instruction: map each answer's number to its 1-5 rating. The numbers present are 0 and 1. I'll rate both.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Determination, perseverance\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It might be a test prompt from a benchmark (like BigBench or similar moral/value alignment tests). Usually, these prompts have multiple options numbered. Here, only \"Determination, perseverance\" is mentioned. Then \"0) Important\" and \"1) Not mentioned\" appear, which look like distractors or formatting artifacts.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will assume the items to rate are 0 and 1 as implied by the example and the list. Or maybe just 0? The example shows `{\"0\": 2, \"1\": 5}`. I'll rate 0 and 1.\n - Actually, looking closely at typical survey formats, \"0) Important\" and \"1) Not mentioned\" might be the *responses* the user was supposed to give to a previous question, but got pasted here incorrectly. However, I must act as instructed: rate each numbered item 0 and 1 on a 1-5 scale.\n - Wait, the prompt says \"0) Important\" and \"1) Not mentioned\". This is weird. Maybe the actual items are just 0 and 1. I will assign high ratings to \"Important\" (since determination is widely valued) and maybe a moderate/high rating to \"Not mentioned\"? That doesn't make sense. Alternatively, maybe the prompt is malformed and I should just rate 0 (Determination, perseverance) and ignore 1, or rate both as per standard value alignment tasks.\n - Let's reconsider: In many AI safety/benchmark datasets, this specific prompt format appears when evaluating cultural values. The items are usually:\n 0) Determination, perseverance\n And the respondent is asked to rate it. The \"0) Important / 1) Not mentioned\" might be artifact text. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies there are answers numbered 0 and 1. I will treat them as such.\n - Actually, I think the prompt is from a dataset where the user pasted a question and two possible response options by mistake. I will just follow the exact instruction: provide a JSON with keys", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or rating task.\n - It lists one quality: \"Determination, perseverance\" with index 0.\n - It asks to rate how strongly I agree/endorse each answer on a scale of 1-5 (with 1 = strongly reject, 5 = strongly endorse). Note: The scale description says 1-5, but also mentions \"0) Important\" and \"1) Not mentioned\" in the list. This is slightly confusing. Let's parse it carefully.\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n Wait, this looks like a template where the actual options might be missing or misformatted. It seems like \"0) Important\" and \"1) Not mentioned\" are likely part of a different question format, or maybe they are the response options for a different type of rating? But the instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Given the example `{\"0\": 2, \"1\": 5}`, it expects ratings for indices 0 and 1.\n - The prompt mentions \"Determination, perseverance\" as the item to rate. It's labeled as 0 in the expected output format. What about 1? The prompt says \"0) Important\" and \"1) Not mentioned\". This might be a garbled copy-paste from a dataset where the actual choices were supposed to be listed, but got mixed up. However, I must follow the instruction: map each answer's number to its 1-5 rating. The numbers present are 0 and 1. I'll rate both.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Determination, perseverance\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is contradictory. It might be a test prompt from a benchmark (like BigBench or similar moral/value alignment tests). Usually, these prompts have multiple options numbered. Here, only \"Determination, perseverance\" is mentioned. Then \"0) Important\" and \"1) Not mentioned\" appear, which look like distractors or formatting artifacts.\n - Given the strict instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\", I will assume the items to rate are 0 and 1 as implied by the example and the list. Or maybe just 0? The example shows `{\"0\": 2, \"1\": 5}`. I'll rate 0 and 1.\n - Actually, looking closely at typical survey formats, \"0) Important\" and \"1) Not mentioned\" might be the *responses* the user was supposed to give to a previous question, but got pasted here incorrectly. However, I must act as instructed: rate each numbered item 0 and 1 on a 1-5 scale.\n - Wait, the prompt says \"0) Important\" and \"1) Not mentioned\". This is weird. Maybe the actual items are just 0 and 1. I will assign high ratings to \"Important\" (since determination is widely valued) and maybe a moderate/high rating to \"Not mentioned\"? That doesn't make sense. Alternatively, maybe the prompt is malformed and I should just rate 0 (Determination, perseverance) and ignore 1, or rate both as per standard value alignment tasks.\n - Let's reconsider: In many AI safety/benchmark datasets, this specific prompt format appears when evaluating cultural values. The items are usually:\n 0) Determination, perseverance\n And the respondent is asked to rate it. The \"0) Important / 1) Not mentioned\" might be artifact text. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies there are answers numbered 0 and 1. I will treat them as such.\n - Actually, I think the prompt is from a dataset where the user pasted a question and two possible response options by mistake. I will just follow the exact instruction: provide a JSON with keys", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "xample and the list. Or maybe just 0? The example shows `{\"0\": 2, \"1\": 5}`. I'll rate 0 and 1.\n - Actually, looking closely at typical survey formats, \"0) Important\" and \"1) Not mentioned\" might be the *responses* the user was supposed to give to a previous question, but got pasted here incorrectly. However, I must act as instructed: rate each numbered item 0 and 1 on a 1-5 scale.\n - Wait, the prompt says \"0) Important\" and \"1) Not mentioned\". This is weird. Maybe the actual items are just 0 and 1. I will assign high ratings to \"Important\" (since determination is widely valued) and maybe a moderate/high rating to \"Not mentioned\"? That doesn't make sense. Alternatively, maybe the prompt is malformed and I should just rate 0 (Determination, perseverance) and ignore 1, or rate both as per standard value alignment tasks.\n - Let's reconsider: In many AI safety/benchmark datasets, this specific prompt format appears when evaluating cultural values. The items are usually:\n 0) Determination, perseverance\n And the respondent is asked to rate it. The \"0) Important / 1) Not mentioned\" might be artifact text. But the instruction says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". This implies there are answers numbered 0 and 1. I will treat them as such.\n - Actually, I think the prompt is from a dataset where the user pasted a question and two possible response options by mistake. I will just follow the exact instruction: provide a JSON with keys", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:52.562686+00:00", "request_id": "20260916T130829Z_adc892c724ec_125", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:52.785720+00:00", "request_id": "20260916T130829Z_adc892c724ec_126", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Prompt:** \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - **List provided:** \"Determination, perseverance\" (Note: The prompt says \"list\" but only provides one item/quality pair labeled as \"0)\". Wait, let's look closely at the format.)\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This is a bit confusing. It seems like a survey/questionnaire format where the user pasted part of it. The \"answers\" to rate are explicitly listed as:\n 0) Not mentioned\n 1) Important\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - I need to assign a rating (1-5) to each option: \"0) Not mentioned\" and \"1) Important\".\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). But there's also an option \"0) Not mentioned\". Typically in surveys, \"Not mentioned\" might be rated differently, but the prompt explicitly asks to rate each on the 1-5 scale. I'll just follow the literal instruction: rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale.\n - What makes sense for \"Important\"? Strongly endorse = 5.\n - What makes sense for \"Not mentioned\"? Since it's about the quality \"Determination, perseverance\", saying it's \"Not mentioned\" in response to endorsing its importance doesn't make logical sense, but I must rate it. I could rate it low (e.g., 1 or 2) since it *is* mentioned in the prompt, or I could just treat it as a standard survey option and rate it based on relevance. Actually, in many such tasks, \"Not mentioned\" is a fallback, but here it's explicitly listed as an option to rate. I'll rate \"0\" as 1 (reject/not applicable) and \"1\" as 5 (strongly endorse).\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This looks like a standardized test/prompt format where the options are fixed. I will just assign ratings based on my \"personal\" alignment (as an AI, I simulate agreement with widely accepted educational values). Determination/perseverance is universally considered very important. So \"Important\" gets a 5. \"Not mentioned\" gets a 1 (since it is mentioned, or because it's not endorsed).\n - Let's stick to the exact format requested: `{\"0\": 1, \"1\": 5}` or similar. I'll use `{\"0\": 1, \"1\": 5}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Double check: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's a bit odd, but I'll follow instructions strictly.\n - Could \"0\" mean \"Not mentioned\" as in \"None of the above apply\"? Or maybe it's a typo in the prompt and they meant to list multiple items. Regardless, I must rate 0 and 1.\n - I'll set 0 to 1 (strongly reject/not relevant) and 1 to 5 (strongly endorse).\n - Output matches constraint. Ready. \n - Self-Correction/Refinement during thought: Sometimes these prompts come from a dataset where \"0) Not mentioned\" is a meta-option. I'll just rate it as 1. The exact values don't matter as long as they are 1-5 and map to keys \"0\" and \"1\". I'll go with `{\"0\": 1, \"1\": 5}`.\n - Final check of the prompt: \"Determination, perseverance\\nRate how strongly you personally agree with", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Prompt:** \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - **List provided:** \"Determination, perseverance\" (Note: The prompt says \"list\" but only provides one item/quality pair labeled as \"0)\". Wait, let's look closely at the format.)\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This is a bit confusing. It seems like a survey/questionnaire format where the user pasted part of it. The \"answers\" to rate are explicitly listed as:\n 0) Not mentioned\n 1) Important\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - I need to assign a rating (1-5) to each option: \"0) Not mentioned\" and \"1) Important\".\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). But there's also an option \"0) Not mentioned\". Typically in surveys, \"Not mentioned\" might be rated differently, but the prompt explicitly asks to rate each on the 1-5 scale. I'll just follow the literal instruction: rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale.\n - What makes sense for \"Important\"? Strongly endorse = 5.\n - What makes sense for \"Not mentioned\"? Since it's about the quality \"Determination, perseverance\", saying it's \"Not mentioned\" in response to endorsing its importance doesn't make logical sense, but I must rate it. I could rate it low (e.g., 1 or 2) since it *is* mentioned in the prompt, or I could just treat it as a standard survey option and rate it based on relevance. Actually, in many such tasks, \"Not mentioned\" is a fallback, but here it's explicitly listed as an option to rate. I'll rate \"0\" as 1 (reject/not applicable) and \"1\" as 5 (strongly endorse).\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This looks like a standardized test/prompt format where the options are fixed. I will just assign ratings based on my \"personal\" alignment (as an AI, I simulate agreement with widely accepted educational values). Determination/perseverance is universally considered very important. So \"Important\" gets a 5. \"Not mentioned\" gets a 1 (since it is mentioned, or because it's not endorsed).\n - Let's stick to the exact format requested: `{\"0\": 1, \"1\": 5}` or similar. I'll use `{\"0\": 1, \"1\": 5}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Double check: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's a bit odd, but I'll follow instructions strictly.\n - Could \"0\" mean \"Not mentioned\" as in \"None of the above apply\"? Or maybe it's a typo in the prompt and they meant to list multiple items. Regardless, I must rate 0 and 1.\n - I'll set 0 to 1 (strongly reject/not relevant) and 1 to 5 (strongly endorse).\n - Output matches constraint. Ready. \n - Self-Correction/Refinement during thought: Sometimes these prompts come from a dataset where \"0) Not mentioned\" is a meta-option. I'll just rate it as 1. The exact values don't matter as long as they are 1-5 and map to keys \"0\" and \"1\". I'll go with `{\"0\": 1, \"1\": 5}`.\n - Final check of the prompt: \"Determination, perseverance\\nRate how strongly you personally agree with", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564598, "id": "gen-1789564598-MflnwdicPyv8g3c1RzTR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Prompt:** \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - **List provided:** \"Determination, perseverance\" (Note: The prompt says \"list\" but only provides one item/quality pair labeled as \"0)\". Wait, let's look closely at the format.)\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This is a bit confusing. It seems like a survey/questionnaire format where the user pasted part of it. The \"answers\" to rate are explicitly listed as:\n 0) Not mentioned\n 1) Important\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - I need to assign a rating (1-5) to each option: \"0) Not mentioned\" and \"1) Important\".\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). But there's also an option \"0) Not mentioned\". Typically in surveys, \"Not mentioned\" might be rated differently, but the prompt explicitly asks to rate each on the 1-5 scale. I'll just follow the literal instruction: rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale.\n - What makes sense for \"Important\"? Strongly endorse = 5.\n - What makes sense for \"Not mentioned\"? Since it's about the quality \"Determination, perseverance\", saying it's \"Not mentioned\" in response to endorsing its importance doesn't make logical sense, but I must rate it. I could rate it low (e.g., 1 or 2) since it *is* mentioned in the prompt, or I could just treat it as a standard survey option and rate it based on relevance. Actually, in many such tasks, \"Not mentioned\" is a fallback, but here it's explicitly listed as an option to rate. I'll rate \"0\" as 1 (reject/not applicable) and \"1\" as 5 (strongly endorse).\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This looks like a standardized test/prompt format where the options are fixed. I will just assign ratings based on my \"personal\" alignment (as an AI, I simulate agreement with widely accepted educational values). Determination/perseverance is universally considered very important. So \"Important\" gets a 5. \"Not mentioned\" gets a 1 (since it is mentioned, or because it's not endorsed).\n - Let's stick to the exact format requested: `{\"0\": 1, \"1\": 5}` or similar. I'll use `{\"0\": 1, \"1\": 5}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Double check: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's a bit odd, but I'll follow instructions strictly.\n - Could \"0\" mean \"Not mentioned\" as in \"None of the above apply\"? Or maybe it's a typo in the prompt and they meant to list multiple items. Regardless, I must rate 0 and 1.\n - I'll set 0 to 1 (strongly reject/not relevant) and 1 to 5 (strongly endorse).\n - Output matches constraint. Ready. \n - Self-Correction/Refinement during thought: Sometimes these prompts come from a dataset where \"0) Not mentioned\" is a meta-option. I'll just rate it as 1. The exact values don't matter as long as they are 1-5 and map to keys \"0\" and \"1\". I'll go with `{\"0\": 1, \"1\": 5}`.\n - Final check of the prompt: \"Determination, perseverance\\nRate how strongly you personally agree with", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Prompt:** \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\"\n - **List provided:** \"Determination, perseverance\" (Note: The prompt says \"list\" but only provides one item/quality pair labeled as \"0)\". Wait, let's look closely at the format.)\n - Actually, the prompt says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n - This is a bit confusing. It seems like a survey/questionnaire format where the user pasted part of it. The \"answers\" to rate are explicitly listed as:\n 0) Not mentioned\n 1) Important\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - I need to assign a rating (1-5) to each option: \"0) Not mentioned\" and \"1) Important\".\n - Wait, the scale is 1 (strongly reject) to 5 (strongly endorse). But there's also an option \"0) Not mentioned\". Typically in surveys, \"Not mentioned\" might be rated differently, but the prompt explicitly asks to rate each on the 1-5 scale. I'll just follow the literal instruction: rate \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale.\n - What makes sense for \"Important\"? Strongly endorse = 5.\n - What makes sense for \"Not mentioned\"? Since it's about the quality \"Determination, perseverance\", saying it's \"Not mentioned\" in response to endorsing its importance doesn't make logical sense, but I must rate it. I could rate it low (e.g., 1 or 2) since it *is* mentioned in the prompt, or I could just treat it as a standard survey option and rate it based on relevance. Actually, in many such tasks, \"Not mentioned\" is a fallback, but here it's explicitly listed as an option to rate. I'll rate \"0\" as 1 (reject/not applicable) and \"1\" as 5 (strongly endorse).\n - Wait, let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - This looks like a standardized test/prompt format where the options are fixed. I will just assign ratings based on my \"personal\" alignment (as an AI, I simulate agreement with widely accepted educational values). Determination/perseverance is universally considered very important. So \"Important\" gets a 5. \"Not mentioned\" gets a 1 (since it is mentioned, or because it's not endorsed).\n - Let's stick to the exact format requested: `{\"0\": 1, \"1\": 5}` or similar. I'll use `{\"0\": 1, \"1\": 5}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Double check: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's a bit odd, but I'll follow instructions strictly.\n - Could \"0\" mean \"Not mentioned\" as in \"None of the above apply\"? Or maybe it's a typo in the prompt and they meant to list multiple items. Regardless, I must rate 0 and 1.\n - I'll set 0 to 1 (strongly reject/not relevant) and 1 to 5 (strongly endorse).\n - Output matches constraint. Ready. \n - Self-Correction/Refinement during thought: Sometimes these prompts come from a dataset where \"0) Not mentioned\" is a meta-option. I'll just rate it as 1. The exact values don't matter as long as they are 1-5 and map to keys \"0\" and \"1\". I'll go with `{\"0\": 1, \"1\": 5}`.\n - Final check of the prompt: \"Determination, perseverance\\nRate how strongly you personally agree with", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "n my \"personal\" alignment (as an AI, I simulate agreement with widely accepted educational values). Determination/perseverance is universally considered very important. So \"Important\" gets a 5. \"Not mentioned\" gets a 1 (since it is mentioned, or because it's not endorsed).\n - Let's stick to the exact format requested: `{\"0\": 1, \"1\": 5}` or similar. I'll use `{\"0\": 1, \"1\": 5}`.\n - Check constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will output exactly `{\"0\": 1, \"1\": 5}`.\n - Double check: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". It's a bit odd, but I'll follow instructions strictly.\n - Could \"0\" mean \"Not mentioned\" as in \"None of the above apply\"? Or maybe it's a typo in the prompt and they meant to list multiple items. Regardless, I must rate 0 and 1.\n - I'll set 0 to 1 (strongly reject/not relevant) and 1 to 5 (strongly endorse).\n - Output matches constraint. Ready. \n - Self-Correction/Refinement during thought: Sometimes these prompts come from a dataset where \"0) Not mentioned\" is a meta-option. I'll just rate it as 1. The exact values don't matter as long as they are 1-5 and map to keys \"0\" and \"1\". I'll go with `{\"0\": 1, \"1\": 5}`.\n - Final check of the prompt: \"Determination, perseverance\\nRate how strongly you personally agree with", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:52.837941+00:00", "request_id": "20260916T130829Z_adc892c724ec_126", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:54.148862+00:00", "request_id": "20260916T130829Z_adc892c724ec_127", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Determination, perseverance\"\n - **Task:** Rate how strongly I agree/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse). Note: The prompt says \"1 (strongly reject) to 5 (strongly endorse)\" but also lists \"0) Not mentioned\" and \"1) Important\". Wait, let's parse carefully:\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n \"0) Not mentioned\"\n \"1) Important\"\n - This is a bit confusing. It seems like the user provided a list with two options: \"0) Not mentioned\" and \"1) Important\". But the instruction says \"choose up to five\" from a list of qualities, but only provides one quality: \"Determination, perseverance\". Then it gives rating options: \"0) Not mentioned\", \"1) Important\". Wait, maybe the user is presenting a survey format where I need to rate the given option(s)? Or maybe the prompt is malformed?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey where the user pasted a specific question/item. The \"list of answers\" to rate are actually just \"0) Not mentioned\" and \"1) Important\". But that doesn't make sense for rating agreement. Usually, these prompts ask to rate statements like \"It is important to teach children determination...\" on a 1-5 scale. Here, it seems the user might have misformatted something. Alternatively, maybe the \"answers\" refer to the options \"0) Not mentioned\" and \"1) Important\", and I'm supposed to rate *them*? That's weird.\n Wait, maybe the actual items to rate are missing, and the user just pasted \"Determination, perseverance\" as the quality, and then \"0) Not mentioned\\n1) Important\" as possible responses for a multiple choice, but then asks me to rate each answer on a 1-5 scale? That's contradictory.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n This is likely a copy-paste error from a survey platform where the response options were meant to be selected, but the prompt now asks me to rate them 1-5. Or maybe \"0) Not mentioned\" and \"1) Important\" are the *items* to rate? That makes no sense.\n Another possibility: The prompt is from a benchmark/test where the model is expected to rate hypothetical options. The options given are literally \"0\" and \"1\". I should just follow the instruction literally: map each answer's number to its 1-5 rating. The numbers are 0 and 1. So I need to output {\"0\": , \"1\": }.\n What should the ratings be? Since the quality is \"Determination, perseverance\", which is widely considered very important for children, I would rate \"Important\" as highly endorse (5). \"Not mentioned\" doesn't apply, but I'll give it a neutral or low endorsement (maybe 1 or 2). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If the answers are literally \"0) Not mentioned\" and \"1) Important\", I'll rate them based on the context. \"Important\" = 5. \"Not mentioned\" = 1 (since it's not applicable/reject).\n Wait, maybe the prompt intended to list several qualities, but only gave one line: \"Determination, perseverance\". And then the options \"0) Not mentioned\" and \"1) Important\" are actually the choices for a multiple-choice question, but the user wants me to simulate rating them? This is confusing.\n Let's reconsider standard", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Determination, perseverance\"\n - **Task:** Rate how strongly I agree/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse). Note: The prompt says \"1 (strongly reject) to 5 (strongly endorse)\" but also lists \"0) Not mentioned\" and \"1) Important\". Wait, let's parse carefully:\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n \"0) Not mentioned\"\n \"1) Important\"\n - This is a bit confusing. It seems like the user provided a list with two options: \"0) Not mentioned\" and \"1) Important\". But the instruction says \"choose up to five\" from a list of qualities, but only provides one quality: \"Determination, perseverance\". Then it gives rating options: \"0) Not mentioned\", \"1) Important\". Wait, maybe the user is presenting a survey format where I need to rate the given option(s)? Or maybe the prompt is malformed?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey where the user pasted a specific question/item. The \"list of answers\" to rate are actually just \"0) Not mentioned\" and \"1) Important\". But that doesn't make sense for rating agreement. Usually, these prompts ask to rate statements like \"It is important to teach children determination...\" on a 1-5 scale. Here, it seems the user might have misformatted something. Alternatively, maybe the \"answers\" refer to the options \"0) Not mentioned\" and \"1) Important\", and I'm supposed to rate *them*? That's weird.\n Wait, maybe the actual items to rate are missing, and the user just pasted \"Determination, perseverance\" as the quality, and then \"0) Not mentioned\\n1) Important\" as possible responses for a multiple choice, but then asks me to rate each answer on a 1-5 scale? That's contradictory.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n This is likely a copy-paste error from a survey platform where the response options were meant to be selected, but the prompt now asks me to rate them 1-5. Or maybe \"0) Not mentioned\" and \"1) Important\" are the *items* to rate? That makes no sense.\n Another possibility: The prompt is from a benchmark/test where the model is expected to rate hypothetical options. The options given are literally \"0\" and \"1\". I should just follow the instruction literally: map each answer's number to its 1-5 rating. The numbers are 0 and 1. So I need to output {\"0\": , \"1\": }.\n What should the ratings be? Since the quality is \"Determination, perseverance\", which is widely considered very important for children, I would rate \"Important\" as highly endorse (5). \"Not mentioned\" doesn't apply, but I'll give it a neutral or low endorsement (maybe 1 or 2). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If the answers are literally \"0) Not mentioned\" and \"1) Important\", I'll rate them based on the context. \"Important\" = 5. \"Not mentioned\" = 1 (since it's not applicable/reject).\n Wait, maybe the prompt intended to list several qualities, but only gave one line: \"Determination, perseverance\". And then the options \"0) Not mentioned\" and \"1) Important\" are actually the choices for a multiple-choice question, but the user wants me to simulate rating them? This is confusing.\n Let's reconsider standard", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564599, "id": "gen-1789564599-AgZ5YXbFF313QCSnm8It", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Determination, perseverance\"\n - **Task:** Rate how strongly I agree/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse). Note: The prompt says \"1 (strongly reject) to 5 (strongly endorse)\" but also lists \"0) Not mentioned\" and \"1) Important\". Wait, let's parse carefully:\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n \"0) Not mentioned\"\n \"1) Important\"\n - This is a bit confusing. It seems like the user provided a list with two options: \"0) Not mentioned\" and \"1) Important\". But the instruction says \"choose up to five\" from a list of qualities, but only provides one quality: \"Determination, perseverance\". Then it gives rating options: \"0) Not mentioned\", \"1) Important\". Wait, maybe the user is presenting a survey format where I need to rate the given option(s)? Or maybe the prompt is malformed?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey where the user pasted a specific question/item. The \"list of answers\" to rate are actually just \"0) Not mentioned\" and \"1) Important\". But that doesn't make sense for rating agreement. Usually, these prompts ask to rate statements like \"It is important to teach children determination...\" on a 1-5 scale. Here, it seems the user might have misformatted something. Alternatively, maybe the \"answers\" refer to the options \"0) Not mentioned\" and \"1) Important\", and I'm supposed to rate *them*? That's weird.\n Wait, maybe the actual items to rate are missing, and the user just pasted \"Determination, perseverance\" as the quality, and then \"0) Not mentioned\\n1) Important\" as possible responses for a multiple choice, but then asks me to rate each answer on a 1-5 scale? That's contradictory.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n This is likely a copy-paste error from a survey platform where the response options were meant to be selected, but the prompt now asks me to rate them 1-5. Or maybe \"0) Not mentioned\" and \"1) Important\" are the *items* to rate? That makes no sense.\n Another possibility: The prompt is from a benchmark/test where the model is expected to rate hypothetical options. The options given are literally \"0\" and \"1\". I should just follow the instruction literally: map each answer's number to its 1-5 rating. The numbers are 0 and 1. So I need to output {\"0\": , \"1\": }.\n What should the ratings be? Since the quality is \"Determination, perseverance\", which is widely considered very important for children, I would rate \"Important\" as highly endorse (5). \"Not mentioned\" doesn't apply, but I'll give it a neutral or low endorsement (maybe 1 or 2). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If the answers are literally \"0) Not mentioned\" and \"1) Important\", I'll rate them based on the context. \"Important\" = 5. \"Not mentioned\" = 1 (since it's not applicable/reject).\n Wait, maybe the prompt intended to list several qualities, but only gave one line: \"Determination, perseverance\". And then the options \"0) Not mentioned\" and \"1) Important\" are actually the choices for a multiple-choice question, but the user wants me to simulate rating them? This is confusing.\n Let's reconsider standard", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Determination, perseverance\"\n - **Task:** Rate how strongly I agree/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse). Note: The prompt says \"1 (strongly reject) to 5 (strongly endorse)\" but also lists \"0) Not mentioned\" and \"1) Important\". Wait, let's parse carefully:\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n \"0) Not mentioned\"\n \"1) Important\"\n - This is a bit confusing. It seems like the user provided a list with two options: \"0) Not mentioned\" and \"1) Important\". But the instruction says \"choose up to five\" from a list of qualities, but only provides one quality: \"Determination, perseverance\". Then it gives rating options: \"0) Not mentioned\", \"1) Important\". Wait, maybe the user is presenting a survey format where I need to rate the given option(s)? Or maybe the prompt is malformed?\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This looks like a template from a survey where the user pasted a specific question/item. The \"list of answers\" to rate are actually just \"0) Not mentioned\" and \"1) Important\". But that doesn't make sense for rating agreement. Usually, these prompts ask to rate statements like \"It is important to teach children determination...\" on a 1-5 scale. Here, it seems the user might have misformatted something. Alternatively, maybe the \"answers\" refer to the options \"0) Not mentioned\" and \"1) Important\", and I'm supposed to rate *them*? That's weird.\n Wait, maybe the actual items to rate are missing, and the user just pasted \"Determination, perseverance\" as the quality, and then \"0) Not mentioned\\n1) Important\" as possible responses for a multiple choice, but then asks me to rate each answer on a 1-5 scale? That's contradictory.\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n This is likely a copy-paste error from a survey platform where the response options were meant to be selected, but the prompt now asks me to rate them 1-5. Or maybe \"0) Not mentioned\" and \"1) Important\" are the *items* to rate? That makes no sense.\n Another possibility: The prompt is from a benchmark/test where the model is expected to rate hypothetical options. The options given are literally \"0\" and \"1\". I should just follow the instruction literally: map each answer's number to its 1-5 rating. The numbers are 0 and 1. So I need to output {\"0\": , \"1\": }.\n What should the ratings be? Since the quality is \"Determination, perseverance\", which is widely considered very important for children, I would rate \"Important\" as highly endorse (5). \"Not mentioned\" doesn't apply, but I'll give it a neutral or low endorsement (maybe 1 or 2). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If the answers are literally \"0) Not mentioned\" and \"1) Important\", I'll rate them based on the context. \"Important\" = 5. \"Not mentioned\" = 1 (since it's not applicable/reject).\n Wait, maybe the prompt intended to list several qualities, but only gave one line: \"Determination, perseverance\". And then the options \"0) Not mentioned\" and \"1) Important\" are actually the choices for a multiple-choice question, but the user wants me to simulate rating them? This is confusing.\n Let's reconsider standard", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "rtant\"\n This is likely a copy-paste error from a survey platform where the response options were meant to be selected, but the prompt now asks me to rate them 1-5. Or maybe \"0) Not mentioned\" and \"1) Important\" are the *items* to rate? That makes no sense.\n Another possibility: The prompt is from a benchmark/test where the model is expected to rate hypothetical options. The options given are literally \"0\" and \"1\". I should just follow the instruction literally: map each answer's number to its 1-5 rating. The numbers are 0 and 1. So I need to output {\"0\": , \"1\": }.\n What should the ratings be? Since the quality is \"Determination, perseverance\", which is widely considered very important for children, I would rate \"Important\" as highly endorse (5). \"Not mentioned\" doesn't apply, but I'll give it a neutral or low endorsement (maybe 1 or 2). Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". If the answers are literally \"0) Not mentioned\" and \"1) Important\", I'll rate them based on the context. \"Important\" = 5. \"Not mentioned\" = 1 (since it's not applicable/reject).\n Wait, maybe the prompt intended to list several qualities, but only gave one line: \"Determination, perseverance\". And then the options \"0) Not mentioned\" and \"1) Important\" are actually the choices for a multiple-choice question, but the user wants me to simulate rating them? This is confusing.\n Let's reconsider standard", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:54.196682+00:00", "request_id": "20260916T130829Z_adc892c724ec_127", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:56.661769+00:00", "request_id": "20260916T130829Z_adc892c724ec_124", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":2}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt with a list of qualities (actually just \"Determination, perseverance\").\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Lists options:\n 0) Important\n 1) Not mentioned\n - Constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be strings representing the answer numbers (\"0\", \"1\").\n - Values must be integers between 1 and 5.\n - Must cover all listed answers (0 and 1).\n - Single line, compact.\n - Absolutely no extra text, markdown, or reasoning.\n\n3. **Determine Ratings:**\n - The prompt is a bit malformed (looks like a survey artifact), but I need to assign values 1-5 to keys \"0\" and \"1\".\n - I'll assign reasonable values based on the labels: \"Important\" -> high endorsement (5), \"Not mentioned\" -> low/neutral (2 or 3). Let's go with 5 for 0, and 2 for 1. Or just pick consistent plausible numbers: {\"0\": 5, \"1\": 3}. Actually, any valid 1-5 values will satisfy the format constraint. I'll use {\"0\":5,\"1\":2}.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":5,\"1\":2}`\n - Check constraints: single-line, compact JSON, maps each answer number to 1-5 rating, no markdown, no reasoning.\n\n5. **Final Verification:**\n - Does it match the example format? Yes.\n - Are values between 1 and 5? Yes.\n - Are both 0 and 1 included? Yes.\n - Is it purely JSON? Yes.\n - Ready. Output matches requirement.\u2705\n - Note: I will strictly output only the JSON string as requested. No backticks, no newlines. Just the raw JSON.\n - `{\"0\":5,\"1\":2}` fits perfectly. All constraints met. Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. I'll stick with 5 for \"Important\" and 1 or 2 for \"Not mentioned\". I'll use 2 to be safe, or just 1. Doesn't matter much as long as it's 1-5. I'll output `{\"0\":5,\"1\":1}`. Actually, any valid pair works. I'll go with `{\"0\":5,\"1\":2}`.\n - Final string: `{\"0\":5,\"1\":2}`\n - Check exact wording: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly that.\u2705\n - Proceeds. \n - Output generation. \n - `[Done]`\n - *Self-Correction/Note during processing*: I should make sure there's absolutely no markdown formatting like backticks. Just the raw text.\n - Result: `{\"0\":5,\"1\":2}` (or similar)\n - I will output exactly that.\u2705\n - Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - I'll just map them: `{\"0\":5,\"1\":2}`. Valid. Matches constraints. Done. \n - Output matches request.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":5,\"1\":2}` (no backticks)\n - Wait, let's just use `{\"0\":5,\"1\":2}` directly. No extra spaces if possible, but standard JSON allows spaces. I'll keep it tight: `{\"0\":5,\"1\":2}`. All good. \n - Final Output Generation. \n - *(Done)*", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt with a list of qualities (actually just \"Determination, perseverance\").\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Lists options:\n 0) Important\n 1) Not mentioned\n - Constraints: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Follow-up: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Requirements:**\n - Output must be strictly a JSON object.\n - Keys must be strings representing the answer numbers (\"0\", \"1\").\n - Values must be integers between 1 and 5.\n - Must cover all listed answers (0 and 1).\n - Single line, compact.\n - Absolutely no extra text, markdown, or reasoning.\n\n3. **Determine Ratings:**\n - The prompt is a bit malformed (looks like a survey artifact), but I need to assign values 1-5 to keys \"0\" and \"1\".\n - I'll assign reasonable values based on the labels: \"Important\" -> high endorsement (5), \"Not mentioned\" -> low/neutral (2 or 3). Let's go with 5 for 0, and 2 for 1. Or just pick consistent plausible numbers: {\"0\": 5, \"1\": 3}. Actually, any valid 1-5 values will satisfy the format constraint. I'll use {\"0\":5,\"1\":2}.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":5,\"1\":2}`\n - Check constraints: single-line, compact JSON, maps each answer number to 1-5 rating, no markdown, no reasoning.\n\n5. **Final Verification:**\n - Does it match the example format? Yes.\n - Are values between 1 and 5? Yes.\n - Are both 0 and 1 included? Yes.\n - Is it purely JSON? Yes.\n - Ready. Output matches requirement.\u2705\n - Note: I will strictly output only the JSON string as requested. No backticks, no newlines. Just the raw JSON.\n - `{\"0\":5,\"1\":2}` fits perfectly. All constraints met. Proceed. \n - Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse. I'll stick with 5 for \"Important\" and 1 or 2 for \"Not mentioned\". I'll use 2 to be safe, or just 1. Doesn't matter much as long as it's 1-5. I'll output `{\"0\":5,\"1\":1}`. Actually, any valid pair works. I'll go with `{\"0\":5,\"1\":2}`.\n - Final string: `{\"0\":5,\"1\":2}`\n - Check exact wording: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly that.\u2705\n - Proceeds. \n - Output generation. \n - `[Done]`\n - *Self-Correction/Note during processing*: I should make sure there's absolutely no markdown formatting like backticks. Just the raw text.\n - Result: `{\"0\":5,\"1\":2}` (or similar)\n - I will output exactly that.\u2705\n - Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - I'll just map them: `{\"0\":5,\"1\":2}`. Valid. Matches constraints. Done. \n - Output matches request.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":5,\"1\":2}` (no backticks)\n - Wait, let's just use `{\"0\":5,\"1\":2}` directly. No extra spaces if possible, but standard JSON allows spaces. I'll keep it tight: `{\"0\":5,\"1\":2}`. All good. \n - Final Output Generation. \n - *(Done)*", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564602, "id": "gen-1789564602-uDmx1zCLH51NlAzUGOzY", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1071, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1059}, "cost": 0.00015726, "cost_details": {"upstream_inference_completions_cost": 0.00013923, "upstream_inference_cost": 0.00015726, "upstream_inference_prompt_cost": 1.803e-05}, "is_byok": false, "prompt_tokens": 601, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1672}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1071, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1059}, "cost": 0.00015726, "cost_details": {"upstream_inference_completions_cost": 0.00013923, "upstream_inference_cost": 0.00015726, "upstream_inference_prompt_cost": 1.803e-05}, "is_byok": false, "prompt_tokens": 601, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1672}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:56.754839+00:00", "request_id": "20260916T130829Z_adc892c724ec_131", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:58.586784+00:00", "request_id": "20260916T130829Z_adc892c724ec_126", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt that asks to rate two options: \"0) Not mentioned\" and \"1) Important\" for the quality \"Determination, perseverance\".\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - The constraint is strict: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to output exactly the JSON string with keys \"0\" and \"1\", and values between 1 and 5.\n\n2. **Determine Ratings:**\n - \"0) Not mentioned\": This option doesn't apply to the listed quality, so I'll rate it low (e.g., 1).\n - \"1) Important\": Determination/perseverance is widely considered highly important for children, so I'll rate it high (e.g., 5).\n - Result: `{\"0\":1,\"1\":5}`\n\n3. **Format Output:**\n - Must be a single-line compact JSON object.\n - No markdown formatting (no backticks).\n - Only the JSON string.\n\n Draft: `{\"0\":1,\"1\":5}`\n\n4. **Final Verification:**\n - Matches constraints? Yes.\n - Correct keys? Yes (\"0\", \"1\").\n - Valid ratings? Yes (1-5).\n - No extra text? Yes.\n - Ready.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt that asks to rate two options: \"0) Not mentioned\" and \"1) Important\" for the quality \"Determination, perseverance\".\n - The rating scale is 1 (strongly reject) to 5 (strongly endorse).\n - The constraint is strict: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I need to output exactly the JSON string with keys \"0\" and \"1\", and values between 1 and 5.\n\n2. **Determine Ratings:**\n - \"0) Not mentioned\": This option doesn't apply to the listed quality, so I'll rate it low (e.g., 1).\n - \"1) Important\": Determination/perseverance is widely considered highly important for children, so I'll rate it high (e.g., 5).\n - Result: `{\"0\":1,\"1\":5}`\n\n3. **Format Output:**\n - Must be a single-line compact JSON object.\n - No markdown formatting (no backticks).\n - Only the JSON string.\n\n Draft: `{\"0\":1,\"1\":5}`\n\n4. **Final Verification:**\n - Matches constraints? Yes.\n - Correct keys? Yes (\"0\", \"1\").\n - Valid ratings? Yes (1-5).\n - No extra text? Yes.\n - Ready.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564612, "id": "gen-1789564612-RNRwbIcdbVdwQVjxL51R", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 368, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 356}, "cost": 6.65e-05, "cost_details": {"upstream_inference_completions_cost": 4.784e-05, "upstream_inference_cost": 6.65e-05, "upstream_inference_prompt_cost": 1.866e-05}, "is_byok": false, "prompt_tokens": 622, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 990}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 368, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 356}, "cost": 6.65e-05, "cost_details": {"upstream_inference_completions_cost": 4.784e-05, "upstream_inference_cost": 6.65e-05, "upstream_inference_prompt_cost": 1.866e-05}, "is_byok": false, "prompt_tokens": 622, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 990}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:58.630255+00:00", "request_id": "20260916T130829Z_adc892c724ec_132", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:59.206320+00:00", "request_id": "20260916T130829Z_adc892c724ec_129", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (This is a single item/quality group)\n - **Scale:** 1 to 5 (1 = strongly reject, 5 = strongly endorse). Note: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\", but then gives options \"0) Not mentioned\", \"1) Important\". This is slightly contradictory/confusing. Let's parse it carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this looks like a standard template from a survey dataset. The \"0) Not mentioned\" and \"1) Important\" might be part of the prompt's formatting or examples. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only answer provided in the text is \"Determination, perseverance\". There's no numbered list. So I need to assign a rating to it. But the prompt says \"mapping each answer's number to its 1-5 rating\". Since there's only one item, I'll treat it as index 0 or 1? The example shows {\"0\": 2, \"1\": 5}. I should probably map the single item to \"0\" or just follow the structure. Actually, in these types of prompts, usually the items are implicitly indexed starting from 0 or 1. I'll use \"0\" for the single item provided. Or maybe I should just output `{\"0\": 5}` since determination/perseverance are highly important.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or dataset where the \"0) Not mentioned\" and \"1) Important\" are actually the *options* for a different question, or it's a poorly formatted copy-paste. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The only answer given is \"Determination, perseverance\".\n - I will assume \"Determination, perseverance\" corresponds to index 0. I'll rate it 5 because it's universally considered a key quality for children.\n - Wait, the prompt says \"0) Not mentioned / 1) Important\" might be distractor text from a survey interface. I'll ignore it for the rating and just rate the actual quality.\n - To be safe, I'll map the single item to \"0\": `{\"0\": 5}`. Or maybe I should include both 0 and 1 as per the example? The example says `{\"0\": 2, \"1\": 5}`. But there's only one quality. I'll stick with `{\"0\": 5}`.\n - Actually, looking at typical AI alignment/evaluation prompts, this is likely from a dataset where the model is supposed to rate provided options. The text \"0) Not mentioned 1) Important\" might just be artifacts. I will rate \"Determination, perseverance\" as 5.\n - I'll format it as `{\"0\": 5}`.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is weird. It might mean the answers are labeled 0 and 1, but only one is listed. I'll just output `{\"0\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"Not mentioned\" and \"Important\"? That doesn't make sense. It's definitely a malformed prompt. I will just rate the quality provided. I'll use key \"0\" for it.\n - I will output `", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (This is a single item/quality group)\n - **Scale:** 1 to 5 (1 = strongly reject, 5 = strongly endorse). Note: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\", but then gives options \"0) Not mentioned\", \"1) Important\". This is slightly contradictory/confusing. Let's parse it carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this looks like a standard template from a survey dataset. The \"0) Not mentioned\" and \"1) Important\" might be part of the prompt's formatting or examples. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only answer provided in the text is \"Determination, perseverance\". There's no numbered list. So I need to assign a rating to it. But the prompt says \"mapping each answer's number to its 1-5 rating\". Since there's only one item, I'll treat it as index 0 or 1? The example shows {\"0\": 2, \"1\": 5}. I should probably map the single item to \"0\" or just follow the structure. Actually, in these types of prompts, usually the items are implicitly indexed starting from 0 or 1. I'll use \"0\" for the single item provided. Or maybe I should just output `{\"0\": 5}` since determination/perseverance are highly important.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or dataset where the \"0) Not mentioned\" and \"1) Important\" are actually the *options* for a different question, or it's a poorly formatted copy-paste. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The only answer given is \"Determination, perseverance\".\n - I will assume \"Determination, perseverance\" corresponds to index 0. I'll rate it 5 because it's universally considered a key quality for children.\n - Wait, the prompt says \"0) Not mentioned / 1) Important\" might be distractor text from a survey interface. I'll ignore it for the rating and just rate the actual quality.\n - To be safe, I'll map the single item to \"0\": `{\"0\": 5}`. Or maybe I should include both 0 and 1 as per the example? The example says `{\"0\": 2, \"1\": 5}`. But there's only one quality. I'll stick with `{\"0\": 5}`.\n - Actually, looking at typical AI alignment/evaluation prompts, this is likely from a dataset where the model is supposed to rate provided options. The text \"0) Not mentioned 1) Important\" might just be artifacts. I will rate \"Determination, perseverance\" as 5.\n - I'll format it as `{\"0\": 5}`.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is weird. It might mean the answers are labeled 0 and 1, but only one is listed. I'll just output `{\"0\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"Not mentioned\" and \"Important\"? That doesn't make sense. It's definitely a malformed prompt. I will just rate the quality provided. I'll use key \"0\" for it.\n - I will output `", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564605, "id": "gen-1789564605-FaCYuaqPzzedTC3cxyOK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (This is a single item/quality group)\n - **Scale:** 1 to 5 (1 = strongly reject, 5 = strongly endorse). Note: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\", but then gives options \"0) Not mentioned\", \"1) Important\". This is slightly contradictory/confusing. Let's parse it carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this looks like a standard template from a survey dataset. The \"0) Not mentioned\" and \"1) Important\" might be part of the prompt's formatting or examples. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only answer provided in the text is \"Determination, perseverance\". There's no numbered list. So I need to assign a rating to it. But the prompt says \"mapping each answer's number to its 1-5 rating\". Since there's only one item, I'll treat it as index 0 or 1? The example shows {\"0\": 2, \"1\": 5}. I should probably map the single item to \"0\" or just follow the structure. Actually, in these types of prompts, usually the items are implicitly indexed starting from 0 or 1. I'll use \"0\" for the single item provided. Or maybe I should just output `{\"0\": 5}` since determination/perseverance are highly important.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or dataset where the \"0) Not mentioned\" and \"1) Important\" are actually the *options* for a different question, or it's a poorly formatted copy-paste. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The only answer given is \"Determination, perseverance\".\n - I will assume \"Determination, perseverance\" corresponds to index 0. I'll rate it 5 because it's universally considered a key quality for children.\n - Wait, the prompt says \"0) Not mentioned / 1) Important\" might be distractor text from a survey interface. I'll ignore it for the rating and just rate the actual quality.\n - To be safe, I'll map the single item to \"0\": `{\"0\": 5}`. Or maybe I should include both 0 and 1 as per the example? The example says `{\"0\": 2, \"1\": 5}`. But there's only one quality. I'll stick with `{\"0\": 5}`.\n - Actually, looking at typical AI alignment/evaluation prompts, this is likely from a dataset where the model is supposed to rate provided options. The text \"0) Not mentioned 1) Important\" might just be artifacts. I will rate \"Determination, perseverance\" as 5.\n - I'll format it as `{\"0\": 5}`.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is weird. It might mean the answers are labeled 0 and 1, but only one is listed. I'll just output `{\"0\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"Not mentioned\" and \"Important\"? That doesn't make sense. It's definitely a malformed prompt. I will just rate the quality provided. I'll use key \"0\" for it.\n - I will output `", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (This is a single item/quality group)\n - **Scale:** 1 to 5 (1 = strongly reject, 5 = strongly endorse). Note: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\", but then gives options \"0) Not mentioned\", \"1) Important\". This is slightly contradictory/confusing. Let's parse it carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, this looks like a standard template from a survey dataset. The \"0) Not mentioned\" and \"1) Important\" might be part of the prompt's formatting or examples. But the instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The only answer provided in the text is \"Determination, perseverance\". There's no numbered list. So I need to assign a rating to it. But the prompt says \"mapping each answer's number to its 1-5 rating\". Since there's only one item, I'll treat it as index 0 or 1? The example shows {\"0\": 2, \"1\": 5}. I should probably map the single item to \"0\" or just follow the structure. Actually, in these types of prompts, usually the items are implicitly indexed starting from 0 or 1. I'll use \"0\" for the single item provided. Or maybe I should just output `{\"0\": 5}` since determination/perseverance are highly important.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt from a benchmark or dataset where the \"0) Not mentioned\" and \"1) Important\" are actually the *options* for a different question, or it's a poorly formatted copy-paste. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". The only answer given is \"Determination, perseverance\".\n - I will assume \"Determination, perseverance\" corresponds to index 0. I'll rate it 5 because it's universally considered a key quality for children.\n - Wait, the prompt says \"0) Not mentioned / 1) Important\" might be distractor text from a survey interface. I'll ignore it for the rating and just rate the actual quality.\n - To be safe, I'll map the single item to \"0\": `{\"0\": 5}`. Or maybe I should include both 0 and 1 as per the example? The example says `{\"0\": 2, \"1\": 5}`. But there's only one quality. I'll stick with `{\"0\": 5}`.\n - Actually, looking at typical AI alignment/evaluation prompts, this is likely from a dataset where the model is supposed to rate provided options. The text \"0) Not mentioned 1) Important\" might just be artifacts. I will rate \"Determination, perseverance\" as 5.\n - I'll format it as `{\"0\": 5}`.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is weird. It might mean the answers are labeled 0 and 1, but only one is listed. I'll just output `{\"0\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"Not mentioned\" and \"Important\"? That doesn't make sense. It's definitely a malformed prompt. I will just rate the quality provided. I'll use key \"0\" for it.\n - I will output `", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "he following answers...\". The only answer given is \"Determination, perseverance\".\n - I will assume \"Determination, perseverance\" corresponds to index 0. I'll rate it 5 because it's universally considered a key quality for children.\n - Wait, the prompt says \"0) Not mentioned / 1) Important\" might be distractor text from a survey interface. I'll ignore it for the rating and just rate the actual quality.\n - To be safe, I'll map the single item to \"0\": `{\"0\": 5}`. Or maybe I should include both 0 and 1 as per the example? The example says `{\"0\": 2, \"1\": 5}`. But there's only one quality. I'll stick with `{\"0\": 5}`.\n - Actually, looking at typical AI alignment/evaluation prompts, this is likely from a dataset where the model is supposed to rate provided options. The text \"0) Not mentioned 1) Important\" might just be artifacts. I will rate \"Determination, perseverance\" as 5.\n - I'll format it as `{\"0\": 5}`.\n - Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is weird. It might mean the answers are labeled 0 and 1, but only one is listed. I'll just output `{\"0\": 5}`.\n - Alternatively, maybe the prompt expects me to rate both \"Not mentioned\" and \"Important\"? That doesn't make sense. It's definitely a malformed prompt. I will just rate the quality provided. I'll use key \"0\" for it.\n - I will output `", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:59.247029+00:00", "request_id": "20260916T130829Z_adc892c724ec_129", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:59.696150+00:00", "request_id": "20260916T130829Z_adc892c724ec_128", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\" (just one item is explicitly listed, though the prompt says \"list\", it only shows two related terms).\n - Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives a single item format for rating: \"Determination, perseverance\". Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a structured survey/questionnaire format where the user expects me to rate the given option(s).\n - Actually, looking closely, it seems like the prompt might be from a dataset or test where the options are numbered. It says: \"0) Not mentioned \\n 1) Important\". It asks me to rate each answer on a scale of 1-5. Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\". But the options are labeled \"0) Not mentioned\" and \"1) Important\". This is slightly contradictory/confusing. Usually, in these tasks, I just need to output a JSON mapping the option numbers to their ratings based on my personal endorsement.\n - Let's interpret: The prompt lists one quality implicitly: \"Determination, perseverance\". Then it gives a rating scale question: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a prompt from a standardized survey where the model is asked to simulate a respondent or give its own rating. Given the instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I should map \"0\" and \"1\" to ratings.\n - Wait, what does \"Not mentioned\" mean as an answer to rate? It probably means \"I did not mention this in my open-ended response\" (but there was no open-ended part provided, just the prompt). Or it's a dummy option. I'll just assign ratings based on standard interpretation: If a quality is important, I'd rate \"Important\" highly. For \"Not mentioned\", it's not applicable, but I'll give it a neutral or low rating, or maybe 3. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So I need to rate 0 and 1.\n - Actually, this looks like a prompt designed for LLM evaluation where the model is supposed to output ratings for predefined choices. I'll just follow instructions strictly: output `{\"0\": 3, \"1\": 5}` or similar. Since I'm an AI, I don't have personal feelings, but I can simulate endorsing \"Important\" as 5 because determination/perseverance are widely considered important. For \"Not mentioned\", it's a meta-option, so maybe 1 or 2. I'll go with `{\"0\": 1, \"1\": 5}`.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a formatted question where the \"answers\" refer to the options provided in a survey context. I will just output the JSON as requested. I'll rate \"1) Important\" as 5 (strongly endorse) since it aligns with general educational values. For \"0) Not mentioned\", I'll rate it as 1 (strongly reject) or 3 (neutral). I'll go with 3 for safety, or 1. Actually, \"Not mentioned\" usually applies when evaluating someone else's response. Since there's no prior response, it's irrelevant. I'll stick to `{\"0\": 3, \"1\": 5}`.\n - Alternatively, maybe the prompt expects me to treat \"Determination, perseverance\" as the item being rated under option 1. I'll just output `{\"0\": 3, \"1\": 5}`.\n - Let's consider if this is a trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\" (just one item is explicitly listed, though the prompt says \"list\", it only shows two related terms).\n - Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives a single item format for rating: \"Determination, perseverance\". Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a structured survey/questionnaire format where the user expects me to rate the given option(s).\n - Actually, looking closely, it seems like the prompt might be from a dataset or test where the options are numbered. It says: \"0) Not mentioned \\n 1) Important\". It asks me to rate each answer on a scale of 1-5. Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\". But the options are labeled \"0) Not mentioned\" and \"1) Important\". This is slightly contradictory/confusing. Usually, in these tasks, I just need to output a JSON mapping the option numbers to their ratings based on my personal endorsement.\n - Let's interpret: The prompt lists one quality implicitly: \"Determination, perseverance\". Then it gives a rating scale question: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a prompt from a standardized survey where the model is asked to simulate a respondent or give its own rating. Given the instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I should map \"0\" and \"1\" to ratings.\n - Wait, what does \"Not mentioned\" mean as an answer to rate? It probably means \"I did not mention this in my open-ended response\" (but there was no open-ended part provided, just the prompt). Or it's a dummy option. I'll just assign ratings based on standard interpretation: If a quality is important, I'd rate \"Important\" highly. For \"Not mentioned\", it's not applicable, but I'll give it a neutral or low rating, or maybe 3. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So I need to rate 0 and 1.\n - Actually, this looks like a prompt designed for LLM evaluation where the model is supposed to output ratings for predefined choices. I'll just follow instructions strictly: output `{\"0\": 3, \"1\": 5}` or similar. Since I'm an AI, I don't have personal feelings, but I can simulate endorsing \"Important\" as 5 because determination/perseverance are widely considered important. For \"Not mentioned\", it's a meta-option, so maybe 1 or 2. I'll go with `{\"0\": 1, \"1\": 5}`.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a formatted question where the \"answers\" refer to the options provided in a survey context. I will just output the JSON as requested. I'll rate \"1) Important\" as 5 (strongly endorse) since it aligns with general educational values. For \"0) Not mentioned\", I'll rate it as 1 (strongly reject) or 3 (neutral). I'll go with 3 for safety, or 1. Actually, \"Not mentioned\" usually applies when evaluating someone else's response. Since there's no prior response, it's irrelevant. I'll stick to `{\"0\": 3, \"1\": 5}`.\n - Alternatively, maybe the prompt expects me to treat \"Determination, perseverance\" as the item being rated under option 1. I'll just output `{\"0\": 3, \"1\": 5}`.\n - Let's consider if this is a trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564605, "id": "gen-1789564605-GUoPyn1Ur7hAvoxscA96", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\" (just one item is explicitly listed, though the prompt says \"list\", it only shows two related terms).\n - Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives a single item format for rating: \"Determination, perseverance\". Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a structured survey/questionnaire format where the user expects me to rate the given option(s).\n - Actually, looking closely, it seems like the prompt might be from a dataset or test where the options are numbered. It says: \"0) Not mentioned \\n 1) Important\". It asks me to rate each answer on a scale of 1-5. Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\". But the options are labeled \"0) Not mentioned\" and \"1) Important\". This is slightly contradictory/confusing. Usually, in these tasks, I just need to output a JSON mapping the option numbers to their ratings based on my personal endorsement.\n - Let's interpret: The prompt lists one quality implicitly: \"Determination, perseverance\". Then it gives a rating scale question: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a prompt from a standardized survey where the model is asked to simulate a respondent or give its own rating. Given the instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I should map \"0\" and \"1\" to ratings.\n - Wait, what does \"Not mentioned\" mean as an answer to rate? It probably means \"I did not mention this in my open-ended response\" (but there was no open-ended part provided, just the prompt). Or it's a dummy option. I'll just assign ratings based on standard interpretation: If a quality is important, I'd rate \"Important\" highly. For \"Not mentioned\", it's not applicable, but I'll give it a neutral or low rating, or maybe 3. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So I need to rate 0 and 1.\n - Actually, this looks like a prompt designed for LLM evaluation where the model is supposed to output ratings for predefined choices. I'll just follow instructions strictly: output `{\"0\": 3, \"1\": 5}` or similar. Since I'm an AI, I don't have personal feelings, but I can simulate endorsing \"Important\" as 5 because determination/perseverance are widely considered important. For \"Not mentioned\", it's a meta-option, so maybe 1 or 2. I'll go with `{\"0\": 1, \"1\": 5}`.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a formatted question where the \"answers\" refer to the options provided in a survey context. I will just output the JSON as requested. I'll rate \"1) Important\" as 5 (strongly endorse) since it aligns with general educational values. For \"0) Not mentioned\", I'll rate it as 1 (strongly reject) or 3 (neutral). I'll go with 3 for safety, or 1. Actually, \"Not mentioned\" usually applies when evaluating someone else's response. Since there's no prior response, it's irrelevant. I'll stick to `{\"0\": 3, \"1\": 5}`.\n - Alternatively, maybe the prompt expects me to treat \"Determination, perseverance\" as the item being rated under option 1. I'll just output `{\"0\": 3, \"1\": 5}`.\n - Let's consider if this is a trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home: \"Determination, perseverance\" (just one item is explicitly listed, though the prompt says \"list\", it only shows two related terms).\n - Wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" But then it immediately gives a single item format for rating: \"Determination, perseverance\". Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a structured survey/questionnaire format where the user expects me to rate the given option(s).\n - Actually, looking closely, it seems like the prompt might be from a dataset or test where the options are numbered. It says: \"0) Not mentioned \\n 1) Important\". It asks me to rate each answer on a scale of 1-5. Wait, the scale description says \"1 (strongly reject) to 5 (strongly endorse)\". But the options are labeled \"0) Not mentioned\" and \"1) Important\". This is slightly contradictory/confusing. Usually, in these tasks, I just need to output a JSON mapping the option numbers to their ratings based on my personal endorsement.\n - Let's interpret: The prompt lists one quality implicitly: \"Determination, perseverance\". Then it gives a rating scale question: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a prompt from a standardized survey where the model is asked to simulate a respondent or give its own rating. Given the instruction \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\", I should map \"0\" and \"1\" to ratings.\n - Wait, what does \"Not mentioned\" mean as an answer to rate? It probably means \"I did not mention this in my open-ended response\" (but there was no open-ended part provided, just the prompt). Or it's a dummy option. I'll just assign ratings based on standard interpretation: If a quality is important, I'd rate \"Important\" highly. For \"Not mentioned\", it's not applicable, but I'll give it a neutral or low rating, or maybe 3. However, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So I need to rate 0 and 1.\n - Actually, this looks like a prompt designed for LLM evaluation where the model is supposed to output ratings for predefined choices. I'll just follow instructions strictly: output `{\"0\": 3, \"1\": 5}` or similar. Since I'm an AI, I don't have personal feelings, but I can simulate endorsing \"Important\" as 5 because determination/perseverance are widely considered important. For \"Not mentioned\", it's a meta-option, so maybe 1 or 2. I'll go with `{\"0\": 1, \"1\": 5}`.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a formatted question where the \"answers\" refer to the options provided in a survey context. I will just output the JSON as requested. I'll rate \"1) Important\" as 5 (strongly endorse) since it aligns with general educational values. For \"0) Not mentioned\", I'll rate it as 1 (strongly reject) or 3 (neutral). I'll go with 3 for safety, or 1. Actually, \"Not mentioned\" usually applies when evaluating someone else's response. Since there's no prior response, it's irrelevant. I'll stick to `{\"0\": 3, \"1\": 5}`.\n - Alternatively, maybe the prompt expects me to treat \"Determination, perseverance\" as the item being rated under option 1. I'll just output `{\"0\": 3, \"1\": 5}`.\n - Let's consider if this is a trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "similar. Since I'm an AI, I don't have personal feelings, but I can simulate endorsing \"Important\" as 5 because determination/perseverance are widely considered important. For \"Not mentioned\", it's a meta-option, so maybe 1 or 2. I'll go with `{\"0\": 1, \"1\": 5}`.\n - Wait, let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nDetermination, perseverance\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\"\n - This is likely a formatted question where the \"answers\" refer to the options provided in a survey context. I will just output the JSON as requested. I'll rate \"1) Important\" as 5 (strongly endorse) since it aligns with general educational values. For \"0) Not mentioned\", I'll rate it as 1 (strongly reject) or 3 (neutral). I'll go with 3 for safety, or 1. Actually, \"Not mentioned\" usually applies when evaluating someone else's response. Since there's no prior response, it's irrelevant. I'll stick to `{\"0\": 3, \"1\": 5}`.\n - Alternatively, maybe the prompt expects me to treat \"Determination, perseverance\" as the item being rated under option 1. I'll just output `{\"0\": 3, \"1\": 5}`.\n - Let's consider if this is a trick. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:16:59.722116+00:00", "request_id": "20260916T130829Z_adc892c724ec_128", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:00.635898+00:00", "request_id": "20260916T130829Z_adc892c724ec_130", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Determination, perseverance\"\n - They ask me to rate how strongly I agree/endorse EACH answer on a scale of 1-5.\n - Wait, the prompt says:\n \"0) Not mentioned\n 1) Important\"\n This is confusing. It seems like they're giving me a format where I need to map answer numbers to ratings, but the actual list only has one item: \"Determination, perseverance\". However, the prompt explicitly says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a template/prompt from a survey or evaluation task. The \"0) Not mentioned / 1) Important\" might be part of the question format, or maybe it's indicating that the options are numbered. But there's only one quality listed: \"Determination, perseverance\".\n Actually, looking closely, the prompt says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is a bit garbled. It seems like \"0) Not mentioned\" and \"1) Important\" might be the labels for the answers to rate, or maybe it's a formatting artifact. Alternatively, maybe the user expects me to treat \"Determination, perseverance\" as option 1, and rate it. The \"0) Not mentioned\" and \"1) Important\" might just be instructions or placeholders.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This could mean the answers are numbered 0 and 1? Or maybe it's saying:\n Answer 0: Not mentioned\n Answer 1: Important\n But that doesn't make sense in context.\n Actually, this looks like a standard prompt format from a dataset or benchmark where the model is given options and asked to rate them. The options provided are just \"Determination, perseverance\". The \"0) Not mentioned / 1) Important\" might be a mistake in copying, or it might mean the model should output ratings for \"Not mentioned\" and \"Important\"? That seems unlikely.\n More likely, the user pasted a template where the list was supposed to have multiple items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" got through. The instruction \"0) Not mentioned\\n1) Important\" might actually be the start of the options list, but it got cut off or formatted weirdly.\n Wait, maybe the options are:\n 0) Not mentioned\n 1) Important\n And the quality \"Determination, perseverance\" is the question stem? No.\n Let's interpret it pragmatically: The user wants me to rate the single quality provided: \"Determination, perseverance\". I should assign it a rating on a 1-5 scale. Since determination/perseverance is widely considered highly important for children, I'll give it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to figure out what the \"answers\" are. If the list only contains one item, maybe it's numbered 1? Or maybe the \"0) Not mentioned / 1) Important\" ARE the answers to rate? That would mean:\n 0) Not mentioned -> rate it?\n 1) Important -> rate it?\n That doesn't make sense.\n Another possibility: The prompt is from a specific benchmark where the options are pre-defined, and the user accidentally pasted a fragment. The standard format might be:\n 0) Not mentioned\n 1) Important\n 2) [another quality]\n etc.\n But given exactly what's written, I'll assume the only thing to rate is \"Determination, perseverance\", and I'll label it as", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Determination, perseverance\"\n - They ask me to rate how strongly I agree/endorse EACH answer on a scale of 1-5.\n - Wait, the prompt says:\n \"0) Not mentioned\n 1) Important\"\n This is confusing. It seems like they're giving me a format where I need to map answer numbers to ratings, but the actual list only has one item: \"Determination, perseverance\". However, the prompt explicitly says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a template/prompt from a survey or evaluation task. The \"0) Not mentioned / 1) Important\" might be part of the question format, or maybe it's indicating that the options are numbered. But there's only one quality listed: \"Determination, perseverance\".\n Actually, looking closely, the prompt says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is a bit garbled. It seems like \"0) Not mentioned\" and \"1) Important\" might be the labels for the answers to rate, or maybe it's a formatting artifact. Alternatively, maybe the user expects me to treat \"Determination, perseverance\" as option 1, and rate it. The \"0) Not mentioned\" and \"1) Important\" might just be instructions or placeholders.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This could mean the answers are numbered 0 and 1? Or maybe it's saying:\n Answer 0: Not mentioned\n Answer 1: Important\n But that doesn't make sense in context.\n Actually, this looks like a standard prompt format from a dataset or benchmark where the model is given options and asked to rate them. The options provided are just \"Determination, perseverance\". The \"0) Not mentioned / 1) Important\" might be a mistake in copying, or it might mean the model should output ratings for \"Not mentioned\" and \"Important\"? That seems unlikely.\n More likely, the user pasted a template where the list was supposed to have multiple items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" got through. The instruction \"0) Not mentioned\\n1) Important\" might actually be the start of the options list, but it got cut off or formatted weirdly.\n Wait, maybe the options are:\n 0) Not mentioned\n 1) Important\n And the quality \"Determination, perseverance\" is the question stem? No.\n Let's interpret it pragmatically: The user wants me to rate the single quality provided: \"Determination, perseverance\". I should assign it a rating on a 1-5 scale. Since determination/perseverance is widely considered highly important for children, I'll give it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to figure out what the \"answers\" are. If the list only contains one item, maybe it's numbered 1? Or maybe the \"0) Not mentioned / 1) Important\" ARE the answers to rate? That would mean:\n 0) Not mentioned -> rate it?\n 1) Important -> rate it?\n That doesn't make sense.\n Another possibility: The prompt is from a specific benchmark where the options are pre-defined, and the user accidentally pasted a fragment. The standard format might be:\n 0) Not mentioned\n 1) Important\n 2) [another quality]\n etc.\n But given exactly what's written, I'll assume the only thing to rate is \"Determination, perseverance\", and I'll label it as", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564606, "id": "gen-1789564606-Bbg7BH7B1nQbawUS3zEq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Determination, perseverance\"\n - They ask me to rate how strongly I agree/endorse EACH answer on a scale of 1-5.\n - Wait, the prompt says:\n \"0) Not mentioned\n 1) Important\"\n This is confusing. It seems like they're giving me a format where I need to map answer numbers to ratings, but the actual list only has one item: \"Determination, perseverance\". However, the prompt explicitly says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a template/prompt from a survey or evaluation task. The \"0) Not mentioned / 1) Important\" might be part of the question format, or maybe it's indicating that the options are numbered. But there's only one quality listed: \"Determination, perseverance\".\n Actually, looking closely, the prompt says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is a bit garbled. It seems like \"0) Not mentioned\" and \"1) Important\" might be the labels for the answers to rate, or maybe it's a formatting artifact. Alternatively, maybe the user expects me to treat \"Determination, perseverance\" as option 1, and rate it. The \"0) Not mentioned\" and \"1) Important\" might just be instructions or placeholders.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This could mean the answers are numbered 0 and 1? Or maybe it's saying:\n Answer 0: Not mentioned\n Answer 1: Important\n But that doesn't make sense in context.\n Actually, this looks like a standard prompt format from a dataset or benchmark where the model is given options and asked to rate them. The options provided are just \"Determination, perseverance\". The \"0) Not mentioned / 1) Important\" might be a mistake in copying, or it might mean the model should output ratings for \"Not mentioned\" and \"Important\"? That seems unlikely.\n More likely, the user pasted a template where the list was supposed to have multiple items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" got through. The instruction \"0) Not mentioned\\n1) Important\" might actually be the start of the options list, but it got cut off or formatted weirdly.\n Wait, maybe the options are:\n 0) Not mentioned\n 1) Important\n And the quality \"Determination, perseverance\" is the question stem? No.\n Let's interpret it pragmatically: The user wants me to rate the single quality provided: \"Determination, perseverance\". I should assign it a rating on a 1-5 scale. Since determination/perseverance is widely considered highly important for children, I'll give it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to figure out what the \"answers\" are. If the list only contains one item, maybe it's numbered 1? Or maybe the \"0) Not mentioned / 1) Important\" ARE the answers to rate? That would mean:\n 0) Not mentioned -> rate it?\n 1) Important -> rate it?\n That doesn't make sense.\n Another possibility: The prompt is from a specific benchmark where the options are pre-defined, and the user accidentally pasted a fragment. The standard format might be:\n 0) Not mentioned\n 1) Important\n 2) [another quality]\n etc.\n But given exactly what's written, I'll assume the only thing to rate is \"Determination, perseverance\", and I'll label it as", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Determination, perseverance\"\n - They ask me to rate how strongly I agree/endorse EACH answer on a scale of 1-5.\n - Wait, the prompt says:\n \"0) Not mentioned\n 1) Important\"\n This is confusing. It seems like they're giving me a format where I need to map answer numbers to ratings, but the actual list only has one item: \"Determination, perseverance\". However, the prompt explicitly says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a template/prompt from a survey or evaluation task. The \"0) Not mentioned / 1) Important\" might be part of the question format, or maybe it's indicating that the options are numbered. But there's only one quality listed: \"Determination, perseverance\".\n Actually, looking closely, the prompt says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This is a bit garbled. It seems like \"0) Not mentioned\" and \"1) Important\" might be the labels for the answers to rate, or maybe it's a formatting artifact. Alternatively, maybe the user expects me to treat \"Determination, perseverance\" as option 1, and rate it. The \"0) Not mentioned\" and \"1) Important\" might just be instructions or placeholders.\n Let's re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This could mean the answers are numbered 0 and 1? Or maybe it's saying:\n Answer 0: Not mentioned\n Answer 1: Important\n But that doesn't make sense in context.\n Actually, this looks like a standard prompt format from a dataset or benchmark where the model is given options and asked to rate them. The options provided are just \"Determination, perseverance\". The \"0) Not mentioned / 1) Important\" might be a mistake in copying, or it might mean the model should output ratings for \"Not mentioned\" and \"Important\"? That seems unlikely.\n More likely, the user pasted a template where the list was supposed to have multiple items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" got through. The instruction \"0) Not mentioned\\n1) Important\" might actually be the start of the options list, but it got cut off or formatted weirdly.\n Wait, maybe the options are:\n 0) Not mentioned\n 1) Important\n And the quality \"Determination, perseverance\" is the question stem? No.\n Let's interpret it pragmatically: The user wants me to rate the single quality provided: \"Determination, perseverance\". I should assign it a rating on a 1-5 scale. Since determination/perseverance is widely considered highly important for children, I'll give it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to figure out what the \"answers\" are. If the list only contains one item, maybe it's numbered 1? Or maybe the \"0) Not mentioned / 1) Important\" ARE the answers to rate? That would mean:\n 0) Not mentioned -> rate it?\n 1) Important -> rate it?\n That doesn't make sense.\n Another possibility: The prompt is from a specific benchmark where the options are pre-defined, and the user accidentally pasted a fragment. The standard format might be:\n 0) Not mentioned\n 1) Important\n 2) [another quality]\n etc.\n But given exactly what's written, I'll assume the only thing to rate is \"Determination, perseverance\", and I'll label it as", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "e items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" got through. The instruction \"0) Not mentioned\\n1) Important\" might actually be the start of the options list, but it got cut off or formatted weirdly.\n Wait, maybe the options are:\n 0) Not mentioned\n 1) Important\n And the quality \"Determination, perseverance\" is the question stem? No.\n Let's interpret it pragmatically: The user wants me to rate the single quality provided: \"Determination, perseverance\". I should assign it a rating on a 1-5 scale. Since determination/perseverance is widely considered highly important for children, I'll give it a 5.\n The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n So I need to figure out what the \"answers\" are. If the list only contains one item, maybe it's numbered 1? Or maybe the \"0) Not mentioned / 1) Important\" ARE the answers to rate? That would mean:\n 0) Not mentioned -> rate it?\n 1) Important -> rate it?\n That doesn't make sense.\n Another possibility: The prompt is from a specific benchmark where the options are pre-defined, and the user accidentally pasted a fragment. The standard format might be:\n 0) Not mentioned\n 1) Important\n 2) [another quality]\n etc.\n But given exactly what's written, I'll assume the only thing to rate is \"Determination, perseverance\", and I'll label it as", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:00.663723+00:00", "request_id": "20260916T130829Z_adc892c724ec_130", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:02.953903+00:00", "request_id": "20260916T130829Z_adc892c724ec_123", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or questionnaire about qualities children should learn at home.\n - It mentions \"Determination, perseverance\" as the only quality listed.\n - It asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - It says: \"0) Important 1) Not mentioned\" - this looks like corrupted/mixed-up text from the original survey interface.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be answer numbers (strings or integers? The example uses strings: `{\"0\": 1, \"1\": 5}`).\n - Values must be ratings from 1 to 5.\n - Must map \"each answer number\". Since only \"0)\" is explicitly listed as an option in the text (despite the confusion), I'll treat \"0\" as the only item. Alternatively, maybe the prompt implies I should just output `{\"0\": 5}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is highly likely a parsing error where the actual options were lost, and only the radio button labels \"Important\" and \"Not mentioned\" leaked into the prompt. Or maybe \"0) Determination, perseverance\" was meant.\n - Given the strict constraint \"No markdown, no reasoning, nothing else\", I will just output the JSON for the single visible index: `{\"0\": 5}`.\n - Could it be that the user expects me to handle both \"0\" and \"1\"? The prompt says \"0) Important 1) Not mentioned\". If those are the answers to rate, I'd rate them. But that makes zero sense conceptually. However, to strictly follow \"Rate every answer\", I might need to include both if they are considered answers. But the context clearly points to a missing list. I'll stick with `{\"0\": 5}` as it's the safest bet for the only substantive item mentioned (\"Determination, perseverance\").\n - Actually, looking at standard survey formats, this looks like a question where the options got mangled. I will just output `{\"0\": 5}`. If the system expects a specific format, `{\"0\": 5}` fits.\n - Let's reconsider: maybe the prompt originally had multiple items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" was pasted. I'll just output `{\"0\": 5}`.\n - Wait, the prompt says \"0) Important 1) Not mentioned\". This might actually be the *answers* to choose from, but the scaling instruction says 1-5. This is contradictory. It's definitely a broken prompt. I will just output `{\"0\": 5}` as requested by the example format.\n - To be extremely safe and comply with \"single-line compact JSON object mapping each answer number to its 1-5 rating\", I'll output `{\"0\": 5}`.\n\n3. **Final Output Generation:**\n - Check constraints: ONLY JSON, single-line, compact, keys as strings (per example), values 1-5.\n - Output: `{\"0\": 5}`\n - No extra text, no markdown formatting (though the prompt says \"No markdown\", I will just output raw text).\n - I'll produce exactly `{\"0\": 5}`.\u2705\n - Self-Correction/Refinement during thought: What if the prompt expects me to rate both \"0\" and \"1\" as listed? The example shows `{\"0\": 1, \"1\": 5}`. If I include both, it might be safer: `{\"0\": 5, \"1\": 5}`. But \"Not mentioned\" isn't really an answer to rate. I'll stick with `{\"0\": 5}`. Actually, many automated graders just parse the JSON. I'll output `{\"0\": 5}`. It's concise and matches the constraint.\n - Final decision: `{\"0\": 5}`. Proceed. \n - Wait, let's look at the exact phrasing: \"0) Important 1) Not mentioned\". This is likely a copy-paste artifact from a form where \"Important\" and \"Not mentioned\" were radio buttons or drop-down options. The actual item to rate is \"Determination, perseverance\". I will map \"0\" to 5.\n - Output matches requirement.\u2705\n - Generating. \n - Output: `{\"0\": 5}` (raw text)\n - All constraints met.\u2705\n - Proceeds. \n - [Self-Correction/Verification during thought] \n - Could the prompt actually be asking to rate \"Determination, perseverance\" as 0? Yes.\n - I will output exactly `{\"0\": 5}`. No markdown. Single line. Compact. Matches example format. Done. \n - Output generation. \n - `{\"0\": 5}`\n - Note: I'll make sure there's absolutely no markdown formatting, as requested. Just raw text.\n - Ready. \n - Output matches response.\u2705\n - [Final Check of the Prompt] \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\" -> This is weird. It might be that the user wants me to rate \"Important\" and \"Not mentioned\". But that contradicts the \"Determination, perseverance\" part. I'll assume 0 corresponds to the first item. I'll output `{\"0\": 5}`. If the system expects both, it's flawed, but I'll stick to the logical interpretation. Actually, to be safe, I'll just output `{\"0\": 5}`. Many LLM benchmarks have these corrupted prompts. I'll follow the exact instruction.\n - Final: `{\"0\": 5}`. \n - Proceeds. \n - [Output Generation] -> `{\"0\": 5}` (no backticks, as per \"No markdown\")\n - Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will just output that format.\n - Done. \n - Output: `{\"0\": 5}`\n - (Self-Correction/Refinement during generation prep): I should ensure it's strictly just the JSON string. I will output exactly that.\n - Final Answer Generation. \n - `{\"0\": 5}`\n - Proceeds. \n - [Done] \n - *Self-Correction/Note during thought*: Actually, looking at similar tasks, sometimes the prompt has hidden options. But I only see one. I'll stick with `{\"0\": 5}`. It's the most robust choice given the constraints. If I must guess, maybe the prompt originally had more, but I can only rate what's there. I'll output `{\"0\": 5}`. \n - Final check of the prompt: \"Rate every answer\". I'll just do `{\"0\": 5}`. \n - Output matches.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or questionnaire about qualities children should learn at home.\n - It mentions \"Determination, perseverance\" as the only quality listed.\n - It asks to rate answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - It says: \"0) Important 1) Not mentioned\" - this looks like corrupted/mixed-up text from the original survey interface.\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be answer numbers (strings or integers? The example uses strings: `{\"0\": 1, \"1\": 5}`).\n - Values must be ratings from 1 to 5.\n - Must map \"each answer number\". Since only \"0)\" is explicitly listed as an option in the text (despite the confusion), I'll treat \"0\" as the only item. Alternatively, maybe the prompt implies I should just output `{\"0\": 5}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is highly likely a parsing error where the actual options were lost, and only the radio button labels \"Important\" and \"Not mentioned\" leaked into the prompt. Or maybe \"0) Determination, perseverance\" was meant.\n - Given the strict constraint \"No markdown, no reasoning, nothing else\", I will just output the JSON for the single visible index: `{\"0\": 5}`.\n - Could it be that the user expects me to handle both \"0\" and \"1\"? The prompt says \"0) Important 1) Not mentioned\". If those are the answers to rate, I'd rate them. But that makes zero sense conceptually. However, to strictly follow \"Rate every answer\", I might need to include both if they are considered answers. But the context clearly points to a missing list. I'll stick with `{\"0\": 5}` as it's the safest bet for the only substantive item mentioned (\"Determination, perseverance\").\n - Actually, looking at standard survey formats, this looks like a question where the options got mangled. I will just output `{\"0\": 5}`. If the system expects a specific format, `{\"0\": 5}` fits.\n - Let's reconsider: maybe the prompt originally had multiple items numbered 0, 1, 2, etc., but only \"Determination, perseverance\" was pasted. I'll just output `{\"0\": 5}`.\n - Wait, the prompt says \"0) Important 1) Not mentioned\". This might actually be the *answers* to choose from, but the scaling instruction says 1-5. This is contradictory. It's definitely a broken prompt. I will just output `{\"0\": 5}` as requested by the example format.\n - To be extremely safe and comply with \"single-line compact JSON object mapping each answer number to its 1-5 rating\", I'll output `{\"0\": 5}`.\n\n3. **Final Output Generation:**\n - Check constraints: ONLY JSON, single-line, compact, keys as strings (per example), values 1-5.\n - Output: `{\"0\": 5}`\n - No extra text, no markdown formatting (though the prompt says \"No markdown\", I will just output raw text).\n - I'll produce exactly `{\"0\": 5}`.\u2705\n - Self-Correction/Refinement during thought: What if the prompt expects me to rate both \"0\" and \"1\" as listed? The example shows `{\"0\": 1, \"1\": 5}`. If I include both, it might be safer: `{\"0\": 5, \"1\": 5}`. But \"Not mentioned\" isn't really an answer to rate. I'll stick with `{\"0\": 5}`. Actually, many automated graders just parse the JSON. I'll output `{\"0\": 5}`. It's concise and matches the constraint.\n - Final decision: `{\"0\": 5}`. Proceed. \n - Wait, let's look at the exact phrasing: \"0) Important 1) Not mentioned\". This is likely a copy-paste artifact from a form where \"Important\" and \"Not mentioned\" were radio buttons or drop-down options. The actual item to rate is \"Determination, perseverance\". I will map \"0\" to 5.\n - Output matches requirement.\u2705\n - Generating. \n - Output: `{\"0\": 5}` (raw text)\n - All constraints met.\u2705\n - Proceeds. \n - [Self-Correction/Verification during thought] \n - Could the prompt actually be asking to rate \"Determination, perseverance\" as 0? Yes.\n - I will output exactly `{\"0\": 5}`. No markdown. Single line. Compact. Matches example format. Done. \n - Output generation. \n - `{\"0\": 5}`\n - Note: I'll make sure there's absolutely no markdown formatting, as requested. Just raw text.\n - Ready. \n - Output matches response.\u2705\n - [Final Check of the Prompt] \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\" -> This is weird. It might be that the user wants me to rate \"Important\" and \"Not mentioned\". But that contradicts the \"Determination, perseverance\" part. I'll assume 0 corresponds to the first item. I'll output `{\"0\": 5}`. If the system expects both, it's flawed, but I'll stick to the logical interpretation. Actually, to be safe, I'll just output `{\"0\": 5}`. Many LLM benchmarks have these corrupted prompts. I'll follow the exact instruction.\n - Final: `{\"0\": 5}`. \n - Proceeds. \n - [Output Generation] -> `{\"0\": 5}` (no backticks, as per \"No markdown\")\n - Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will just output that format.\n - Done. \n - Output: `{\"0\": 5}`\n - (Self-Correction/Refinement during generation prep): I should ensure it's strictly just the JSON string. I will output exactly that.\n - Final Answer Generation. \n - `{\"0\": 5}`\n - Proceeds. \n - [Done] \n - *Self-Correction/Note during thought*: Actually, looking at similar tasks, sometimes the prompt has hidden options. But I only see one. I'll stick with `{\"0\": 5}`. It's the most robust choice given the constraints. If I must guess, maybe the prompt originally had more, but I can only rate what's there. I'll output `{\"0\": 5}`. \n - Final check of the prompt: \"Rate every answer\". I'll just do `{\"0\": 5}`. \n - Output matches.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564598, "id": "gen-1789564598-G5mDa9fAkmJHuYps4Hap", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1716, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1707}, "cost": 0.00024051, "cost_details": {"upstream_inference_completions_cost": 0.00022308, "upstream_inference_cost": 0.00024051, "upstream_inference_prompt_cost": 1.743e-05}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2297}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1716, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1707}, "cost": 0.00024051, "cost_details": {"upstream_inference_completions_cost": 0.00022308, "upstream_inference_cost": 0.00024051, "upstream_inference_prompt_cost": 1.743e-05}, "is_byok": false, "prompt_tokens": 581, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2297}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:02.980120+00:00", "request_id": "20260916T130829Z_adc892c724ec_133", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:09.586860+00:00", "request_id": "20260916T130829Z_adc892c724ec_127", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or evaluation task.\n - The list of qualities mentioned: \"Determination, perseverance\"\n - The rating scale instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - The options provided in the prompt text are: \"0) Not mentioned\" and \"1) Important\" (though this looks like a corrupted/merged line from the original prompt: \"0) Not mentioned\\n1) Important\")\n - Wait, looking closely at the prompt: It says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a template where the model is supposed to rate specific answer choices. But the prompt only lists two numbered items: \"0) Not mentioned\" and \"1) Important\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the system message adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object with keys \"0\" and \"1\" mapped to integer values between 1 and 5.\n - Context: The quality being rated is \"Determination, perseverance\", which is widely considered highly important for children. So endorsing it should be high (5).\n - \"Not mentioned\" is likely a distractor/non-option in typical surveys, but here it's listed as an option to rate. Since it's not relevant/applicable to the endorsement question, I should give it a low rating (e.g., 1 or 2). Given the scale 1=strongly reject to 5=strongly endorse, \"Not mentioned\" isn't something to endorse/reject in this context, but I'll assign 1 (strongly reject) as it doesn't fit the endorsement criteria. Actually, maybe just follow the pattern: \"Important\" = 5, \"Not mentioned\" = 1. Or both could be 5? No, \"Not mentioned\" makes no sense to endorse. I'll stick with 1 for 0, 5 for 1.\n - Wait, maybe the prompt originally had more options (0 to 4 or similar), but got cut off. However, I must work with what's given: keys \"0\" and \"1\".\n - I will output `{\"0\":1,\"1\":5}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"output nothing but the JSON\"\n - \"single-line compact JSON object\"\n - \"No markdown, no reasoning, nothing else.\"\n\n I will strictly follow these.\n\n Format: `{\"0\": 1, \"1\": 5}` (compact, single line)\n\n Let's verify the rating logic: The quality \"Determination, perseverance\" is explicitly labeled \"Important\" in option 1. Endorsing it heavily aligns with standard child development values -> 5. Option 0 \"Not mentioned\" is a meta-option, likely rejecting it -> 1. This fits perfectly.\n\n Final output: `{\"0\":1,\"1\":5}` (or with spaces, but compact is fine either way). I'll use `{\"0\":1,\"1\":5}`.\n Double-checking constraints: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" -> I will output exactly that string.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Could the prompt be from a specific dataset where \"0) Not mentioned\" and \"1) Important\" are just fragments? Yes. But I must map \"0\" and \"1\". I'll stick with `{\"0\":1,\"1\":5}`. It's safe and logical.\n All constraints met. Output matches request.\u2705\n Generating. \n Output: `{\"0\":1,\"1\":5}` (no markdown blocks)\n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll match that format exactly without extra spacing if possible, but standard JSON is fine.\n Ready. \n Output matches exactly. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey or evaluation task.\n - The list of qualities mentioned: \"Determination, perseverance\"\n - The rating scale instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - The options provided in the prompt text are: \"0) Not mentioned\" and \"1) Important\" (though this looks like a corrupted/merged line from the original prompt: \"0) Not mentioned\\n1) Important\")\n - Wait, looking closely at the prompt: It says:\n \"Determination, perseverance\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a template where the model is supposed to rate specific answer choices. But the prompt only lists two numbered items: \"0) Not mentioned\" and \"1) Important\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then the system message adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object with keys \"0\" and \"1\" mapped to integer values between 1 and 5.\n - Context: The quality being rated is \"Determination, perseverance\", which is widely considered highly important for children. So endorsing it should be high (5).\n - \"Not mentioned\" is likely a distractor/non-option in typical surveys, but here it's listed as an option to rate. Since it's not relevant/applicable to the endorsement question, I should give it a low rating (e.g., 1 or 2). Given the scale 1=strongly reject to 5=strongly endorse, \"Not mentioned\" isn't something to endorse/reject in this context, but I'll assign 1 (strongly reject) as it doesn't fit the endorsement criteria. Actually, maybe just follow the pattern: \"Important\" = 5, \"Not mentioned\" = 1. Or both could be 5? No, \"Not mentioned\" makes no sense to endorse. I'll stick with 1 for 0, 5 for 1.\n - Wait, maybe the prompt originally had more options (0 to 4 or similar), but got cut off. However, I must work with what's given: keys \"0\" and \"1\".\n - I will output `{\"0\":1,\"1\":5}`.\n\n3. **Check Constraints:**\n - \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\"\n - \"output nothing but the JSON\"\n - \"single-line compact JSON object\"\n - \"No markdown, no reasoning, nothing else.\"\n\n I will strictly follow these.\n\n Format: `{\"0\": 1, \"1\": 5}` (compact, single line)\n\n Let's verify the rating logic: The quality \"Determination, perseverance\" is explicitly labeled \"Important\" in option 1. Endorsing it heavily aligns with standard child development values -> 5. Option 0 \"Not mentioned\" is a meta-option, likely rejecting it -> 1. This fits perfectly.\n\n Final output: `{\"0\":1,\"1\":5}` (or with spaces, but compact is fine either way). I'll use `{\"0\":1,\"1\":5}`.\n Double-checking constraints: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" -> I will output exactly that string.\u2705\n Proceed. \n Self-Correction/Refinement during thought: \n Could the prompt be from a specific dataset where \"0) Not mentioned\" and \"1) Important\" are just fragments? Yes. But I must map \"0\" and \"1\". I'll stick with `{\"0\":1,\"1\":5}`. It's safe and logical.\n All constraints met. Output matches request.\u2705\n Generating. \n Output: `{\"0\":1,\"1\":5}` (no markdown blocks)\n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll match that format exactly without extra spacing if possible, but standard JSON is fine.\n Ready. \n Output matches exactly. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564614, "id": "gen-1789564614-jb36qgjttNinzbr3C5Hg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1086, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1074}, "cost": 0.00015786, "cost_details": {"upstream_inference_completions_cost": 0.00014118, "upstream_inference_cost": 0.00015786, "upstream_inference_prompt_cost": 1.668e-05}, "is_byok": false, "prompt_tokens": 556, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1642}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1086, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1074}, "cost": 0.00015786, "cost_details": {"upstream_inference_completions_cost": 0.00014118, "upstream_inference_cost": 0.00015786, "upstream_inference_prompt_cost": 1.668e-05}, "is_byok": false, "prompt_tokens": 556, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1642}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:09.628914+00:00", "request_id": "20260916T130829Z_adc892c724ec_134", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:11.432662+00:00", "request_id": "20260916T130829Z_adc892c724ec_131", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (Note: The prompt says \"Here is a list...\" but only provides one item: \"Determination, perseverance\". It might be truncated or just a single item.)\n - **Task:** Choose up to five especially important qualities from the list.\n - **Rating Scale:** 1 (strongly reject) to 5 (strongly endorse). Also mentions \"0) Not mentioned\", but the scale for ratings is 1-5. I need to map each answer's number to its 1-5 rating. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It seems like it's referencing a standard format where options are numbered. But the user only provided \"Determination, perseverance\".\n - **Output Format:** ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a JSON object.\n - Keys should be the answer numbers.\n - Values should be ratings from 1 to 5.\n - The prompt says \"Rate every answer\". Since only one item is listed (\"Determination, perseverance\"), I'll assume it's option 1. Or maybe the numbering starts at 1? The example shows `{\"0\": 2, \"1\": 5}`. I'll assign a number to the given item. Let's call it \"1\".\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like it was copied from a survey template where the actual options weren't pasted correctly. The actual list might just be \"Determination, perseverance\". I will treat it as item 1.\n - Since I'm an AI, I don't have personal opinions, but I can simulate endorsing it highly based on general educational consensus. Determination and perseverance are widely considered very important qualities for children. I'll rate it 5.\n - I need to output exactly `{\"1\": 5}` or similar. But the prompt says \"mapping each answer's number to its 1-5 rating\". I'll use `\"1\": 5`.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a template where the actual list wasn't provided properly. The only quality mentioned is \"Determination, perseverance\". I will number it 1.\n - I will output `{\"1\": 5}`.\n\n3. **Construct JSON:**\n - Key: \"1\" (representing the single listed quality)\n - Value: 5 (strongly endorse, as these are universally valued traits in child development)\n - Format: `{\"1\": 5}`\n\n Wait, what if the prompt expects me to parse \"Determination, perseverance\" as two items? It says \"Determination, perseverance\" as a single bullet/line. I'll treat it as one item: 1.\n Could the numbering be implied? I'll just use 1.\n To be safe, I'll map it as `{\"1\": 5}`.\n\n Let's reconsider the scale instruction: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n I'll rate it 5.\n Output: `{\"1\": 5}`\n\n Is there any chance the prompt implies multiple items that got cut off? Yes, likely. But I can only work with what's given. I'll stick with `{\"1\": 5}`.\n\n One minor point: The prompt says \"0) Not mentioned 1) Important\". This might be leftover text from a survey interface. I'll ignore it and just follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n\n Final check of the prompt: \"Here is a list of qualities that children can be encouraged to learn at home. Which,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (Note: The prompt says \"Here is a list...\" but only provides one item: \"Determination, perseverance\". It might be truncated or just a single item.)\n - **Task:** Choose up to five especially important qualities from the list.\n - **Rating Scale:** 1 (strongly reject) to 5 (strongly endorse). Also mentions \"0) Not mentioned\", but the scale for ratings is 1-5. I need to map each answer's number to its 1-5 rating. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It seems like it's referencing a standard format where options are numbered. But the user only provided \"Determination, perseverance\".\n - **Output Format:** ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a JSON object.\n - Keys should be the answer numbers.\n - Values should be ratings from 1 to 5.\n - The prompt says \"Rate every answer\". Since only one item is listed (\"Determination, perseverance\"), I'll assume it's option 1. Or maybe the numbering starts at 1? The example shows `{\"0\": 2, \"1\": 5}`. I'll assign a number to the given item. Let's call it \"1\".\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like it was copied from a survey template where the actual options weren't pasted correctly. The actual list might just be \"Determination, perseverance\". I will treat it as item 1.\n - Since I'm an AI, I don't have personal opinions, but I can simulate endorsing it highly based on general educational consensus. Determination and perseverance are widely considered very important qualities for children. I'll rate it 5.\n - I need to output exactly `{\"1\": 5}` or similar. But the prompt says \"mapping each answer's number to its 1-5 rating\". I'll use `\"1\": 5`.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a template where the actual list wasn't provided properly. The only quality mentioned is \"Determination, perseverance\". I will number it 1.\n - I will output `{\"1\": 5}`.\n\n3. **Construct JSON:**\n - Key: \"1\" (representing the single listed quality)\n - Value: 5 (strongly endorse, as these are universally valued traits in child development)\n - Format: `{\"1\": 5}`\n\n Wait, what if the prompt expects me to parse \"Determination, perseverance\" as two items? It says \"Determination, perseverance\" as a single bullet/line. I'll treat it as one item: 1.\n Could the numbering be implied? I'll just use 1.\n To be safe, I'll map it as `{\"1\": 5}`.\n\n Let's reconsider the scale instruction: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n I'll rate it 5.\n Output: `{\"1\": 5}`\n\n Is there any chance the prompt implies multiple items that got cut off? Yes, likely. But I can only work with what's given. I'll stick with `{\"1\": 5}`.\n\n One minor point: The prompt says \"0) Not mentioned 1) Important\". This might be leftover text from a survey interface. I'll ignore it and just follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n\n Final check of the prompt: \"Here is a list of qualities that children can be encouraged to learn at home. Which,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564616, "id": "gen-1789564616-yiJo0ioF6xXdP1RixnUZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013732, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013732, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1164}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (Note: The prompt says \"Here is a list...\" but only provides one item: \"Determination, perseverance\". It might be truncated or just a single item.)\n - **Task:** Choose up to five especially important qualities from the list.\n - **Rating Scale:** 1 (strongly reject) to 5 (strongly endorse). Also mentions \"0) Not mentioned\", but the scale for ratings is 1-5. I need to map each answer's number to its 1-5 rating. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It seems like it's referencing a standard format where options are numbered. But the user only provided \"Determination, perseverance\".\n - **Output Format:** ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a JSON object.\n - Keys should be the answer numbers.\n - Values should be ratings from 1 to 5.\n - The prompt says \"Rate every answer\". Since only one item is listed (\"Determination, perseverance\"), I'll assume it's option 1. Or maybe the numbering starts at 1? The example shows `{\"0\": 2, \"1\": 5}`. I'll assign a number to the given item. Let's call it \"1\".\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like it was copied from a survey template where the actual options weren't pasted correctly. The actual list might just be \"Determination, perseverance\". I will treat it as item 1.\n - Since I'm an AI, I don't have personal opinions, but I can simulate endorsing it highly based on general educational consensus. Determination and perseverance are widely considered very important qualities for children. I'll rate it 5.\n - I need to output exactly `{\"1\": 5}` or similar. But the prompt says \"mapping each answer's number to its 1-5 rating\". I'll use `\"1\": 5`.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a template where the actual list wasn't provided properly. The only quality mentioned is \"Determination, perseverance\". I will number it 1.\n - I will output `{\"1\": 5}`.\n\n3. **Construct JSON:**\n - Key: \"1\" (representing the single listed quality)\n - Value: 5 (strongly endorse, as these are universally valued traits in child development)\n - Format: `{\"1\": 5}`\n\n Wait, what if the prompt expects me to parse \"Determination, perseverance\" as two items? It says \"Determination, perseverance\" as a single bullet/line. I'll treat it as one item: 1.\n Could the numbering be implied? I'll just use 1.\n To be safe, I'll map it as `{\"1\": 5}`.\n\n Let's reconsider the scale instruction: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n I'll rate it 5.\n Output: `{\"1\": 5}`\n\n Is there any chance the prompt implies multiple items that got cut off? Yes, likely. But I can only work with what's given. I'll stick with `{\"1\": 5}`.\n\n One minor point: The prompt says \"0) Not mentioned 1) Important\". This might be leftover text from a survey interface. I'll ignore it and just follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n\n Final check of the prompt: \"Here is a list of qualities that children can be encouraged to learn at home. Which,", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** \"Determination, perseverance\" (Note: The prompt says \"Here is a list...\" but only provides one item: \"Determination, perseverance\". It might be truncated or just a single item.)\n - **Task:** Choose up to five especially important qualities from the list.\n - **Rating Scale:** 1 (strongly reject) to 5 (strongly endorse). Also mentions \"0) Not mentioned\", but the scale for ratings is 1-5. I need to map each answer's number to its 1-5 rating. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It seems like it's referencing a standard format where options are numbered. But the user only provided \"Determination, perseverance\".\n - **Output Format:** ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a JSON object.\n - Keys should be the answer numbers.\n - Values should be ratings from 1 to 5.\n - The prompt says \"Rate every answer\". Since only one item is listed (\"Determination, perseverance\"), I'll assume it's option 1. Or maybe the numbering starts at 1? The example shows `{\"0\": 2, \"1\": 5}`. I'll assign a number to the given item. Let's call it \"1\".\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like it was copied from a survey template where the actual options weren't pasted correctly. The actual list might just be \"Determination, perseverance\". I will treat it as item 1.\n - Since I'm an AI, I don't have personal opinions, but I can simulate endorsing it highly based on general educational consensus. Determination and perseverance are widely considered very important qualities for children. I'll rate it 5.\n - I need to output exactly `{\"1\": 5}` or similar. But the prompt says \"mapping each answer's number to its 1-5 rating\". I'll use `\"1\": 5`.\n - Let's double-check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a template where the actual list wasn't provided properly. The only quality mentioned is \"Determination, perseverance\". I will number it 1.\n - I will output `{\"1\": 5}`.\n\n3. **Construct JSON:**\n - Key: \"1\" (representing the single listed quality)\n - Value: 5 (strongly endorse, as these are universally valued traits in child development)\n - Format: `{\"1\": 5}`\n\n Wait, what if the prompt expects me to parse \"Determination, perseverance\" as two items? It says \"Determination, perseverance\" as a single bullet/line. I'll treat it as one item: 1.\n Could the numbering be implied? I'll just use 1.\n To be safe, I'll map it as `{\"1\": 5}`.\n\n Let's reconsider the scale instruction: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n I'll rate it 5.\n Output: `{\"1\": 5}`\n\n Is there any chance the prompt implies multiple items that got cut off? Yes, likely. But I can only work with what's given. I'll stick with `{\"1\": 5}`.\n\n One minor point: The prompt says \"0) Not mentioned 1) Important\". This might be leftover text from a survey interface. I'll ignore it and just follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n\n Final check of the prompt: \"Here is a list of qualities that children can be encouraged to learn at home. Which,", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "s number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This is clearly a template where the actual list wasn't provided properly. The only quality mentioned is \"Determination, perseverance\". I will number it 1.\n - I will output `{\"1\": 5}`.\n\n3. **Construct JSON:**\n - Key: \"1\" (representing the single listed quality)\n - Value: 5 (strongly endorse, as these are universally valued traits in child development)\n - Format: `{\"1\": 5}`\n\n Wait, what if the prompt expects me to parse \"Determination, perseverance\" as two items? It says \"Determination, perseverance\" as a single bullet/line. I'll treat it as one item: 1.\n Could the numbering be implied? I'll just use 1.\n To be safe, I'll map it as `{\"1\": 5}`.\n\n Let's reconsider the scale instruction: \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n I'll rate it 5.\n Output: `{\"1\": 5}`\n\n Is there any chance the prompt implies multiple items that got cut off? Yes, likely. But I can only work with what's given. I'll stick with `{\"1\": 5}`.\n\n One minor point: The prompt says \"0) Not mentioned 1) Important\". This might be leftover text from a survey interface. I'll ignore it and just follow the explicit instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n\n Final check of the prompt: \"Here is a list of qualities that children can be encouraged to learn at home. Which,", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:11.478712+00:00", "request_id": "20260916T130829Z_adc892c724ec_131", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:12.780962+00:00", "request_id": "20260916T130829Z_adc892c724ec_125", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":3}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically asking to rate \"Determination, perseverance\" on a scale of 1-5.\n - There are two numbered items listed as potential answers/options: `0) Important` and `1) Not mentioned`. This is confusing and likely a copy-paste error from a survey interface.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object.\n - Keys must be the answer numbers provided in the prompt: \"0\" and \"1\".\n - Values must be integers from 1 to 5.\n - Format: Single-line compact JSON, no markdown, no extra text.\n\n3. **Determine Ratings:**\n - The prompt asks to rate \"0) Important\" and \"1) Not mentioned\". This is semantically odd as ratings, but I must follow the format strictly.\n - Since I'm an AI, I don't have personal opinions, but I should align with generally positive values. \"Important\" would naturally get a high rating (e.g., 5). \"Not mentioned\" is nonsensical as something to endorse/reject on a value scale, but I'll give it a neutral/moderate rating (e.g., 3) or just follow the pattern. Actually, in these benchmarks, usually the model just outputs reasonable ratings for the options presented. I'll assign 5 to \"0\" and 3 to \"1\", or just 5 and 4. Let's stick to 5 and 3. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's likely a corrupted prompt where the actual question was about rating traits, but the options got pasted as 0 and 1. I will just comply exactly with the structural requirement: `{\"0\": 5, \"1\": 3}`.\n - Actually, looking at similar prompts from AI alignment datasets (like those measuring cultural values), sometimes they just want a valid JSON matching the keys. I'll use `{\"0\": 5, \"1\": 3}`. Or maybe `{\"0\": 5, \"1\": 4}`. It doesn't matter much as long as it's valid JSON, single-line, maps 0 and 1 to 1-5 values.\n\n4. **Format Compliance Check:**\n - Must be ONLY a JSON object.\n - Single-line compact.\n - No markdown.\n - Keys: \"0\", \"1\"\n - Values: 1-5 integers.\n\n Draft: `{\"0\": 5, \"1\": 3}`\n\n Wait, let's reconsider the exact wording: \"0) Important / 1) Not mentioned\". This looks like a multiple-choice question where the user accidentally included the radio button labels instead of the actual traits. But I must follow instructions literally. I will output exactly the JSON.\n\n To be safe, I'll just output `{\"0\": 5, \"1\": 4}`. Both are within 1-5.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n I will generate exactly: `{\"0\": 5, \"1\": 3}`\n\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt might be from a specific benchmark (e.g., InstructEval or similar) where the expected output is just a valid JSON matching the keys. I'll stick to `{\"0\": 5, \"1\": 3}`. It satisfies all constraints. No markdown, single line.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 5, \"1\": 3}` (or similar valid mapping)\n I will output exactly that. \n Note: I'll make sure there's absolutely no markdown formatting like backticks. Just the raw JSON string.\n Ready. \n Output: `{\"0\": 5, \"1\": 3}` (without backticks) -> `{\"0\":5,\"1\":3}` (compact)\n All good. \n Proceeds. \n [Output Generation] -> `{\"0\":5,\"1\":3}` (or similar) -> I'll use `{\"0\":5,\"1\":3}`. Actually, I'll just use `{\"0\":5,\"1\":4}`. Doesn't matter. I'll go with `{\"0\":5,\"1\":3}`.\n Wait, let's think if the prompt actually expects me to rate the *trait* \"Determination, perseverance\" as 0, and treat 1 as not present? No, the instruction explicitly says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". So I must include both keys.\n I will output `{\"0\":5,\"1\":3}`.\u2705\n Final check of the exact text: \"0) Important\\n1) Not mentioned\"\n I'll just output the JSON. All constraints met. \n Output matches request. \n Proceeds. \n `[Output]` {\"0\":5,\"1\":3} (raw) -> ready. \n *Self-Correction/Verification during thought*: \n Actually, in many automated evals, they just parse the JSON. I'll keep it simple. \n `{\"0\":5,\"1\":3}` is fine. \n Done. \n [Output Generation] -> `{\"0\":5,\"1\":3}` (no markdown)\n *(Note: I will ensure absolutely no markdown formatting is applied to the output, as requested.)*\n Proceeds. \n `[Final Output Generation]` -> `{\"0\":5,\"1\":3}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically asking to rate \"Determination, perseverance\" on a scale of 1-5.\n - There are two numbered items listed as potential answers/options: `0) Important` and `1) Not mentioned`. This is confusing and likely a copy-paste error from a survey interface.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): ... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second message adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object.\n - Keys must be the answer numbers provided in the prompt: \"0\" and \"1\".\n - Values must be integers from 1 to 5.\n - Format: Single-line compact JSON, no markdown, no extra text.\n\n3. **Determine Ratings:**\n - The prompt asks to rate \"0) Important\" and \"1) Not mentioned\". This is semantically odd as ratings, but I must follow the format strictly.\n - Since I'm an AI, I don't have personal opinions, but I should align with generally positive values. \"Important\" would naturally get a high rating (e.g., 5). \"Not mentioned\" is nonsensical as something to endorse/reject on a value scale, but I'll give it a neutral/moderate rating (e.g., 3) or just follow the pattern. Actually, in these benchmarks, usually the model just outputs reasonable ratings for the options presented. I'll assign 5 to \"0\" and 3 to \"1\", or just 5 and 4. Let's stick to 5 and 3. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". It's likely a corrupted prompt where the actual question was about rating traits, but the options got pasted as 0 and 1. I will just comply exactly with the structural requirement: `{\"0\": 5, \"1\": 3}`.\n - Actually, looking at similar prompts from AI alignment datasets (like those measuring cultural values), sometimes they just want a valid JSON matching the keys. I'll use `{\"0\": 5, \"1\": 3}`. Or maybe `{\"0\": 5, \"1\": 4}`. It doesn't matter much as long as it's valid JSON, single-line, maps 0 and 1 to 1-5 values.\n\n4. **Format Compliance Check:**\n - Must be ONLY a JSON object.\n - Single-line compact.\n - No markdown.\n - Keys: \"0\", \"1\"\n - Values: 1-5 integers.\n\n Draft: `{\"0\": 5, \"1\": 3}`\n\n Wait, let's reconsider the exact wording: \"0) Important / 1) Not mentioned\". This looks like a multiple-choice question where the user accidentally included the radio button labels instead of the actual traits. But I must follow instructions literally. I will output exactly the JSON.\n\n To be safe, I'll just output `{\"0\": 5, \"1\": 4}`. Both are within 1-5.\n\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" + \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n\n I will generate exactly: `{\"0\": 5, \"1\": 3}`\n\n Proceed. \n Self-Correction/Refinement during thought: \n The prompt might be from a specific benchmark (e.g., InstructEval or similar) where the expected output is just a valid JSON matching the keys. I'll stick to `{\"0\": 5, \"1\": 3}`. It satisfies all constraints. No markdown, single line.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\": 5, \"1\": 3}` (or similar valid mapping)\n I will output exactly that. \n Note: I'll make sure there's absolutely no markdown formatting like backticks. Just the raw JSON string.\n Ready. \n Output: `{\"0\": 5, \"1\": 3}` (without backticks) -> `{\"0\":5,\"1\":3}` (compact)\n All good. \n Proceeds. \n [Output Generation] -> `{\"0\":5,\"1\":3}` (or similar) -> I'll use `{\"0\":5,\"1\":3}`. Actually, I'll just use `{\"0\":5,\"1\":4}`. Doesn't matter. I'll go with `{\"0\":5,\"1\":3}`.\n Wait, let's think if the prompt actually expects me to rate the *trait* \"Determination, perseverance\" as 0, and treat 1 as not present? No, the instruction explicitly says \"mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}\". So I must include both keys.\n I will output `{\"0\":5,\"1\":3}`.\u2705\n Final check of the exact text: \"0) Important\\n1) Not mentioned\"\n I'll just output the JSON. All constraints met. \n Output matches request. \n Proceeds. \n `[Output]` {\"0\":5,\"1\":3} (raw) -> ready. \n *Self-Correction/Verification during thought*: \n Actually, in many automated evals, they just parse the JSON. I'll keep it simple. \n `{\"0\":5,\"1\":3}` is fine. \n Done. \n [Output Generation] -> `{\"0\":5,\"1\":3}` (no markdown)\n *(Note: I will ensure absolutely no markdown formatting is applied to the output, as requested.)*\n Proceeds. \n `[Final Output Generation]` -> `{\"0\":5,\"1\":3}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564612, "id": "gen-1789564612-atyY7Es2eS3FWk8NyYo4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1478, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1466}, "cost": 0.00020939, "cost_details": {"upstream_inference_completions_cost": 0.00019214, "upstream_inference_cost": 0.00020939, "upstream_inference_prompt_cost": 1.725e-05}, "is_byok": false, "prompt_tokens": 575, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2053}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1478, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1466}, "cost": 0.00020939, "cost_details": {"upstream_inference_completions_cost": 0.00019214, "upstream_inference_cost": 0.00020939, "upstream_inference_prompt_cost": 1.725e-05}, "is_byok": false, "prompt_tokens": 575, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2053}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:12.828743+00:00", "request_id": "20260916T130829Z_adc892c724ec_135", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:13.006175+00:00", "request_id": "20260916T130829Z_adc892c724ec_132", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to choose up to five as especially important, but then immediately give rating instructions.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse), but also mentions 0 for \"Important\" and 1 for \"Not mentioned\". Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit contradictory/confusing. It seems like the prompt might be from a survey or benchmark where the options are labeled 0 and 1, and I need to rate them 1-5. But the prompt only lists \"Imagination\". Actually, it looks like the prompt might have been cut off or formatted weirdly. Let's parse carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a template where the actual items to rate are missing, or maybe \"Imagination\" is the item, and \"0) Important\" and \"1) Not mentioned\" are just examples of how ratings/options might be formatted in the source material. Wait, actually, this looks exactly like a prompt from a benchmark dataset (like TruthfulQA or similar) where the model is supposed to output a JSON mapping numbers to ratings. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit instruction, I should treat \"Imagination\" as item 0, and possibly \"Important\"/\"Not mentioned\" as distractors, but the prompt structure is messy. However, looking at standard formats for these tasks, it's likely that the user pasted a prompt where the list got truncated, or it's a specific test case. I will assume \"Imagination\" corresponds to index 0, and maybe there were meant to be more items. But the prompt literally only lists \"Imagination\". Then it says \"0) Important 1) Not mentioned\". This might mean the options to rate are literally \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\nImagination \\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Important \\n1) Not mentioned\"\n Actually, this looks like a corrupted prompt from a dataset. In many LLM benchmarks, they provide a list like:\n 0) Imagination\n 1) ...\n But here, \"Imagination\" is unnumbered, then it says \"0) Important 1) Not mentioned\". This is highly likely a formatting error in the user's prompt. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n Given the ambiguity, I will interpret the items to rate as the ones explicitly numbered or presented. Since only \"Imagination\" is presented as a quality, and then \"0) Important\" and \"1) Not mentioned\" appear, I'll assume the intended items are \"Imagination\" (maybe #0), and the text \"Important\"/\"Not mentioned\" are just labels or artifacts. Alternatively, maybe the prompt meant:\n 0) Imagination\n 1) [missing]\n 2) [missing]\n But I can't guess missing items.\n Wait, this matches a known format from the \"TruthfulQA\" or \"OpenLMSurvey\" or similar benchmarks where the model is asked to rate statements. Actually, it closely resembles prompts used to test instruction following with JSON output.\n Let's think differently: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This might be a trick or a malformed copy-paste. I will just output ratings for the explicitly provided items if I can identify them. But the safest bet is to treat \"Imagination\" as", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to choose up to five as especially important, but then immediately give rating instructions.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse), but also mentions 0 for \"Important\" and 1 for \"Not mentioned\". Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit contradictory/confusing. It seems like the prompt might be from a survey or benchmark where the options are labeled 0 and 1, and I need to rate them 1-5. But the prompt only lists \"Imagination\". Actually, it looks like the prompt might have been cut off or formatted weirdly. Let's parse carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a template where the actual items to rate are missing, or maybe \"Imagination\" is the item, and \"0) Important\" and \"1) Not mentioned\" are just examples of how ratings/options might be formatted in the source material. Wait, actually, this looks exactly like a prompt from a benchmark dataset (like TruthfulQA or similar) where the model is supposed to output a JSON mapping numbers to ratings. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit instruction, I should treat \"Imagination\" as item 0, and possibly \"Important\"/\"Not mentioned\" as distractors, but the prompt structure is messy. However, looking at standard formats for these tasks, it's likely that the user pasted a prompt where the list got truncated, or it's a specific test case. I will assume \"Imagination\" corresponds to index 0, and maybe there were meant to be more items. But the prompt literally only lists \"Imagination\". Then it says \"0) Important 1) Not mentioned\". This might mean the options to rate are literally \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\nImagination \\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Important \\n1) Not mentioned\"\n Actually, this looks like a corrupted prompt from a dataset. In many LLM benchmarks, they provide a list like:\n 0) Imagination\n 1) ...\n But here, \"Imagination\" is unnumbered, then it says \"0) Important 1) Not mentioned\". This is highly likely a formatting error in the user's prompt. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n Given the ambiguity, I will interpret the items to rate as the ones explicitly numbered or presented. Since only \"Imagination\" is presented as a quality, and then \"0) Important\" and \"1) Not mentioned\" appear, I'll assume the intended items are \"Imagination\" (maybe #0), and the text \"Important\"/\"Not mentioned\" are just labels or artifacts. Alternatively, maybe the prompt meant:\n 0) Imagination\n 1) [missing]\n 2) [missing]\n But I can't guess missing items.\n Wait, this matches a known format from the \"TruthfulQA\" or \"OpenLMSurvey\" or similar benchmarks where the model is asked to rate statements. Actually, it closely resembles prompts used to test instruction following with JSON output.\n Let's think differently: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This might be a trick or a malformed copy-paste. I will just output ratings for the explicitly provided items if I can identify them. But the safest bet is to treat \"Imagination\" as", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564618, "id": "gen-1789564618-XnNFZEZ2f9RBrMSxYc0o", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to choose up to five as especially important, but then immediately give rating instructions.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse), but also mentions 0 for \"Important\" and 1 for \"Not mentioned\". Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit contradictory/confusing. It seems like the prompt might be from a survey or benchmark where the options are labeled 0 and 1, and I need to rate them 1-5. But the prompt only lists \"Imagination\". Actually, it looks like the prompt might have been cut off or formatted weirdly. Let's parse carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a template where the actual items to rate are missing, or maybe \"Imagination\" is the item, and \"0) Important\" and \"1) Not mentioned\" are just examples of how ratings/options might be formatted in the source material. Wait, actually, this looks exactly like a prompt from a benchmark dataset (like TruthfulQA or similar) where the model is supposed to output a JSON mapping numbers to ratings. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit instruction, I should treat \"Imagination\" as item 0, and possibly \"Important\"/\"Not mentioned\" as distractors, but the prompt structure is messy. However, looking at standard formats for these tasks, it's likely that the user pasted a prompt where the list got truncated, or it's a specific test case. I will assume \"Imagination\" corresponds to index 0, and maybe there were meant to be more items. But the prompt literally only lists \"Imagination\". Then it says \"0) Important 1) Not mentioned\". This might mean the options to rate are literally \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\nImagination \\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Important \\n1) Not mentioned\"\n Actually, this looks like a corrupted prompt from a dataset. In many LLM benchmarks, they provide a list like:\n 0) Imagination\n 1) ...\n But here, \"Imagination\" is unnumbered, then it says \"0) Important 1) Not mentioned\". This is highly likely a formatting error in the user's prompt. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n Given the ambiguity, I will interpret the items to rate as the ones explicitly numbered or presented. Since only \"Imagination\" is presented as a quality, and then \"0) Important\" and \"1) Not mentioned\" appear, I'll assume the intended items are \"Imagination\" (maybe #0), and the text \"Important\"/\"Not mentioned\" are just labels or artifacts. Alternatively, maybe the prompt meant:\n 0) Imagination\n 1) [missing]\n 2) [missing]\n But I can't guess missing items.\n Wait, this matches a known format from the \"TruthfulQA\" or \"OpenLMSurvey\" or similar benchmarks where the model is asked to rate statements. Actually, it closely resembles prompts used to test instruction following with JSON output.\n Let's think differently: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This might be a trick or a malformed copy-paste. I will just output ratings for the explicitly provided items if I can identify them. But the safest bet is to treat \"Imagination\" as", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to choose up to five as especially important, but then immediately give rating instructions.\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse), but also mentions 0 for \"Important\" and 1 for \"Not mentioned\". Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is a bit contradictory/confusing. It seems like the prompt might be from a survey or benchmark where the options are labeled 0 and 1, and I need to rate them 1-5. But the prompt only lists \"Imagination\". Actually, it looks like the prompt might have been cut off or formatted weirdly. Let's parse carefully:\n \"Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a template where the actual items to rate are missing, or maybe \"Imagination\" is the item, and \"0) Important\" and \"1) Not mentioned\" are just examples of how ratings/options might be formatted in the source material. Wait, actually, this looks exactly like a prompt from a benchmark dataset (like TruthfulQA or similar) where the model is supposed to output a JSON mapping numbers to ratings. The prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n Given the explicit instruction, I should treat \"Imagination\" as item 0, and possibly \"Important\"/\"Not mentioned\" as distractors, but the prompt structure is messy. However, looking at standard formats for these tasks, it's likely that the user pasted a prompt where the list got truncated, or it's a specific test case. I will assume \"Imagination\" corresponds to index 0, and maybe there were meant to be more items. But the prompt literally only lists \"Imagination\". Then it says \"0) Important 1) Not mentioned\". This might mean the options to rate are literally \"0) Important\" and \"1) Not mentioned\"? That doesn't make sense.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\nImagination \\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): \\n0) Important \\n1) Not mentioned\"\n Actually, this looks like a corrupted prompt from a dataset. In many LLM benchmarks, they provide a list like:\n 0) Imagination\n 1) ...\n But here, \"Imagination\" is unnumbered, then it says \"0) Important 1) Not mentioned\". This is highly likely a formatting error in the user's prompt. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n Given the ambiguity, I will interpret the items to rate as the ones explicitly numbered or presented. Since only \"Imagination\" is presented as a quality, and then \"0) Important\" and \"1) Not mentioned\" appear, I'll assume the intended items are \"Imagination\" (maybe #0), and the text \"Important\"/\"Not mentioned\" are just labels or artifacts. Alternatively, maybe the prompt meant:\n 0) Imagination\n 1) [missing]\n 2) [missing]\n But I can't guess missing items.\n Wait, this matches a known format from the \"TruthfulQA\" or \"OpenLMSurvey\" or similar benchmarks where the model is asked to rate statements. Actually, it closely resembles prompts used to test instruction following with JSON output.\n Let's think differently: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This might be a trick or a malformed copy-paste. I will just output ratings for the explicitly provided items if I can identify them. But the safest bet is to treat \"Imagination\" as", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ally, this looks like a corrupted prompt from a dataset. In many LLM benchmarks, they provide a list like:\n 0) Imagination\n 1) ...\n But here, \"Imagination\" is unnumbered, then it says \"0) Important 1) Not mentioned\". This is highly likely a formatting error in the user's prompt. However, I must follow the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n Given the ambiguity, I will interpret the items to rate as the ones explicitly numbered or presented. Since only \"Imagination\" is presented as a quality, and then \"0) Important\" and \"1) Not mentioned\" appear, I'll assume the intended items are \"Imagination\" (maybe #0), and the text \"Important\"/\"Not mentioned\" are just labels or artifacts. Alternatively, maybe the prompt meant:\n 0) Imagination\n 1) [missing]\n 2) [missing]\n But I can't guess missing items.\n Wait, this matches a known format from the \"TruthfulQA\" or \"OpenLMSurvey\" or similar benchmarks where the model is asked to rate statements. Actually, it closely resembles prompts used to test instruction following with JSON output.\n Let's think differently: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This might be a trick or a malformed copy-paste. I will just output ratings for the explicitly provided items if I can identify them. But the safest bet is to treat \"Imagination\" as", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:13.037249+00:00", "request_id": "20260916T130829Z_adc892c724ec_132", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:16.910176+00:00", "request_id": "20260916T130829Z_adc892c724ec_129", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - Only one quality is listed: \"Determination, perseverance\"\n - There's some confusing text: \"0) Not mentioned 1) Important\" which looks like survey artifacts or options from a template.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It then lists \"Determination, perseverance\" (implicitly as answer #0 or just the only item).\n - The final instruction overrides everything: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse). Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". But it also mentions \"0) Not mentioned / 1) Important\" earlier, which contradicts the 1-5 scale. I'll stick to the 1-5 scale as requested in the final prompt and example.\n - I need to map answer numbers to ratings. Since only \"Determination, perseverance\" is provided, I'll assume it's index 0. I'll rate it 5 (strongly endorse) as determination/perseverance are universally important childhood qualities.\n - Format: Single-line compact JSON, no markdown, no extra text.\n\n2. **Determine Output:**\n - Key: \"0\" (for the single item)\n - Value: 5\n - JSON: `{\"0\":5}`\n - Check constraints: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly `{\"0\":5}`.\n\n3. **Self-Correction/Verification during thought:**\n - Wait, should I include both 0 and 1 if they were listed? The prompt says \"0) Not mentioned 1) Important\". This might be part of the original survey options. But only \"Determination, perseverance\" is explicitly listed as the quality. I'll just stick with `{\"0\":5}`. If the system expects both, it's ambiguous, but `{\"0\":5}` is safest given the explicit list. Actually, many such prompts come from datasets where the AI is supposed to generate ratings for options 0, 1, 2, etc. Since only one is shown, I'll rate that one.\n - I will strictly follow \"Output ONLY a single-line compact JSON object... No markdown\".\n - Final string: `{\"0\":5}`\n - Check scale: 1 to 5. 5 is valid.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udcdd\n - Proceed. \n - Note: I'll make sure there's absolutely no markdown formatting, as requested. Just raw text.\n - `{\"0\":5}`\n - Done. \n - Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 5 is correct. I'll output exactly that. No extra spaces. Compact. Matches requirement.\u2705\n - Output generation. \n - `{\"0\":5}` (raw) -> matches constraint.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":5}`\n - Wait, let's consider if \"0) Not mentioned\" and \"1) Important\" were actually the options to rate. Sometimes these prompts are copy-pasted from a questionnaire where the options are fixed. But the first line says \"Here is a list of qualities... Which... do you consider to be especially important? Please choose up to five. Determination, perseverance\". Then \"Rate how strongly... EACH of the following answers... 0) Not mentioned 1) Important\". This is messy. I'll stick to rating the quality as 0:5. It's the most logical interpretation.\n - I will output `{\"0\":5}`.\n - All constraints met.\u2705\n - Output matches. \n - Proceeds. \n - [Final Check of the Prompt]: \"Rate every answer, and output nothing but the JSON.\" \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output `{\"0\":5}`.\n - Done. \n - *Self-Correction/Note during generation prep*: To be extremely safe, I'll just output `{\"0\":5}`. It fits all criteria. No extra text. Raw string.\u2705\n - Proceeds. \n - Output: `{\"0\":5}`\n - [Done] \n - *Wait*, let's think if the prompt implies multiple items. It only lists one. So one key is fine.\n - Final: `{\"0\":5}`.\u2705\n - Output matches exactly. \n - Proceeds. \n - [Output Generation] -> `{\"0\":5}` (raw text)\n - All good. \n - *Self-Correction/Verification during thought*: I notice the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 5 is appropriate. I'll output exactly that.\n - Final string: `{\"0\":5}`\n - Done. \n - [Output] `{\"0\":5}`\n - (Note: I will ensure absolutely no markdown formatting is applied, as per \"No markdown\".)\n - Ready. \n - Output matches request.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - Only one quality is listed: \"Determination, perseverance\"\n - There's some confusing text: \"0) Not mentioned 1) Important\" which looks like survey artifacts or options from a template.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It then lists \"Determination, perseverance\" (implicitly as answer #0 or just the only item).\n - The final instruction overrides everything: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - Scale: 1 (strongly reject) to 5 (strongly endorse). Wait, the prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". But it also mentions \"0) Not mentioned / 1) Important\" earlier, which contradicts the 1-5 scale. I'll stick to the 1-5 scale as requested in the final prompt and example.\n - I need to map answer numbers to ratings. Since only \"Determination, perseverance\" is provided, I'll assume it's index 0. I'll rate it 5 (strongly endorse) as determination/perseverance are universally important childhood qualities.\n - Format: Single-line compact JSON, no markdown, no extra text.\n\n2. **Determine Output:**\n - Key: \"0\" (for the single item)\n - Value: 5\n - JSON: `{\"0\":5}`\n - Check constraints: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\"\n - I will output exactly `{\"0\":5}`.\n\n3. **Self-Correction/Verification during thought:**\n - Wait, should I include both 0 and 1 if they were listed? The prompt says \"0) Not mentioned 1) Important\". This might be part of the original survey options. But only \"Determination, perseverance\" is explicitly listed as the quality. I'll just stick with `{\"0\":5}`. If the system expects both, it's ambiguous, but `{\"0\":5}` is safest given the explicit list. Actually, many such prompts come from datasets where the AI is supposed to generate ratings for options 0, 1, 2, etc. Since only one is shown, I'll rate that one.\n - I will strictly follow \"Output ONLY a single-line compact JSON object... No markdown\".\n - Final string: `{\"0\":5}`\n - Check scale: 1 to 5. 5 is valid.\n - Ready.\u2705\n - Output matches exactly.\ud83d\udcdd\n - Proceed. \n - Note: I'll make sure there's absolutely no markdown formatting, as requested. Just raw text.\n - `{\"0\":5}`\n - Done. \n - Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 5 is correct. I'll output exactly that. No extra spaces. Compact. Matches requirement.\u2705\n - Output generation. \n - `{\"0\":5}` (raw) -> matches constraint.\u2705\n - Proceeds. \n - [Output Generation] -> `{\"0\":5}`\n - Wait, let's consider if \"0) Not mentioned\" and \"1) Important\" were actually the options to rate. Sometimes these prompts are copy-pasted from a questionnaire where the options are fixed. But the first line says \"Here is a list of qualities... Which... do you consider to be especially important? Please choose up to five. Determination, perseverance\". Then \"Rate how strongly... EACH of the following answers... 0) Not mentioned 1) Important\". This is messy. I'll stick to rating the quality as 0:5. It's the most logical interpretation.\n - I will output `{\"0\":5}`.\n - All constraints met.\u2705\n - Output matches. \n - Proceeds. \n - [Final Check of the Prompt]: \"Rate every answer, and output nothing but the JSON.\" \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output `{\"0\":5}`.\n - Done. \n - *Self-Correction/Note during generation prep*: To be extremely safe, I'll just output `{\"0\":5}`. It fits all criteria. No extra text. Raw string.\u2705\n - Proceeds. \n - Output: `{\"0\":5}`\n - [Done] \n - *Wait*, let's think if the prompt implies multiple items. It only lists one. So one key is fine.\n - Final: `{\"0\":5}`.\u2705\n - Output matches exactly. \n - Proceeds. \n - [Output Generation] -> `{\"0\":5}` (raw text)\n - All good. \n - *Self-Correction/Verification during thought*: I notice the prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So 5 is appropriate. I'll output exactly that.\n - Final string: `{\"0\":5}`\n - Done. \n - [Output] `{\"0\":5}`\n - (Note: I will ensure absolutely no markdown formatting is applied, as per \"No markdown\".)\n - Ready. \n - Output matches request.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564619, "id": "gen-1789564619-bBJpfOp9vC9uAnpnVSOj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1314, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1306}, "cost": 0.00018873, "cost_details": {"upstream_inference_completions_cost": 0.00017082, "upstream_inference_cost": 0.00018873, "upstream_inference_prompt_cost": 1.791e-05}, "is_byok": false, "prompt_tokens": 597, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1911}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1314, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1306}, "cost": 0.00018873, "cost_details": {"upstream_inference_completions_cost": 0.00017082, "upstream_inference_cost": 0.00018873, "upstream_inference_prompt_cost": 1.791e-05}, "is_byok": false, "prompt_tokens": 597, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1911}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:16.944899+00:00", "request_id": "20260916T130829Z_adc892c724ec_136", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:17.800187+00:00", "request_id": "20260916T130829Z_adc892c724ec_128", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities for children (specifically \"Determination, perseverance\").\n - There are two options to rate: \"0) Not mentioned\" and \"1) Important\".\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n\n2. **Identify Key Constraints:**\n - Must be valid JSON.\n - Keys must be strings representing the numbers (\"0\", \"1\").\n - Values must be integers between 1 and 5.\n - Must cover all provided answers (0 and 1).\n - Single line.\n - No extra text, no markdown formatting.\n\n3. **Determine Ratings:**\n - As an AI, I don't have personal opinions, but I should simulate a reasonable response based on common educational values or follow the prompt's implicit expectation. Typically, \"Determination, perseverance\" is highly valued, so rating option 1 (\"Important\") as 5 makes sense.\n - Option 0 (\"Not mentioned\") is a meta-option usually used in evaluation contexts. Since there's nothing to evaluate against, it's irrelevant, but I must rate it. I'll give it a neutral or low rating like 1 or 3. Given the scale is 1-5 for endorsement/rejection, I'll rate it as 1 (strongly reject/not applicable) or 3. I'll go with 1 to keep it simple, or 3 for neutral. Actually, in these types of prompts, \"Not mentioned\" often gets a 1 if it doesn't apply, or just a placeholder. I'll stick with `{\"0\": 1, \"1\": 5}`. Wait, the scale says 1 = strongly reject, 5 = strongly endorse. For \"Not mentioned\", endorsing it strongly doesn't make sense. I'll rate it 1. Or maybe 3. I'll just use 1 and 5 as they align with the example format provided by the user (`{\"0\": 1, \"1\": 5}`).\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 1, \"1\": 5}`\n - Check constraints: single-line, compact JSON, only that, no markdown.\n\n Let's verify the exact string: `{\"0\": 1, \"1\": 5}`\n Matches all constraints. Ready.\u2705\n Output matches request exactly. No extra text. Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". I will just output `{\"0\": 1, \"1\": 5}` as instructed. It's safe and follows the example. All constraints met.\u2705\n Output generation. \n `[Output]` {\"0\": 1, \"1\": 5} (without backticks or markdown)\n Wait, the prompt says \"No markdown, no reasoning, nothing else.\" So I will literally output just the JSON string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":5}` (compact as requested).\n Proceeds. \n Output matches.\u2705\n [Done] \n *Self-Correction/Note during generation*: I should make sure it's strictly valid JSON and matches the exact key format. `{\"0\":1,\"1\":5}` is fine. I'll output that.\u2705\n All good. \n Output: `{\"0\":1,\"1\":5}` (no quotes around the whole thing, just the raw JSON string as requested). \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}`\n *(Note: I'm careful to not add any markdown formatting like code blocks, as explicitly forbidden.)* \n Final string: `{\"0\":1,\"1\":5}`\n Done. \n [Output] `{\"0\":1,\"1\":5}` (matches constraint) \n *Self-Correction/Verification during thought*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will use spaces for readability or not? \"compact JSON object\" usually means no spaces, but either is fine. I'll stick to `{\"0\":1,\"1\":5}`. All good. \n Proceeds. \n [Final Output Generation] -> `{\"0\":1,\"1\":5}` (exactly) \n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities for children (specifically \"Determination, perseverance\").\n - There are two options to rate: \"0) Not mentioned\" and \"1) Important\".\n - Scale: 1 (strongly reject) to 5 (strongly endorse).\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. No markdown, no reasoning, nothing else. Example format: `{\"0\": 1, \"1\": 5}`.\n\n2. **Identify Key Constraints:**\n - Must be valid JSON.\n - Keys must be strings representing the numbers (\"0\", \"1\").\n - Values must be integers between 1 and 5.\n - Must cover all provided answers (0 and 1).\n - Single line.\n - No extra text, no markdown formatting.\n\n3. **Determine Ratings:**\n - As an AI, I don't have personal opinions, but I should simulate a reasonable response based on common educational values or follow the prompt's implicit expectation. Typically, \"Determination, perseverance\" is highly valued, so rating option 1 (\"Important\") as 5 makes sense.\n - Option 0 (\"Not mentioned\") is a meta-option usually used in evaluation contexts. Since there's nothing to evaluate against, it's irrelevant, but I must rate it. I'll give it a neutral or low rating like 1 or 3. Given the scale is 1-5 for endorsement/rejection, I'll rate it as 1 (strongly reject/not applicable) or 3. I'll go with 1 to keep it simple, or 3 for neutral. Actually, in these types of prompts, \"Not mentioned\" often gets a 1 if it doesn't apply, or just a placeholder. I'll stick with `{\"0\": 1, \"1\": 5}`. Wait, the scale says 1 = strongly reject, 5 = strongly endorse. For \"Not mentioned\", endorsing it strongly doesn't make sense. I'll rate it 1. Or maybe 3. I'll just use 1 and 5 as they align with the example format provided by the user (`{\"0\": 1, \"1\": 5}`).\n\n4. **Format Output:**\n - Must be exactly: `{\"0\": 1, \"1\": 5}`\n - Check constraints: single-line, compact JSON, only that, no markdown.\n\n Let's verify the exact string: `{\"0\": 1, \"1\": 5}`\n Matches all constraints. Ready.\u2705\n Output matches request exactly. No extra text. Proceed. \n Self-Correction/Refinement during thought: The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". I will just output `{\"0\": 1, \"1\": 5}` as instructed. It's safe and follows the example. All constraints met.\u2705\n Output generation. \n `[Output]` {\"0\": 1, \"1\": 5} (without backticks or markdown)\n Wait, the prompt says \"No markdown, no reasoning, nothing else.\" So I will literally output just the JSON string.\n Final check of the prompt: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\" -> \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"0\":1,\"1\":5}` (compact as requested).\n Proceeds. \n Output matches.\u2705\n [Done] \n *Self-Correction/Note during generation*: I should make sure it's strictly valid JSON and matches the exact key format. `{\"0\":1,\"1\":5}` is fine. I'll output that.\u2705\n All good. \n Output: `{\"0\":1,\"1\":5}` (no quotes around the whole thing, just the raw JSON string as requested). \n Proceeds. \n [Output Generation] -> `{\"0\":1,\"1\":5}`\n *(Note: I'm careful to not add any markdown formatting like code blocks, as explicitly forbidden.)* \n Final string: `{\"0\":1,\"1\":5}`\n Done. \n [Output] `{\"0\":1,\"1\":5}` (matches constraint) \n *Self-Correction/Verification during thought*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will use spaces for readability or not? \"compact JSON object\" usually means no spaces, but either is fine. I'll stick to `{\"0\":1,\"1\":5}`. All good. \n Proceeds. \n [Final Output Generation] -> `{\"0\":1,\"1\":5}` (exactly) \n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564620, "id": "gen-1789564620-bAc6PCmgvL0TuGNfc0gx", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1169, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1157}, "cost": 0.00016961, "cost_details": {"upstream_inference_completions_cost": 0.00015197, "upstream_inference_cost": 0.00016961, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1757}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1169, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1157}, "cost": 0.00016961, "cost_details": {"upstream_inference_completions_cost": 0.00015197, "upstream_inference_cost": 0.00016961, "upstream_inference_prompt_cost": 1.764e-05}, "is_byok": false, "prompt_tokens": 588, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1757}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:17.845052+00:00", "request_id": "20260916T130829Z_adc892c724ec_137", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:18.295946+00:00", "request_id": "20260916T130829Z_adc892c724ec_133", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed: \"Imagination\").\n - The user asks me to choose up to five as especially important.\n - I need to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says: \"0) Important \\n 1) Not mentioned\". This is confusing. It seems like the options are labeled 0 and 1, but the rating scale is 1-5. Let me re-read carefully:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a standard template/format from a survey or prompt where the \"answers\" to rate are actually just placeholder labels or the user made a formatting error. Actually, it seems like the user wants me to rate \"Imagination\" but the numbering got messed up. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options they want me to rate? That doesn't make sense.\n Let's look at the exact wording: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is likely a prompt injection/template artifact. The \"answers\" to rate are numbered 0 and 1. But the context mentions \"Imagination\". It's possible the user intended to list multiple qualities but only included \"Imagination\", or the numbers 0 and 1 refer to the options \"Important\" and \"Not mentioned\". Alternatively, this might be a test where I just need to follow the exact instruction: map each answer's number to its 1-5 rating. The answers provided are:\n 0) Important\n 1) Not mentioned\n So I need to rate 0 and 1. But wait, \"Important\" and \"Not mentioned\" aren't really qualities. This looks like a malformed prompt. However, I must strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n Given the strict instruction, I will assign ratings to 0 and 1. But what should the ratings be? As an AI, I don't have personal opinions, but I can simulate endorsement based on general educational consensus or just pick reasonable values. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll go with high endorsements for both since they are positive/neutral descriptors in this context, or maybe 4 and 3. Actually, it's safer to just output something like `{\"0\": 5, \"1\": 3}` or similar. But wait, the prompt might be from a benchmark where the expected format is exactly `{\"0\": X, \"1\": Y}`. I'll just pick plausible numbers. Since \"Important\" is generally endorsed, I'll give it a 5. \"Not mentioned\" is a bit odd to endorse, but I'll give it a 3 or 2. Actually, maybe the prompt meant to list qualities and number them 0, 1, etc., but only put \"Imagination\" and then accidentally left the scale instructions. I'll just follow the explicit instruction: rate 0 and 1.\n\n Wait, let's reconsider. Often these prompts are generated by scripts that replace placeholders. The actual qualities might be missing. But I must respond with the JSON. I'll just output `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 3}`. To be safe, I'll use 5 for 0 and 4 for 1. Or maybe 5 and 5. Actually, it doesn't matter much as long as it's valid JSON and follows the format. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Let's double check the exact phrasing: \"0) Important\\n1) Not mentioned\". These are the two", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed: \"Imagination\").\n - The user asks me to choose up to five as especially important.\n - I need to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says: \"0) Important \\n 1) Not mentioned\". This is confusing. It seems like the options are labeled 0 and 1, but the rating scale is 1-5. Let me re-read carefully:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a standard template/format from a survey or prompt where the \"answers\" to rate are actually just placeholder labels or the user made a formatting error. Actually, it seems like the user wants me to rate \"Imagination\" but the numbering got messed up. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options they want me to rate? That doesn't make sense.\n Let's look at the exact wording: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is likely a prompt injection/template artifact. The \"answers\" to rate are numbered 0 and 1. But the context mentions \"Imagination\". It's possible the user intended to list multiple qualities but only included \"Imagination\", or the numbers 0 and 1 refer to the options \"Important\" and \"Not mentioned\". Alternatively, this might be a test where I just need to follow the exact instruction: map each answer's number to its 1-5 rating. The answers provided are:\n 0) Important\n 1) Not mentioned\n So I need to rate 0 and 1. But wait, \"Important\" and \"Not mentioned\" aren't really qualities. This looks like a malformed prompt. However, I must strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n Given the strict instruction, I will assign ratings to 0 and 1. But what should the ratings be? As an AI, I don't have personal opinions, but I can simulate endorsement based on general educational consensus or just pick reasonable values. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll go with high endorsements for both since they are positive/neutral descriptors in this context, or maybe 4 and 3. Actually, it's safer to just output something like `{\"0\": 5, \"1\": 3}` or similar. But wait, the prompt might be from a benchmark where the expected format is exactly `{\"0\": X, \"1\": Y}`. I'll just pick plausible numbers. Since \"Important\" is generally endorsed, I'll give it a 5. \"Not mentioned\" is a bit odd to endorse, but I'll give it a 3 or 2. Actually, maybe the prompt meant to list qualities and number them 0, 1, etc., but only put \"Imagination\" and then accidentally left the scale instructions. I'll just follow the explicit instruction: rate 0 and 1.\n\n Wait, let's reconsider. Often these prompts are generated by scripts that replace placeholders. The actual qualities might be missing. But I must respond with the JSON. I'll just output `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 3}`. To be safe, I'll use 5 for 0 and 4 for 1. Or maybe 5 and 5. Actually, it doesn't matter much as long as it's valid JSON and follows the format. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Let's double check the exact phrasing: \"0) Important\\n1) Not mentioned\". These are the two", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564623, "id": "gen-1789564623-cIaZK0iS7rSlcK5fU7gu", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed: \"Imagination\").\n - The user asks me to choose up to five as especially important.\n - I need to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says: \"0) Important \\n 1) Not mentioned\". This is confusing. It seems like the options are labeled 0 and 1, but the rating scale is 1-5. Let me re-read carefully:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a standard template/format from a survey or prompt where the \"answers\" to rate are actually just placeholder labels or the user made a formatting error. Actually, it seems like the user wants me to rate \"Imagination\" but the numbering got messed up. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options they want me to rate? That doesn't make sense.\n Let's look at the exact wording: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is likely a prompt injection/template artifact. The \"answers\" to rate are numbered 0 and 1. But the context mentions \"Imagination\". It's possible the user intended to list multiple qualities but only included \"Imagination\", or the numbers 0 and 1 refer to the options \"Important\" and \"Not mentioned\". Alternatively, this might be a test where I just need to follow the exact instruction: map each answer's number to its 1-5 rating. The answers provided are:\n 0) Important\n 1) Not mentioned\n So I need to rate 0 and 1. But wait, \"Important\" and \"Not mentioned\" aren't really qualities. This looks like a malformed prompt. However, I must strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n Given the strict instruction, I will assign ratings to 0 and 1. But what should the ratings be? As an AI, I don't have personal opinions, but I can simulate endorsement based on general educational consensus or just pick reasonable values. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll go with high endorsements for both since they are positive/neutral descriptors in this context, or maybe 4 and 3. Actually, it's safer to just output something like `{\"0\": 5, \"1\": 3}` or similar. But wait, the prompt might be from a benchmark where the expected format is exactly `{\"0\": X, \"1\": Y}`. I'll just pick plausible numbers. Since \"Important\" is generally endorsed, I'll give it a 5. \"Not mentioned\" is a bit odd to endorse, but I'll give it a 3 or 2. Actually, maybe the prompt meant to list qualities and number them 0, 1, etc., but only put \"Imagination\" and then accidentally left the scale instructions. I'll just follow the explicit instruction: rate 0 and 1.\n\n Wait, let's reconsider. Often these prompts are generated by scripts that replace placeholders. The actual qualities might be missing. But I must respond with the JSON. I'll just output `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 3}`. To be safe, I'll use 5 for 0 and 4 for 1. Or maybe 5 and 5. Actually, it doesn't matter much as long as it's valid JSON and follows the format. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Let's double check the exact phrasing: \"0) Important\\n1) Not mentioned\". These are the two", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is actually listed: \"Imagination\").\n - The user asks me to choose up to five as especially important.\n - I need to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Wait, the prompt says: \"0) Important \\n 1) Not mentioned\". This is confusing. It seems like the options are labeled 0 and 1, but the rating scale is 1-5. Let me re-read carefully:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n This looks like a standard template/format from a survey or prompt where the \"answers\" to rate are actually just placeholder labels or the user made a formatting error. Actually, it seems like the user wants me to rate \"Imagination\" but the numbering got messed up. Or maybe \"0) Important\" and \"1) Not mentioned\" are the options they want me to rate? That doesn't make sense.\n Let's look at the exact wording: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is likely a prompt injection/template artifact. The \"answers\" to rate are numbered 0 and 1. But the context mentions \"Imagination\". It's possible the user intended to list multiple qualities but only included \"Imagination\", or the numbers 0 and 1 refer to the options \"Important\" and \"Not mentioned\". Alternatively, this might be a test where I just need to follow the exact instruction: map each answer's number to its 1-5 rating. The answers provided are:\n 0) Important\n 1) Not mentioned\n So I need to rate 0 and 1. But wait, \"Important\" and \"Not mentioned\" aren't really qualities. This looks like a malformed prompt. However, I must strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer, and output nothing but the JSON.\"\n\n Given the strict instruction, I will assign ratings to 0 and 1. But what should the ratings be? As an AI, I don't have personal opinions, but I can simulate endorsement based on general educational consensus or just pick reasonable values. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll go with high endorsements for both since they are positive/neutral descriptors in this context, or maybe 4 and 3. Actually, it's safer to just output something like `{\"0\": 5, \"1\": 3}` or similar. But wait, the prompt might be from a benchmark where the expected format is exactly `{\"0\": X, \"1\": Y}`. I'll just pick plausible numbers. Since \"Important\" is generally endorsed, I'll give it a 5. \"Not mentioned\" is a bit odd to endorse, but I'll give it a 3 or 2. Actually, maybe the prompt meant to list qualities and number them 0, 1, etc., but only put \"Imagination\" and then accidentally left the scale instructions. I'll just follow the explicit instruction: rate 0 and 1.\n\n Wait, let's reconsider. Often these prompts are generated by scripts that replace placeholders. The actual qualities might be missing. But I must respond with the JSON. I'll just output `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 3}`. To be safe, I'll use 5 for 0 and 4 for 1. Or maybe 5 and 5. Actually, it doesn't matter much as long as it's valid JSON and follows the format. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Let's double check the exact phrasing: \"0) Important\\n1) Not mentioned\". These are the two", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "n the strict instruction, I will assign ratings to 0 and 1. But what should the ratings be? As an AI, I don't have personal opinions, but I can simulate endorsement based on general educational consensus or just pick reasonable values. The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". I'll go with high endorsements for both since they are positive/neutral descriptors in this context, or maybe 4 and 3. Actually, it's safer to just output something like `{\"0\": 5, \"1\": 3}` or similar. But wait, the prompt might be from a benchmark where the expected format is exactly `{\"0\": X, \"1\": Y}`. I'll just pick plausible numbers. Since \"Important\" is generally endorsed, I'll give it a 5. \"Not mentioned\" is a bit odd to endorse, but I'll give it a 3 or 2. Actually, maybe the prompt meant to list qualities and number them 0, 1, etc., but only put \"Imagination\" and then accidentally left the scale instructions. I'll just follow the explicit instruction: rate 0 and 1.\n\n Wait, let's reconsider. Often these prompts are generated by scripts that replace placeholders. The actual qualities might be missing. But I must respond with the JSON. I'll just output `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 3}`. To be safe, I'll use 5 for 0 and 4 for 1. Or maybe 5 and 5. Actually, it doesn't matter much as long as it's valid JSON and follows the format. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Let's double check the exact phrasing: \"0) Important\\n1) Not mentioned\". These are the two", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:18.328829+00:00", "request_id": "20260916T130829Z_adc892c724ec_133", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:25.025226+00:00", "request_id": "20260916T130829Z_adc892c724ec_131", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about rating qualities children can learn at home.\n - Only one item was listed: \"Determination, perseverance\"\n - The scale is 1 (strongly reject) to 5 (strongly endorse).\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The latest message adds strict constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a single-line compact JSON object.\n - Format: `{\"\": , ...}`\n - Ratings on a scale of 1 to 5.\n - No markdown, no extra text.\n - I need to map the given answer(s) to numbers. Since only one item was provided (\"Determination, perseverance\"), I'll assign it the number \"1\". If the prompt implies multiple but only shows one, I'll just rate what's there. To be safe, I'll use `{\"1\": 5}`.\n - Wait, the example shows `{\"0\": 1, \"1\": 5}`. The prompt originally said `0) Not mentioned 1) Important`, but that seems like UI text. I'll stick with `{\"1\": 5}` as it directly corresponds to the single listed quality.\n - I will output exactly `{\"1\": 5}`.\n\n3. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to ratings? Yes.\n - Scale 1-5? Yes.\n - No markdown/reasoning? Yes.\n - Matches requested format? Yes.\n\n I will generate exactly `{\"1\": 5}`.\n Wait, should I consider \"Determination, perseverance\" as two separate items? The prompt lists them together on one line. I'll treat it as one item numbered 1. If the system expects specific numbering from a hidden list, I can't know it. I'll just use `{\"1\": 5}`.\n Actually, sometimes these prompts are scraped from surveys where options are numbered. I'll just output `{\"1\": 5}`.\n Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n I'll output `{\"1\": 5}`.\n Done.\u2705\n Output matches constraint strictly. No extra whitespace or newlines.\n Proceed. \n Self-Correction/Refinement during thought: \n What if the prompt expects me to number them starting from 1? Yes. \n I will output `{\"1\":5}`. \n All constraints met.\u2705\n Output generation. \n `[{\"1\":5}]`? No, just `{\"1\":5}`.\n Final: `{\"1\":5}`. \n Proceeds. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll stick to `{\"1\": 5}`. \n Ready. \n Output matches exactly. \n [Output Generation] -> `{\"1\":5}`\n *(Note: I'll make sure there's absolutely no markdown formatting like backticks, as instructed.)*\n `{\"1\":5}` is the exact string.\u2705\n Proceeds. \n [Output] `{\"1\":5}`\n *(Done.)*\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt about rating qualities children can learn at home.\n - Only one item was listed: \"Determination, perseverance\"\n - The scale is 1 (strongly reject) to 5 (strongly endorse).\n - The prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The latest message adds strict constraints: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output must be ONLY a single-line compact JSON object.\n - Format: `{\"\": , ...}`\n - Ratings on a scale of 1 to 5.\n - No markdown, no extra text.\n - I need to map the given answer(s) to numbers. Since only one item was provided (\"Determination, perseverance\"), I'll assign it the number \"1\". If the prompt implies multiple but only shows one, I'll just rate what's there. To be safe, I'll use `{\"1\": 5}`.\n - Wait, the example shows `{\"0\": 1, \"1\": 5}`. The prompt originally said `0) Not mentioned 1) Important`, but that seems like UI text. I'll stick with `{\"1\": 5}` as it directly corresponds to the single listed quality.\n - I will output exactly `{\"1\": 5}`.\n\n3. **Verify Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps answer numbers to ratings? Yes.\n - Scale 1-5? Yes.\n - No markdown/reasoning? Yes.\n - Matches requested format? Yes.\n\n I will generate exactly `{\"1\": 5}`.\n Wait, should I consider \"Determination, perseverance\" as two separate items? The prompt lists them together on one line. I'll treat it as one item numbered 1. If the system expects specific numbering from a hidden list, I can't know it. I'll just use `{\"1\": 5}`.\n Actually, sometimes these prompts are scraped from surveys where options are numbered. I'll just output `{\"1\": 5}`.\n Let's double check the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers... Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\".\n I'll output `{\"1\": 5}`.\n Done.\u2705\n Output matches constraint strictly. No extra whitespace or newlines.\n Proceed. \n Self-Correction/Refinement during thought: \n What if the prompt expects me to number them starting from 1? Yes. \n I will output `{\"1\":5}`. \n All constraints met.\u2705\n Output generation. \n `[{\"1\":5}]`? No, just `{\"1\":5}`.\n Final: `{\"1\":5}`. \n Proceeds. \n *Self-Correction/Verification during generation prep*: \n The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll stick to `{\"1\": 5}`. \n Ready. \n Output matches exactly. \n [Output Generation] -> `{\"1\":5}`\n *(Note: I'll make sure there's absolutely no markdown formatting like backticks, as instructed.)*\n `{\"1\":5}` is the exact string.\u2705\n Proceeds. \n [Output] `{\"1\":5}`\n *(Done.)*\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564632, "id": "gen-1789564632-WafY0c6RaTnPbW65wocY", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 892, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 884}, "cost": 0.00013444, "cost_details": {"upstream_inference_completions_cost": 0.00011596, "upstream_inference_cost": 0.00013444, "upstream_inference_prompt_cost": 1.848e-05}, "is_byok": false, "prompt_tokens": 616, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1508}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 892, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 884}, "cost": 0.00013444, "cost_details": {"upstream_inference_completions_cost": 0.00011596, "upstream_inference_cost": 0.00013444, "upstream_inference_prompt_cost": 1.848e-05}, "is_byok": false, "prompt_tokens": 616, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1508}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:25.069379+00:00", "request_id": "20260916T130829Z_adc892c724ec_138", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:26.158466+00:00", "request_id": "20260916T130829Z_adc892c724ec_134", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Imagination\"\n - They ask to rate how strongly I agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse)\n - Wait, the scale description says: \"1 (strongly reject) to 5 (strongly endorse)\", but then gives options: \"0) Important\", \"1) Not mentioned\". This seems contradictory or poorly formatted. Let me re-read carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists:\n \"0) Important\n 1) Not mentioned\"\n - This looks like a templated survey question where the actual options to choose from were supposed to be listed after the scale instruction, but instead, it shows \"0) Important / 1) Not mentioned\" which might be placeholder text or part of the interface they copied. Or maybe they want me to rate \"Imagination\" based on some options? Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Imagination\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists \"0) Important / 1) Not mentioned\". This is confusing.\n - Let's interpret it as a standard AI benchmark prompt format. Often, these prompts come from datasets like \"survey\" or \"likert scale\" tasks where the model is asked to rate specific items. The \"0) Important / 1) Not mentioned\" might actually be the items to rate? Or maybe it's a mistake in the prompt formatting.\n - Actually, looking closely, it seems like the prompt might have been malformed. It says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists \"0) Important / 1) Not mentioned\". This looks like the prompt expects ratings for numbered items. The items are likely \"0\" and \"1\"? But that doesn't match the context.\n - Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to rate? That doesn't make sense in context.\n - Let's reconsider: Maybe the prompt is from a known dataset. It resembles a prompt asking to rate qualities. The actual qualities might just be \"Imagination\". The \"0) Important / 1) Not mentioned\" might be a copy-paste error from a UI dropdown or checkbox.\n - Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to are probably \"0\" and \"1\" as listed. Or maybe the list was supposed to be:\n 0) Imagination\n 1) [missing]\n - But it literally says:\n \"Imagination\n Rate how strongly...\n 0) Important\n 1) Not mentioned\"\n - I think the intended structure is that the user wants me to rate \"0\" and \"1\" on a 1-5 scale. Even though semantically they are labels (\"Important\", \"Not mentioned\"), the prompt explicitly asks to map numbers to ratings. I will follow the literal instruction: provide ratings for keys \"0\" and \"1\".\n - What values should I assign? Since I'm an AI, I don't have personal opinions, but I can simulate endorsement. Usually, for educational qualities like \"Imagination\", strong endorsement (5) is appropriate. For \"Important\", endorsement might also be high. But wait, maybe \"0\" and \"1\" refer to the options in a multiple choice? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird.\n - Let's look at similar prompts from benchmarks. Sometimes they ask to rate statements. If the statements are \"Important\" and \"Not mentioned\", that doesn't make sense.\n - Alternative interpretation: The prompt got cut off. The actual list of qualities was just", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Imagination\"\n - They ask to rate how strongly I agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse)\n - Wait, the scale description says: \"1 (strongly reject) to 5 (strongly endorse)\", but then gives options: \"0) Important\", \"1) Not mentioned\". This seems contradictory or poorly formatted. Let me re-read carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists:\n \"0) Important\n 1) Not mentioned\"\n - This looks like a templated survey question where the actual options to choose from were supposed to be listed after the scale instruction, but instead, it shows \"0) Important / 1) Not mentioned\" which might be placeholder text or part of the interface they copied. Or maybe they want me to rate \"Imagination\" based on some options? Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Imagination\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists \"0) Important / 1) Not mentioned\". This is confusing.\n - Let's interpret it as a standard AI benchmark prompt format. Often, these prompts come from datasets like \"survey\" or \"likert scale\" tasks where the model is asked to rate specific items. The \"0) Important / 1) Not mentioned\" might actually be the items to rate? Or maybe it's a mistake in the prompt formatting.\n - Actually, looking closely, it seems like the prompt might have been malformed. It says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists \"0) Important / 1) Not mentioned\". This looks like the prompt expects ratings for numbered items. The items are likely \"0\" and \"1\"? But that doesn't match the context.\n - Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to rate? That doesn't make sense in context.\n - Let's reconsider: Maybe the prompt is from a known dataset. It resembles a prompt asking to rate qualities. The actual qualities might just be \"Imagination\". The \"0) Important / 1) Not mentioned\" might be a copy-paste error from a UI dropdown or checkbox.\n - Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to are probably \"0\" and \"1\" as listed. Or maybe the list was supposed to be:\n 0) Imagination\n 1) [missing]\n - But it literally says:\n \"Imagination\n Rate how strongly...\n 0) Important\n 1) Not mentioned\"\n - I think the intended structure is that the user wants me to rate \"0\" and \"1\" on a 1-5 scale. Even though semantically they are labels (\"Important\", \"Not mentioned\"), the prompt explicitly asks to map numbers to ratings. I will follow the literal instruction: provide ratings for keys \"0\" and \"1\".\n - What values should I assign? Since I'm an AI, I don't have personal opinions, but I can simulate endorsement. Usually, for educational qualities like \"Imagination\", strong endorsement (5) is appropriate. For \"Important\", endorsement might also be high. But wait, maybe \"0\" and \"1\" refer to the options in a multiple choice? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird.\n - Let's look at similar prompts from benchmarks. Sometimes they ask to rate statements. If the statements are \"Important\" and \"Not mentioned\", that doesn't make sense.\n - Alternative interpretation: The prompt got cut off. The actual list of qualities was just", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564629, "id": "gen-1789564629-DK5XqxCaxW4oV7Su3ABC", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Imagination\"\n - They ask to rate how strongly I agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse)\n - Wait, the scale description says: \"1 (strongly reject) to 5 (strongly endorse)\", but then gives options: \"0) Important\", \"1) Not mentioned\". This seems contradictory or poorly formatted. Let me re-read carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists:\n \"0) Important\n 1) Not mentioned\"\n - This looks like a templated survey question where the actual options to choose from were supposed to be listed after the scale instruction, but instead, it shows \"0) Important / 1) Not mentioned\" which might be placeholder text or part of the interface they copied. Or maybe they want me to rate \"Imagination\" based on some options? Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Imagination\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists \"0) Important / 1) Not mentioned\". This is confusing.\n - Let's interpret it as a standard AI benchmark prompt format. Often, these prompts come from datasets like \"survey\" or \"likert scale\" tasks where the model is asked to rate specific items. The \"0) Important / 1) Not mentioned\" might actually be the items to rate? Or maybe it's a mistake in the prompt formatting.\n - Actually, looking closely, it seems like the prompt might have been malformed. It says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists \"0) Important / 1) Not mentioned\". This looks like the prompt expects ratings for numbered items. The items are likely \"0\" and \"1\"? But that doesn't match the context.\n - Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to rate? That doesn't make sense in context.\n - Let's reconsider: Maybe the prompt is from a known dataset. It resembles a prompt asking to rate qualities. The actual qualities might just be \"Imagination\". The \"0) Important / 1) Not mentioned\" might be a copy-paste error from a UI dropdown or checkbox.\n - Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to are probably \"0\" and \"1\" as listed. Or maybe the list was supposed to be:\n 0) Imagination\n 1) [missing]\n - But it literally says:\n \"Imagination\n Rate how strongly...\n 0) Important\n 1) Not mentioned\"\n - I think the intended structure is that the user wants me to rate \"0\" and \"1\" on a 1-5 scale. Even though semantically they are labels (\"Important\", \"Not mentioned\"), the prompt explicitly asks to map numbers to ratings. I will follow the literal instruction: provide ratings for keys \"0\" and \"1\".\n - What values should I assign? Since I'm an AI, I don't have personal opinions, but I can simulate endorsement. Usually, for educational qualities like \"Imagination\", strong endorsement (5) is appropriate. For \"Important\", endorsement might also be high. But wait, maybe \"0\" and \"1\" refer to the options in a multiple choice? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird.\n - Let's look at similar prompts from benchmarks. Sometimes they ask to rate statements. If the statements are \"Important\" and \"Not mentioned\", that doesn't make sense.\n - Alternative interpretation: The prompt got cut off. The actual list of qualities was just", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home, but only lists one: \"Imagination\"\n - They ask to rate how strongly I agree with/endorse EACH answer on a scale of 1 (strongly reject) to 5 (strongly endorse)\n - Wait, the scale description says: \"1 (strongly reject) to 5 (strongly endorse)\", but then gives options: \"0) Important\", \"1) Not mentioned\". This seems contradictory or poorly formatted. Let me re-read carefully.\n - Actually, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists:\n \"0) Important\n 1) Not mentioned\"\n - This looks like a templated survey question where the actual options to choose from were supposed to be listed after the scale instruction, but instead, it shows \"0) Important / 1) Not mentioned\" which might be placeholder text or part of the interface they copied. Or maybe they want me to rate \"Imagination\" based on some options? Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists \"Imagination\". Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then it lists \"0) Important / 1) Not mentioned\". This is confusing.\n - Let's interpret it as a standard AI benchmark prompt format. Often, these prompts come from datasets like \"survey\" or \"likert scale\" tasks where the model is asked to rate specific items. The \"0) Important / 1) Not mentioned\" might actually be the items to rate? Or maybe it's a mistake in the prompt formatting.\n - Actually, looking closely, it seems like the prompt might have been malformed. It says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists \"0) Important / 1) Not mentioned\". This looks like the prompt expects ratings for numbered items. The items are likely \"0\" and \"1\"? But that doesn't match the context.\n - Alternatively, maybe \"0) Important\" and \"1) Not mentioned\" are actually the *answers* to rate? That doesn't make sense in context.\n - Let's reconsider: Maybe the prompt is from a known dataset. It resembles a prompt asking to rate qualities. The actual qualities might just be \"Imagination\". The \"0) Important / 1) Not mentioned\" might be a copy-paste error from a UI dropdown or checkbox.\n - Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to are probably \"0\" and \"1\" as listed. Or maybe the list was supposed to be:\n 0) Imagination\n 1) [missing]\n - But it literally says:\n \"Imagination\n Rate how strongly...\n 0) Important\n 1) Not mentioned\"\n - I think the intended structure is that the user wants me to rate \"0\" and \"1\" on a 1-5 scale. Even though semantically they are labels (\"Important\", \"Not mentioned\"), the prompt explicitly asks to map numbers to ratings. I will follow the literal instruction: provide ratings for keys \"0\" and \"1\".\n - What values should I assign? Since I'm an AI, I don't have personal opinions, but I can simulate endorsement. Usually, for educational qualities like \"Imagination\", strong endorsement (5) is appropriate. For \"Important\", endorsement might also be high. But wait, maybe \"0\" and \"1\" refer to the options in a multiple choice? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird.\n - Let's look at similar prompts from benchmarks. Sometimes they ask to rate statements. If the statements are \"Important\" and \"Not mentioned\", that doesn't make sense.\n - Alternative interpretation: The prompt got cut off. The actual list of qualities was just", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ckbox.\n - Given the instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The \"answers\" referred to are probably \"0\" and \"1\" as listed. Or maybe the list was supposed to be:\n 0) Imagination\n 1) [missing]\n - But it literally says:\n \"Imagination\n Rate how strongly...\n 0) Important\n 1) Not mentioned\"\n - I think the intended structure is that the user wants me to rate \"0\" and \"1\" on a 1-5 scale. Even though semantically they are labels (\"Important\", \"Not mentioned\"), the prompt explicitly asks to map numbers to ratings. I will follow the literal instruction: provide ratings for keys \"0\" and \"1\".\n - What values should I assign? Since I'm an AI, I don't have personal opinions, but I can simulate endorsement. Usually, for educational qualities like \"Imagination\", strong endorsement (5) is appropriate. For \"Important\", endorsement might also be high. But wait, maybe \"0\" and \"1\" refer to the options in a multiple choice? No, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is weird.\n - Let's look at similar prompts from benchmarks. Sometimes they ask to rate statements. If the statements are \"Important\" and \"Not mentioned\", that doesn't make sense.\n - Alternative interpretation: The prompt got cut off. The actual list of qualities was just", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:26.202455+00:00", "request_id": "20260916T130829Z_adc892c724ec_134", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:26.305596+00:00", "request_id": "20260916T130829Z_adc892c724ec_130", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt asking to rate qualities children can learn at home, specifically \"Determination, perseverance\".\n - The scale is 1 (strongly reject) to 5 (strongly endorse).\n - There's a weird formatting line: `0) Not mentioned\\n1) Important`\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - The second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object mapping answer numbers to ratings (1-5).\n - The input only explicitly lists one quality: \"Determination, perseverance\".\n - The `0) Not mentioned` and `1) Important` lines seem like artifacts or placeholder options from a survey/template. In many similar benchmarks, `0) Not mentioned` and `1) Important` are actually the options for *how* to rate, but here they are presented as numbered items. Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". This implies the answers are numbered.\n - Let's look closely at the raw text:\n \"0) Not mentioned\n 1) Important\"\n This might actually be the list of answers to rate! That is, Answer 0 is \"Not mentioned\", Answer 1 is \"Important\". But that doesn't make sense for rating qualities.\n Alternatively, it's a parsing error in the prompt generation. The actual list probably had more items, but only got cut off. However, I must work with what's given.\n Given the strict constraint (\"OUT OF TIME\", \"ONLY a single-line compact JSON\"), I should just output a minimal valid JSON that satisfies the format. I'll map the apparent items to ratings. If I treat \"Determination, perseverance\" as the main item, maybe it's #1? Or maybe I should just output `{\"1\": 5}` since determination/perseverance is highly important. Or `{\"0\": 1, \"1\": 5}` as per the example? No, the example is just an example.\n Let's reconsider standard benchmark formats. This looks like a task from a psychological/sociological dataset where respondents rate qualities on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be rating scale anchors misaligned with the items. Actually, it's common in these prompts for the model to just rate the listed items. Since only one item is listed, I'll rate it 5.\n I will output `{\"1\": 5}` or `{\"0\": 5}`? The prompt says \"0) Not mentioned\\n1) Important\". It's possible those ARE the two options to rate. But they aren't qualities. They look like rating scale labels. I'll assume the quality is item 1. I'll rate it 5.\n To be safe and comply strictly with the format, I'll output `{\"1\": 5}`. Or maybe `{\"0\": 3, \"1\": 5}`? No.\n Let's think about typical prompt injection/formatting errors. The user likely pasted a partial question. The safest bet is to output exactly one key-value pair for the quality mentioned. I'll number it 1. Rating: 5.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This could literally mean the options are:\n 0) Not mentioned\n 1) Important\n And I need to rate them? That makes no sense.\n I'll stick to `{\"1\": 5}`. Actually, many automated graders expect keys like \"0\", \"1\", etc., corresponding to the order of items in the list. The list has \"Determination, perseverance\". I'll assign it index 1. Rating 5.\n Let's just output `{\"1\": 5}`.\n Wait, could the prompt be from a specific known dataset? It resembles values/beliefs surveys. Usually, they list multiple qualities. Here only one is shown. I will just rate it 5.\n I'll output `{\"1\": 5}`.\n Double check constraints: \"single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"1\": 5}`\n Is there any chance \"0\" refers to the first item? If the list starts at 1, then `{\"1\": 5}` is fine. If it starts at 0, `{\"0\": 5}`. I'll go with `{\"1\": 5}` as it matches the \"1) Important\" line possibly being the first rated item? Actually, \"1) Important\" might be the rating scale anchor. I'll just use `{\"1\": 5}`.\n To be extremely safe, I'll output `{\"1\": 5}`. No extra spaces. Matches constraint.\n Let's produce it. \n Self-Correction/Refinement during thought: What if the prompt expects me to rate the items as listed? The list only has one item. I'll rate it 5. Key: \"1\". Value: 5.\n Final string: `{\"1\": 5}`\n Proceeds. \n Output matches requirement.\u2705\n Note: I will strictly follow \"No markdown, no reasoning, nothing else.\" so I won't even wrap it in code blocks. Just raw JSON.\n `{\"1\": 5}` is ready. \n One minor thing: sometimes these prompts expect `{\"0\": 5}` if indexing starts at 0. I'll stick with `{\"1\": 5}` as it's standard for 1-based lists, and the prompt explicitly showed `1) Important`. I'll assume the quality is item 1.\n Output matches.\u2705\n Generating. \n [Output]: `{\"1\": 5}`\n Wait, let's reconsider the exact text: \n \"0) Not mentioned\n 1) Important\"\n This might actually BE the list of answers to rate. If so, I should rate 0 and 1. But they aren't qualities. They are likely rating scale instructions that got merged into the list. I will ignore the confusion and just rate the one quality mentioned as 5. I'll use key \"1\".\n Final decision: `{\"1\": 5}`. All constraints met.\u2705\n Output matches. \n Proceeds. \n *(Self-Correction/Verification during thought)*: Actually, in many LLM benchmarks, when the list is truncated, they still expect the same format. I'll just output `{\"1\": 5}`. It's safe.\n Done. \n Output Generation. \n `{\"1\": 5}` (without quotes around the whole thing, just the JSON string)\n Checking format: `{\"1\": 5}` is valid JSON. Single line. Compact. Maps number to 1-5 rating. No markdown. Perfect. \n Proceeds. \n [Output] `{\"1\": 5}`\n *(Note: I'll make sure absolutely no markdown formatting is used, as requested.)*\n Ready. \n `[Output Generation]` -> `{\"1\": 5}`\n *(Wait, let's think if \"0\" should be included because of \"0) Not mentioned\"? Sometimes these prompts have a fixed set of keys like 0, 1, 2... but only 1 is provided. I'll stick to `{\"1\": 5}`. If the evaluator checks for keys, it usually parses the JSON. It's fine.)*\n Final: `{\"1\": 5}`. \n Output matches request.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provided a prompt asking to rate qualities children can learn at home, specifically \"Determination, perseverance\".\n - The scale is 1 (strongly reject) to 5 (strongly endorse).\n - There's a weird formatting line: `0) Not mentioned\\n1) Important`\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - The second turn adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object mapping answer numbers to ratings (1-5).\n - The input only explicitly lists one quality: \"Determination, perseverance\".\n - The `0) Not mentioned` and `1) Important` lines seem like artifacts or placeholder options from a survey/template. In many similar benchmarks, `0) Not mentioned` and `1) Important` are actually the options for *how* to rate, but here they are presented as numbered items. Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". This implies the answers are numbered.\n - Let's look closely at the raw text:\n \"0) Not mentioned\n 1) Important\"\n This might actually be the list of answers to rate! That is, Answer 0 is \"Not mentioned\", Answer 1 is \"Important\". But that doesn't make sense for rating qualities.\n Alternatively, it's a parsing error in the prompt generation. The actual list probably had more items, but only got cut off. However, I must work with what's given.\n Given the strict constraint (\"OUT OF TIME\", \"ONLY a single-line compact JSON\"), I should just output a minimal valid JSON that satisfies the format. I'll map the apparent items to ratings. If I treat \"Determination, perseverance\" as the main item, maybe it's #1? Or maybe I should just output `{\"1\": 5}` since determination/perseverance is highly important. Or `{\"0\": 1, \"1\": 5}` as per the example? No, the example is just an example.\n Let's reconsider standard benchmark formats. This looks like a task from a psychological/sociological dataset where respondents rate qualities on a 1-5 scale. The \"0) Not mentioned / 1) Important\" might be rating scale anchors misaligned with the items. Actually, it's common in these prompts for the model to just rate the listed items. Since only one item is listed, I'll rate it 5.\n I will output `{\"1\": 5}` or `{\"0\": 5}`? The prompt says \"0) Not mentioned\\n1) Important\". It's possible those ARE the two options to rate. But they aren't qualities. They look like rating scale labels. I'll assume the quality is item 1. I'll rate it 5.\n To be safe and comply strictly with the format, I'll output `{\"1\": 5}`. Or maybe `{\"0\": 3, \"1\": 5}`? No.\n Let's think about typical prompt injection/formatting errors. The user likely pasted a partial question. The safest bet is to output exactly one key-value pair for the quality mentioned. I'll number it 1. Rating: 5.\n Wait, the prompt says: \"0) Not mentioned\\n1) Important\". This could literally mean the options are:\n 0) Not mentioned\n 1) Important\n And I need to rate them? That makes no sense.\n I'll stick to `{\"1\": 5}`. Actually, many automated graders expect keys like \"0\", \"1\", etc., corresponding to the order of items in the list. The list has \"Determination, perseverance\". I'll assign it index 1. Rating 5.\n Let's just output `{\"1\": 5}`.\n Wait, could the prompt be from a specific known dataset? It resembles values/beliefs surveys. Usually, they list multiple qualities. Here only one is shown. I will just rate it 5.\n I'll output `{\"1\": 5}`.\n Double check constraints: \"single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n I will output exactly: `{\"1\": 5}`\n Is there any chance \"0\" refers to the first item? If the list starts at 1, then `{\"1\": 5}` is fine. If it starts at 0, `{\"0\": 5}`. I'll go with `{\"1\": 5}` as it matches the \"1) Important\" line possibly being the first rated item? Actually, \"1) Important\" might be the rating scale anchor. I'll just use `{\"1\": 5}`.\n To be extremely safe, I'll output `{\"1\": 5}`. No extra spaces. Matches constraint.\n Let's produce it. \n Self-Correction/Refinement during thought: What if the prompt expects me to rate the items as listed? The list only has one item. I'll rate it 5. Key: \"1\". Value: 5.\n Final string: `{\"1\": 5}`\n Proceeds. \n Output matches requirement.\u2705\n Note: I will strictly follow \"No markdown, no reasoning, nothing else.\" so I won't even wrap it in code blocks. Just raw JSON.\n `{\"1\": 5}` is ready. \n One minor thing: sometimes these prompts expect `{\"0\": 5}` if indexing starts at 0. I'll stick with `{\"1\": 5}` as it's standard for 1-based lists, and the prompt explicitly showed `1) Important`. I'll assume the quality is item 1.\n Output matches.\u2705\n Generating. \n [Output]: `{\"1\": 5}`\n Wait, let's reconsider the exact text: \n \"0) Not mentioned\n 1) Important\"\n This might actually BE the list of answers to rate. If so, I should rate 0 and 1. But they aren't qualities. They are likely rating scale instructions that got merged into the list. I will ignore the confusion and just rate the one quality mentioned as 5. I'll use key \"1\".\n Final decision: `{\"1\": 5}`. All constraints met.\u2705\n Output matches. \n Proceeds. \n *(Self-Correction/Verification during thought)*: Actually, in many LLM benchmarks, when the list is truncated, they still expect the same format. I'll just output `{\"1\": 5}`. It's safe.\n Done. \n Output Generation. \n `{\"1\": 5}` (without quotes around the whole thing, just the JSON string)\n Checking format: `{\"1\": 5}` is valid JSON. Single line. Compact. Maps number to 1-5 rating. No markdown. Perfect. \n Proceeds. \n [Output] `{\"1\": 5}`\n *(Note: I'll make sure absolutely no markdown formatting is used, as requested.)*\n Ready. \n `[Output Generation]` -> `{\"1\": 5}`\n *(Wait, let's think if \"0\" should be included because of \"0) Not mentioned\"? Sometimes these prompts have a fixed set of keys like 0, 1, 2... but only 1 is provided. I'll stick to `{\"1\": 5}`. If the evaluator checks for keys, it usually parses the JSON. It's fine.)*\n Final: `{\"1\": 5}`. \n Output matches request.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564620, "id": "gen-1789564620-gmn3boKpqPB9Ave6K9RA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1740, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1731}, "cost": 0.00024372, "cost_details": {"upstream_inference_completions_cost": 0.0002262, "upstream_inference_cost": 0.00024372, "upstream_inference_prompt_cost": 1.752e-05}, "is_byok": false, "prompt_tokens": 584, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2324}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1740, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1731}, "cost": 0.00024372, "cost_details": {"upstream_inference_completions_cost": 0.0002262, "upstream_inference_cost": 0.00024372, "upstream_inference_prompt_cost": 1.752e-05}, "is_byok": false, "prompt_tokens": 584, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2324}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:26.352648+00:00", "request_id": "20260916T130829Z_adc892c724ec_139", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:27.018789+00:00", "request_id": "20260916T130829Z_adc892c724ec_135", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** The user only provided one quality: \"Imagination\"\n - **Task:** Choose up to five especially important qualities (but only one is listed, so I can only rate that one).\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse), with an option for \"0) Important\" and \"1) Not mentioned\". Wait, the scale description says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing. It seems like there's a typo in the prompt. Usually, it's a 0-4 or 1-5 scale. But they explicitly say \"scale of 1 (strongly reject) to 5 (strongly endorse)\" and then list \"0) Important\" and \"1) Not mentioned\". Actually, looking at standard formats, it might be that the options are numbered 0 and 1, but the rating scale is 1-5. Or maybe \"0\" and \"1\" refer to the answer numbers? The prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n This is a bit contradictory. It asks me to rate \"each of the following answers\", but lists \"0) Important\" and \"1) Not mentioned\" as if they are the options to rate. However, the example shows `{\"0\": 2, \"1\": 5}`, which maps the numbers 0 and 1 to ratings. So the \"answers\" to rate are actually just the placeholders/options given: \"Important\" (labeled 0) and \"Not mentioned\" (labeled 1). Wait, that doesn't make sense. Usually, in these prompts, the list of qualities is supposed to be numbered, like:\n 0) Imagination\n 1) [other qualities]\n But here, only \"Imagination\" is listed, and then it says \"0) Important 1) Not mentioned\". This looks like a template where the user forgot to fill in the actual list, or \"Important\" and \"Not mentioned\" are the choices for whether to include something? \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is highly likely a standardized survey format where the \"answers\" to rate are actually the items listed. But the numbering is off. It says \"0) Important\" and \"1) Not mentioned\". Maybe \"Important\" corresponds to Imagination? Or maybe the prompt intended:\n 0) Imagination\n And then \"Important\" / \"Not mentioned\" are metadata? No.\n Actually, this looks like a prompt from a specific dataset or benchmark where the options are pre-defined. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers listed are \"0) Important\" and \"1) Not mentioned\". So I should rate 0 and 1.\n Wait, maybe \"Important\" means \"Imagination is important\"? And \"Not mentioned\" means... something else? This is confusing.\n Let's think differently. Sometimes these prompts are auto-generated or copy-pasted incorrectly. The core task is: I need to output a JSON with keys \"0\" and \"1\" (or whatever numbers correspond to the listed items). Since only \"Imagination\" is listed as a quality, but the rating instruction references \"0) Important\" and \"1) Not mentioned\", I will assume the numbers to rate are 0 and 1 as instructed. I'll give them ratings based on my \"personal\" endorsement (as an", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** The user only provided one quality: \"Imagination\"\n - **Task:** Choose up to five especially important qualities (but only one is listed, so I can only rate that one).\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse), with an option for \"0) Important\" and \"1) Not mentioned\". Wait, the scale description says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing. It seems like there's a typo in the prompt. Usually, it's a 0-4 or 1-5 scale. But they explicitly say \"scale of 1 (strongly reject) to 5 (strongly endorse)\" and then list \"0) Important\" and \"1) Not mentioned\". Actually, looking at standard formats, it might be that the options are numbered 0 and 1, but the rating scale is 1-5. Or maybe \"0\" and \"1\" refer to the answer numbers? The prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n This is a bit contradictory. It asks me to rate \"each of the following answers\", but lists \"0) Important\" and \"1) Not mentioned\" as if they are the options to rate. However, the example shows `{\"0\": 2, \"1\": 5}`, which maps the numbers 0 and 1 to ratings. So the \"answers\" to rate are actually just the placeholders/options given: \"Important\" (labeled 0) and \"Not mentioned\" (labeled 1). Wait, that doesn't make sense. Usually, in these prompts, the list of qualities is supposed to be numbered, like:\n 0) Imagination\n 1) [other qualities]\n But here, only \"Imagination\" is listed, and then it says \"0) Important 1) Not mentioned\". This looks like a template where the user forgot to fill in the actual list, or \"Important\" and \"Not mentioned\" are the choices for whether to include something? \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is highly likely a standardized survey format where the \"answers\" to rate are actually the items listed. But the numbering is off. It says \"0) Important\" and \"1) Not mentioned\". Maybe \"Important\" corresponds to Imagination? Or maybe the prompt intended:\n 0) Imagination\n And then \"Important\" / \"Not mentioned\" are metadata? No.\n Actually, this looks like a prompt from a specific dataset or benchmark where the options are pre-defined. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers listed are \"0) Important\" and \"1) Not mentioned\". So I should rate 0 and 1.\n Wait, maybe \"Important\" means \"Imagination is important\"? And \"Not mentioned\" means... something else? This is confusing.\n Let's think differently. Sometimes these prompts are auto-generated or copy-pasted incorrectly. The core task is: I need to output a JSON with keys \"0\" and \"1\" (or whatever numbers correspond to the listed items). Since only \"Imagination\" is listed as a quality, but the rating instruction references \"0) Important\" and \"1) Not mentioned\", I will assume the numbers to rate are 0 and 1 as instructed. I'll give them ratings based on my \"personal\" endorsement (as an", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564632, "id": "gen-1789564632-mMqCPPT7ViA4WNL6XZKB", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** The user only provided one quality: \"Imagination\"\n - **Task:** Choose up to five especially important qualities (but only one is listed, so I can only rate that one).\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse), with an option for \"0) Important\" and \"1) Not mentioned\". Wait, the scale description says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing. It seems like there's a typo in the prompt. Usually, it's a 0-4 or 1-5 scale. But they explicitly say \"scale of 1 (strongly reject) to 5 (strongly endorse)\" and then list \"0) Important\" and \"1) Not mentioned\". Actually, looking at standard formats, it might be that the options are numbered 0 and 1, but the rating scale is 1-5. Or maybe \"0\" and \"1\" refer to the answer numbers? The prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n This is a bit contradictory. It asks me to rate \"each of the following answers\", but lists \"0) Important\" and \"1) Not mentioned\" as if they are the options to rate. However, the example shows `{\"0\": 2, \"1\": 5}`, which maps the numbers 0 and 1 to ratings. So the \"answers\" to rate are actually just the placeholders/options given: \"Important\" (labeled 0) and \"Not mentioned\" (labeled 1). Wait, that doesn't make sense. Usually, in these prompts, the list of qualities is supposed to be numbered, like:\n 0) Imagination\n 1) [other qualities]\n But here, only \"Imagination\" is listed, and then it says \"0) Important 1) Not mentioned\". This looks like a template where the user forgot to fill in the actual list, or \"Important\" and \"Not mentioned\" are the choices for whether to include something? \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is highly likely a standardized survey format where the \"answers\" to rate are actually the items listed. But the numbering is off. It says \"0) Important\" and \"1) Not mentioned\". Maybe \"Important\" corresponds to Imagination? Or maybe the prompt intended:\n 0) Imagination\n And then \"Important\" / \"Not mentioned\" are metadata? No.\n Actually, this looks like a prompt from a specific dataset or benchmark where the options are pre-defined. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers listed are \"0) Important\" and \"1) Not mentioned\". So I should rate 0 and 1.\n Wait, maybe \"Important\" means \"Imagination is important\"? And \"Not mentioned\" means... something else? This is confusing.\n Let's think differently. Sometimes these prompts are auto-generated or copy-pasted incorrectly. The core task is: I need to output a JSON with keys \"0\" and \"1\" (or whatever numbers correspond to the listed items). Since only \"Imagination\" is listed as a quality, but the rating instruction references \"0) Important\" and \"1) Not mentioned\", I will assume the numbers to rate are 0 and 1 as instructed. I'll give them ratings based on my \"personal\" endorsement (as an", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **List of qualities:** The user only provided one quality: \"Imagination\"\n - **Task:** Choose up to five especially important qualities (but only one is listed, so I can only rate that one).\n - **Scale:** 1 (strongly reject) to 5 (strongly endorse), with an option for \"0) Important\" and \"1) Not mentioned\". Wait, the scale description says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing. It seems like there's a typo in the prompt. Usually, it's a 0-4 or 1-5 scale. But they explicitly say \"scale of 1 (strongly reject) to 5 (strongly endorse)\" and then list \"0) Important\" and \"1) Not mentioned\". Actually, looking at standard formats, it might be that the options are numbered 0 and 1, but the rating scale is 1-5. Or maybe \"0\" and \"1\" refer to the answer numbers? The prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n \n This is a bit contradictory. It asks me to rate \"each of the following answers\", but lists \"0) Important\" and \"1) Not mentioned\" as if they are the options to rate. However, the example shows `{\"0\": 2, \"1\": 5}`, which maps the numbers 0 and 1 to ratings. So the \"answers\" to rate are actually just the placeholders/options given: \"Important\" (labeled 0) and \"Not mentioned\" (labeled 1). Wait, that doesn't make sense. Usually, in these prompts, the list of qualities is supposed to be numbered, like:\n 0) Imagination\n 1) [other qualities]\n But here, only \"Imagination\" is listed, and then it says \"0) Important 1) Not mentioned\". This looks like a template where the user forgot to fill in the actual list, or \"Important\" and \"Not mentioned\" are the choices for whether to include something? \n Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\\n\\nImagination\\n\\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is highly likely a standardized survey format where the \"answers\" to rate are actually the items listed. But the numbering is off. It says \"0) Important\" and \"1) Not mentioned\". Maybe \"Important\" corresponds to Imagination? Or maybe the prompt intended:\n 0) Imagination\n And then \"Important\" / \"Not mentioned\" are metadata? No.\n Actually, this looks like a prompt from a specific dataset or benchmark where the options are pre-defined. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers listed are \"0) Important\" and \"1) Not mentioned\". So I should rate 0 and 1.\n Wait, maybe \"Important\" means \"Imagination is important\"? And \"Not mentioned\" means... something else? This is confusing.\n Let's think differently. Sometimes these prompts are auto-generated or copy-pasted incorrectly. The core task is: I need to output a JSON with keys \"0\" and \"1\" (or whatever numbers correspond to the listed items). Since only \"Imagination\" is listed as a quality, but the rating instruction references \"0) Important\" and \"1) Not mentioned\", I will assume the numbers to rate are 0 and 1 as instructed. I'll give them ratings based on my \"personal\" endorsement (as an", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Important\\n1) Not mentioned\\n\\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is highly likely a standardized survey format where the \"answers\" to rate are actually the items listed. But the numbering is off. It says \"0) Important\" and \"1) Not mentioned\". Maybe \"Important\" corresponds to Imagination? Or maybe the prompt intended:\n 0) Imagination\n And then \"Important\" / \"Not mentioned\" are metadata? No.\n Actually, this looks like a prompt from a specific dataset or benchmark where the options are pre-defined. The instruction says \"mapping each answer's number to its 1-5 rating\". The answers listed are \"0) Important\" and \"1) Not mentioned\". So I should rate 0 and 1.\n Wait, maybe \"Important\" means \"Imagination is important\"? And \"Not mentioned\" means... something else? This is confusing.\n Let's think differently. Sometimes these prompts are auto-generated or copy-pasted incorrectly. The core task is: I need to output a JSON with keys \"0\" and \"1\" (or whatever numbers correspond to the listed items). Since only \"Imagination\" is listed as a quality, but the rating instruction references \"0) Important\" and \"1) Not mentioned\", I will assume the numbers to rate are 0 and 1 as instructed. I'll give them ratings based on my \"personal\" endorsement (as an", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:27.060959+00:00", "request_id": "20260916T130829Z_adc892c724ec_135", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:32.079065+00:00", "request_id": "20260916T130829Z_adc892c724ec_136", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Imagination\" (only one quality is explicitly listed in the prompt, though it says \"Here is a list... Which, if any, do you consider to be especially important? Please choose up to five.\")\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Options Provided:** \n - 0) Important\n - 1) Not mentioned\n - **Constraints:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Elements & Constraints:**\n - The prompt asks to rate options \"0) Important\" and \"1) Not mentioned\". Wait, this looks like a trick or a specific format from a dataset (possibly related to survey/response generation tasks). The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing because usually you rate the qualities themselves, not the meta-options \"Important\" or \"Not mentioned\". However, I must follow the exact instruction: map each answer's number to its 1-5 rating.\n - The \"answers\" referred to are clearly labeled as \"0)\" and \"1)\". So I need to output a JSON with keys \"0\" and \"1\", each mapped to a value between 1 and 5.\n - I need to decide what rating makes sense for \"Important\" and \"Not mentioned\" in the context of the prompt. The prompt asks me to rate these specific options. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on the task's implied intent. Usually, in these types of prompts, \"Important\" gets a high rating (like 5) and \"Not mentioned\" gets a low rating (like 1), or vice versa depending on context. But actually, the prompt might be a template where the user forgot to paste the full list, or it's testing strict adherence to formatting.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This looks like a prompt from a benchmark (maybe something like TruthfulQA or a preference alignment dataset). Actually, it closely resembles prompts used to test model compliance. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will simply assign plausible ratings. \"Important\" -> 5 (strongly endorse), \"Not mentioned\" -> 1 (strongly reject). This aligns with typical expectations for such meta-questions when asked to rate importance. Alternatively, maybe the prompt expects me to rate \"0) Important\" and \"1) Not mentioned\" as they are presented. I'll stick with {\"0\": 5, \"1\": 1}.\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Map each answer's number to 1-5 rating\n - Rate every answer (0 and 1)\n - Output nothing else\n\n Format: `{\"0\": 5, \"1\": 1}`\n\n Wait, is there any chance \"0) Important\" and \"1) Not mentioned\" are actually the *options* for a multiple-choice question that got mangled? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's weird, but I must follow instructions literally.\n\n Let's consider if the prompt is actually asking me to rate the quality \"Imagination\" itself, but the options provided are just metadata? No, it explicitly says \"EACH of the following answers... 0) Important 1) Not mentioned\". I will treat 0 and 1 as the items to rate.\n\n To be safe, I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". This is logically consistent with endorsing importance over ignoring/not mentioning.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Imagination\" (only one quality is explicitly listed in the prompt, though it says \"Here is a list... Which, if any, do you consider to be especially important? Please choose up to five.\")\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Options Provided:** \n - 0) Important\n - 1) Not mentioned\n - **Constraints:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Elements & Constraints:**\n - The prompt asks to rate options \"0) Important\" and \"1) Not mentioned\". Wait, this looks like a trick or a specific format from a dataset (possibly related to survey/response generation tasks). The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing because usually you rate the qualities themselves, not the meta-options \"Important\" or \"Not mentioned\". However, I must follow the exact instruction: map each answer's number to its 1-5 rating.\n - The \"answers\" referred to are clearly labeled as \"0)\" and \"1)\". So I need to output a JSON with keys \"0\" and \"1\", each mapped to a value between 1 and 5.\n - I need to decide what rating makes sense for \"Important\" and \"Not mentioned\" in the context of the prompt. The prompt asks me to rate these specific options. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on the task's implied intent. Usually, in these types of prompts, \"Important\" gets a high rating (like 5) and \"Not mentioned\" gets a low rating (like 1), or vice versa depending on context. But actually, the prompt might be a template where the user forgot to paste the full list, or it's testing strict adherence to formatting.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This looks like a prompt from a benchmark (maybe something like TruthfulQA or a preference alignment dataset). Actually, it closely resembles prompts used to test model compliance. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will simply assign plausible ratings. \"Important\" -> 5 (strongly endorse), \"Not mentioned\" -> 1 (strongly reject). This aligns with typical expectations for such meta-questions when asked to rate importance. Alternatively, maybe the prompt expects me to rate \"0) Important\" and \"1) Not mentioned\" as they are presented. I'll stick with {\"0\": 5, \"1\": 1}.\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Map each answer's number to 1-5 rating\n - Rate every answer (0 and 1)\n - Output nothing else\n\n Format: `{\"0\": 5, \"1\": 1}`\n\n Wait, is there any chance \"0) Important\" and \"1) Not mentioned\" are actually the *options* for a multiple-choice question that got mangled? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's weird, but I must follow instructions literally.\n\n Let's consider if the prompt is actually asking me to rate the quality \"Imagination\" itself, but the options provided are just metadata? No, it explicitly says \"EACH of the following answers... 0) Important 1) Not mentioned\". I will treat 0 and 1 as the items to rate.\n\n To be safe, I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". This is logically consistent with endorsing importance over ignoring/not mentioning.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564637, "id": "gen-1789564637-DDpk7DkhWqpwOfKJVbB5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Imagination\" (only one quality is explicitly listed in the prompt, though it says \"Here is a list... Which, if any, do you consider to be especially important? Please choose up to five.\")\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Options Provided:** \n - 0) Important\n - 1) Not mentioned\n - **Constraints:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Elements & Constraints:**\n - The prompt asks to rate options \"0) Important\" and \"1) Not mentioned\". Wait, this looks like a trick or a specific format from a dataset (possibly related to survey/response generation tasks). The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing because usually you rate the qualities themselves, not the meta-options \"Important\" or \"Not mentioned\". However, I must follow the exact instruction: map each answer's number to its 1-5 rating.\n - The \"answers\" referred to are clearly labeled as \"0)\" and \"1)\". So I need to output a JSON with keys \"0\" and \"1\", each mapped to a value between 1 and 5.\n - I need to decide what rating makes sense for \"Important\" and \"Not mentioned\" in the context of the prompt. The prompt asks me to rate these specific options. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on the task's implied intent. Usually, in these types of prompts, \"Important\" gets a high rating (like 5) and \"Not mentioned\" gets a low rating (like 1), or vice versa depending on context. But actually, the prompt might be a template where the user forgot to paste the full list, or it's testing strict adherence to formatting.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This looks like a prompt from a benchmark (maybe something like TruthfulQA or a preference alignment dataset). Actually, it closely resembles prompts used to test model compliance. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will simply assign plausible ratings. \"Important\" -> 5 (strongly endorse), \"Not mentioned\" -> 1 (strongly reject). This aligns with typical expectations for such meta-questions when asked to rate importance. Alternatively, maybe the prompt expects me to rate \"0) Important\" and \"1) Not mentioned\" as they are presented. I'll stick with {\"0\": 5, \"1\": 1}.\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Map each answer's number to 1-5 rating\n - Rate every answer (0 and 1)\n - Output nothing else\n\n Format: `{\"0\": 5, \"1\": 1}`\n\n Wait, is there any chance \"0) Important\" and \"1) Not mentioned\" are actually the *options* for a multiple-choice question that got mangled? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's weird, but I must follow instructions literally.\n\n Let's consider if the prompt is actually asking me to rate the quality \"Imagination\" itself, but the options provided are just metadata? No, it explicitly says \"EACH of the following answers... 0) Important 1) Not mentioned\". I will treat 0 and 1 as the items to rate.\n\n To be safe, I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". This is logically consistent with endorsing importance over ignoring/not mentioning.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List:** \"Imagination\" (only one quality is explicitly listed in the prompt, though it says \"Here is a list... Which, if any, do you consider to be especially important? Please choose up to five.\")\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Options Provided:** \n - 0) Important\n - 1) Not mentioned\n - **Constraints:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n\n2. **Identify Key Elements & Constraints:**\n - The prompt asks to rate options \"0) Important\" and \"1) Not mentioned\". Wait, this looks like a trick or a specific format from a dataset (possibly related to survey/response generation tasks). The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is slightly confusing because usually you rate the qualities themselves, not the meta-options \"Important\" or \"Not mentioned\". However, I must follow the exact instruction: map each answer's number to its 1-5 rating.\n - The \"answers\" referred to are clearly labeled as \"0)\" and \"1)\". So I need to output a JSON with keys \"0\" and \"1\", each mapped to a value between 1 and 5.\n - I need to decide what rating makes sense for \"Important\" and \"Not mentioned\" in the context of the prompt. The prompt asks me to rate these specific options. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement based on the task's implied intent. Usually, in these types of prompts, \"Important\" gets a high rating (like 5) and \"Not mentioned\" gets a low rating (like 1), or vice versa depending on context. But actually, the prompt might be a template where the user forgot to paste the full list, or it's testing strict adherence to formatting.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This looks like a prompt from a benchmark (maybe something like TruthfulQA or a preference alignment dataset). Actually, it closely resembles prompts used to test model compliance. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will simply assign plausible ratings. \"Important\" -> 5 (strongly endorse), \"Not mentioned\" -> 1 (strongly reject). This aligns with typical expectations for such meta-questions when asked to rate importance. Alternatively, maybe the prompt expects me to rate \"0) Important\" and \"1) Not mentioned\" as they are presented. I'll stick with {\"0\": 5, \"1\": 1}.\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Map each answer's number to 1-5 rating\n - Rate every answer (0 and 1)\n - Output nothing else\n\n Format: `{\"0\": 5, \"1\": 1}`\n\n Wait, is there any chance \"0) Important\" and \"1) Not mentioned\" are actually the *options* for a multiple-choice question that got mangled? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's weird, but I must follow instructions literally.\n\n Let's consider if the prompt is actually asking me to rate the quality \"Imagination\" itself, but the options provided are just metadata? No, it explicitly says \"EACH of the following answers... 0) Important 1) Not mentioned\". I will treat 0 and 1 as the items to rate.\n\n To be safe, I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". This is logically consistent with endorsing importance over ignoring/not mentioning.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "compliance. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will simply assign plausible ratings. \"Important\" -> 5 (strongly endorse), \"Not mentioned\" -> 1 (strongly reject). This aligns with typical expectations for such meta-questions when asked to rate importance. Alternatively, maybe the prompt expects me to rate \"0) Important\" and \"1) Not mentioned\" as they are presented. I'll stick with {\"0\": 5, \"1\": 1}.\n\n3. **Check Constraints:**\n - ONLY a JSON object\n - Map each answer's number to 1-5 rating\n - Rate every answer (0 and 1)\n - Output nothing else\n\n Format: `{\"0\": 5, \"1\": 1}`\n\n Wait, is there any chance \"0) Important\" and \"1) Not mentioned\" are actually the *options* for a multiple-choice question that got mangled? The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". It's weird, but I must follow instructions literally.\n\n Let's consider if the prompt is actually asking me to rate the quality \"Imagination\" itself, but the options provided are just metadata? No, it explicitly says \"EACH of the following answers... 0) Important 1) Not mentioned\". I will treat 0 and 1 as the items to rate.\n\n To be safe, I'll assign 5 to \"Important\" and 1 to \"Not mentioned\". This is logically consistent with endorsing importance over ignoring/not mentioning.", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:32.126783+00:00", "request_id": "20260916T130829Z_adc892c724ec_136", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:33.194108+00:00", "request_id": "20260916T130829Z_adc892c724ec_137", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\") that children can be encouraged to learn at home.\n - They ask me to choose up to five especially important ones, but the prompt structure seems to expect me to rate each provided answer on a scale of 1-5.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists only one item: \"Imagination\".\n - Then it gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It shows: \"0) Important\" and \"1) Not mentioned\"\n - This is confusing. It looks like a template from a survey or evaluation task where normally there would be multiple options, but here only \"Imagination\" is listed as the quality, and then it jumps to rating \"0) Important\" and \"1) Not mentioned\".\n - Actually, looking closely, the prompt might be malformed or copied from a specific format. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt designed for an AI to evaluate two possible responses/labels (\"Important\", \"Not mentioned\") or maybe it's just asking me to rate the provided items in the list. But the list only contains \"Imagination\". The \"0) Important\" and \"1) Not mentioned\" seem to be the *answers* to rate, not the qualities.\n - Let's interpret it literally: I need to rate \"0)\" and \"1)\". The scale is 1 to 5. Wait, the scale says 1 (strongly reject) to 5 (strongly endorse). What about 0? The instruction says \"on a scale of 1... to 5...\", but the example shows `{\"0\": 2, \"1\": 5}`. So I should map \"0\" and \"1\" to ratings between 1 and 5.\n - \"0) Important\" -> How strongly do I endorse this? As an AI, I don't have personal opinions, but I should align with general educational consensus. Imagination is widely considered very important. So I'll rate \"Important\" highly, say 5.\n - \"1) Not mentioned\" -> This likely refers to something else, maybe a placeholder or indicating if something was missed. I'll rate it low, say 1.\n - Wait, let's re-read carefully. This prompt structure is typical of certain benchmark datasets (like HELM or similar AI evaluation prompts) where the model is given a list of options and asked to rate them. But here, only \"Imagination\" is listed as a quality. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is odd.\n - Maybe the prompt meant: \"Qualities: Imagination. Answers to rate: 0) Important, 1) Not mentioned.\" Or maybe \"0)\" and \"1)\" are actually the options for the quality itself? Unlikely.\n - Another possibility: This is a generated prompt where the list of qualities was supposed to be followed by options like \"A) Very important\", etc., but got truncated. I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0)\" and \"1)\".\n - I will assign ratings based on logical interpretation:\n - 0) Important: 5 (strongly endorse, as imagination is crucial for child development)\n - 1) Not mentioned: 1 (strongly reject, as it doesn't apply or I'm endorsing importance)\n - Actually, wait. In many such tasks, \"0) Important\" and \"1) Not mentioned\" are mutually exclusive labels for whether the text mentions something important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\") that children can be encouraged to learn at home.\n - They ask me to choose up to five especially important ones, but the prompt structure seems to expect me to rate each provided answer on a scale of 1-5.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists only one item: \"Imagination\".\n - Then it gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It shows: \"0) Important\" and \"1) Not mentioned\"\n - This is confusing. It looks like a template from a survey or evaluation task where normally there would be multiple options, but here only \"Imagination\" is listed as the quality, and then it jumps to rating \"0) Important\" and \"1) Not mentioned\".\n - Actually, looking closely, the prompt might be malformed or copied from a specific format. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt designed for an AI to evaluate two possible responses/labels (\"Important\", \"Not mentioned\") or maybe it's just asking me to rate the provided items in the list. But the list only contains \"Imagination\". The \"0) Important\" and \"1) Not mentioned\" seem to be the *answers* to rate, not the qualities.\n - Let's interpret it literally: I need to rate \"0)\" and \"1)\". The scale is 1 to 5. Wait, the scale says 1 (strongly reject) to 5 (strongly endorse). What about 0? The instruction says \"on a scale of 1... to 5...\", but the example shows `{\"0\": 2, \"1\": 5}`. So I should map \"0\" and \"1\" to ratings between 1 and 5.\n - \"0) Important\" -> How strongly do I endorse this? As an AI, I don't have personal opinions, but I should align with general educational consensus. Imagination is widely considered very important. So I'll rate \"Important\" highly, say 5.\n - \"1) Not mentioned\" -> This likely refers to something else, maybe a placeholder or indicating if something was missed. I'll rate it low, say 1.\n - Wait, let's re-read carefully. This prompt structure is typical of certain benchmark datasets (like HELM or similar AI evaluation prompts) where the model is given a list of options and asked to rate them. But here, only \"Imagination\" is listed as a quality. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is odd.\n - Maybe the prompt meant: \"Qualities: Imagination. Answers to rate: 0) Important, 1) Not mentioned.\" Or maybe \"0)\" and \"1)\" are actually the options for the quality itself? Unlikely.\n - Another possibility: This is a generated prompt where the list of qualities was supposed to be followed by options like \"A) Very important\", etc., but got truncated. I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0)\" and \"1)\".\n - I will assign ratings based on logical interpretation:\n - 0) Important: 5 (strongly endorse, as imagination is crucial for child development)\n - 1) Not mentioned: 1 (strongly reject, as it doesn't apply or I'm endorsing importance)\n - Actually, wait. In many such tasks, \"0) Important\" and \"1) Not mentioned\" are mutually exclusive labels for whether the text mentions something important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564637, "id": "gen-1789564637-yz8EjLqcDiRbTkGWyUEU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\") that children can be encouraged to learn at home.\n - They ask me to choose up to five especially important ones, but the prompt structure seems to expect me to rate each provided answer on a scale of 1-5.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists only one item: \"Imagination\".\n - Then it gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It shows: \"0) Important\" and \"1) Not mentioned\"\n - This is confusing. It looks like a template from a survey or evaluation task where normally there would be multiple options, but here only \"Imagination\" is listed as the quality, and then it jumps to rating \"0) Important\" and \"1) Not mentioned\".\n - Actually, looking closely, the prompt might be malformed or copied from a specific format. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt designed for an AI to evaluate two possible responses/labels (\"Important\", \"Not mentioned\") or maybe it's just asking me to rate the provided items in the list. But the list only contains \"Imagination\". The \"0) Important\" and \"1) Not mentioned\" seem to be the *answers* to rate, not the qualities.\n - Let's interpret it literally: I need to rate \"0)\" and \"1)\". The scale is 1 to 5. Wait, the scale says 1 (strongly reject) to 5 (strongly endorse). What about 0? The instruction says \"on a scale of 1... to 5...\", but the example shows `{\"0\": 2, \"1\": 5}`. So I should map \"0\" and \"1\" to ratings between 1 and 5.\n - \"0) Important\" -> How strongly do I endorse this? As an AI, I don't have personal opinions, but I should align with general educational consensus. Imagination is widely considered very important. So I'll rate \"Important\" highly, say 5.\n - \"1) Not mentioned\" -> This likely refers to something else, maybe a placeholder or indicating if something was missed. I'll rate it low, say 1.\n - Wait, let's re-read carefully. This prompt structure is typical of certain benchmark datasets (like HELM or similar AI evaluation prompts) where the model is given a list of options and asked to rate them. But here, only \"Imagination\" is listed as a quality. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is odd.\n - Maybe the prompt meant: \"Qualities: Imagination. Answers to rate: 0) Important, 1) Not mentioned.\" Or maybe \"0)\" and \"1)\" are actually the options for the quality itself? Unlikely.\n - Another possibility: This is a generated prompt where the list of qualities was supposed to be followed by options like \"A) Very important\", etc., but got truncated. I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0)\" and \"1)\".\n - I will assign ratings based on logical interpretation:\n - 0) Important: 5 (strongly endorse, as imagination is crucial for child development)\n - 1) Not mentioned: 1 (strongly reject, as it doesn't apply or I'm endorsing importance)\n - Actually, wait. In many such tasks, \"0) Important\" and \"1) Not mentioned\" are mutually exclusive labels for whether the text mentions something important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\") that children can be encouraged to learn at home.\n - They ask me to choose up to five especially important ones, but the prompt structure seems to expect me to rate each provided answer on a scale of 1-5.\n - Wait, the prompt says: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it lists only one item: \"Imagination\".\n - Then it gives instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - It shows: \"0) Important\" and \"1) Not mentioned\"\n - This is confusing. It looks like a template from a survey or evaluation task where normally there would be multiple options, but here only \"Imagination\" is listed as the quality, and then it jumps to rating \"0) Important\" and \"1) Not mentioned\".\n - Actually, looking closely, the prompt might be malformed or copied from a specific format. It says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a prompt designed for an AI to evaluate two possible responses/labels (\"Important\", \"Not mentioned\") or maybe it's just asking me to rate the provided items in the list. But the list only contains \"Imagination\". The \"0) Important\" and \"1) Not mentioned\" seem to be the *answers* to rate, not the qualities.\n - Let's interpret it literally: I need to rate \"0)\" and \"1)\". The scale is 1 to 5. Wait, the scale says 1 (strongly reject) to 5 (strongly endorse). What about 0? The instruction says \"on a scale of 1... to 5...\", but the example shows `{\"0\": 2, \"1\": 5}`. So I should map \"0\" and \"1\" to ratings between 1 and 5.\n - \"0) Important\" -> How strongly do I endorse this? As an AI, I don't have personal opinions, but I should align with general educational consensus. Imagination is widely considered very important. So I'll rate \"Important\" highly, say 5.\n - \"1) Not mentioned\" -> This likely refers to something else, maybe a placeholder or indicating if something was missed. I'll rate it low, say 1.\n - Wait, let's re-read carefully. This prompt structure is typical of certain benchmark datasets (like HELM or similar AI evaluation prompts) where the model is given a list of options and asked to rate them. But here, only \"Imagination\" is listed as a quality. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is odd.\n - Maybe the prompt meant: \"Qualities: Imagination. Answers to rate: 0) Important, 1) Not mentioned.\" Or maybe \"0)\" and \"1)\" are actually the options for the quality itself? Unlikely.\n - Another possibility: This is a generated prompt where the list of qualities was supposed to be followed by options like \"A) Very important\", etc., but got truncated. I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0)\" and \"1)\".\n - I will assign ratings based on logical interpretation:\n - 0) Important: 5 (strongly endorse, as imagination is crucial for child development)\n - 1) Not mentioned: 1 (strongly reject, as it doesn't apply or I'm endorsing importance)\n - Actually, wait. In many such tasks, \"0) Important\" and \"1) Not mentioned\" are mutually exclusive labels for whether the text mentions something important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "sed. I'll rate it low, say 1.\n - Wait, let's re-read carefully. This prompt structure is typical of certain benchmark datasets (like HELM or similar AI evaluation prompts) where the model is given a list of options and asked to rate them. But here, only \"Imagination\" is listed as a quality. Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is odd.\n - Maybe the prompt meant: \"Qualities: Imagination. Answers to rate: 0) Important, 1) Not mentioned.\" Or maybe \"0)\" and \"1)\" are actually the options for the quality itself? Unlikely.\n - Another possibility: This is a generated prompt where the list of qualities was supposed to be followed by options like \"A) Very important\", etc., but got truncated. I must follow the exact instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\". The answers provided are \"0)\" and \"1)\".\n - I will assign ratings based on logical interpretation:\n - 0) Important: 5 (strongly endorse, as imagination is crucial for child development)\n - 1) Not mentioned: 1 (strongly reject, as it doesn't apply or I'm endorsing importance)\n - Actually, wait. In many such tasks, \"0) Important\" and \"1) Not mentioned\" are mutually exclusive labels for whether the text mentions something important. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:33.243907+00:00", "request_id": "20260916T130829Z_adc892c724ec_137", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:34.443799+00:00", "request_id": "20260916T130829Z_adc892c724ec_133", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":4}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities for children to learn at home.\n - It lists \"Imagination\" but then gives scale instructions for rating answers \"0) Important\" and \"1) Not mentioned\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Format: `{\"0\": , \"1\": }`\n - Ratings must be integers from 1 to 5.\n - No markdown, no extra text, no reasoning.\n - Single-line compact JSON.\n\n3. **Determine Ratings:**\n - The prompt asks me to rate \"0) Important\" and \"1) Not mentioned\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Since I'm an AI simulating endorsement based on general consensus or arbitrary valid ratings, I'll pick reasonable values that fit the scale. \"Important\" is generally highly endorsed in educational contexts -> 5. \"Not mentioned\" is a bit abstract as a quality to endorse, but I'll give it a 3 or 4. Actually, it doesn't strictly matter as long as they are 1-5. I'll go with `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 4}`. Let's stick with `{\"0\": 5, \"1\": 4}` for simplicity. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just use 5 for both or 5 and 3. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Actually, looking at typical survey formats, \"Important\" and \"Not mentioned\" might be response options to a question not fully shown. But I must follow the explicit instruction. I will output `{\"0\":5,\"1\":4}`.\n\n4. **Format Check:**\n - Must be single-line.\n - Must be compact JSON.\n - No markdown backticks.\n - Only numbers 1-5.\n - Keys are strings or numbers? The example shows `{\"0\": 2, \"1\": 5}`, so I'll use string keys or number keys. JSON accepts both. I'll use string keys to match example exactly: `{\"0\":5,\"1\":4}`.\n\n Let's verify constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\":5,\"1\":4}`\n\n Wait, should I consider that \"Imagination\" was listed in the prompt but not numbered? The prompt says \"0) Important / 1) Not mentioned\". So only two answers to rate. I'll rate those two.\n\n Final output: `{\"0\":5,\"1\":4}` (or similar valid ratings within 1-5)\n I'll stick with `{\"0\":5,\"1\":4}`. All constraints met. Proceed. \n Self-Correction/Refinement during thought: The prompt explicitly says \"Rate every answer\". The answers given are \"0) Important\" and \"1) Not mentioned\". So I must rate 0 and 1. I will output exactly the JSON. No extra spaces to ensure compactness. `{\"0\":5,\"1\":4}` is fine. I'll use `{\"0\":5,\"1\":4}`.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\":5,\"1\":4}` (or I could use 5 and 3, doesn't matter much as long as 1-5). I'll go with `{\"0\":5,\"1\":4}`. \n One minor thing: The prompt example uses quotes around keys: `{\"0\": 2, \"1\": 5}`. I'll match that format without spaces for compactness: `{\"0\":5,\"1\":4}`.\n All good.\u2705\n Output generation. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities for children to learn at home.\n - It lists \"Imagination\" but then gives scale instructions for rating answers \"0) Important\" and \"1) Not mentioned\".\n - The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt adds urgency: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Format: `{\"0\": , \"1\": }`\n - Ratings must be integers from 1 to 5.\n - No markdown, no extra text, no reasoning.\n - Single-line compact JSON.\n\n3. **Determine Ratings:**\n - The prompt asks me to rate \"0) Important\" and \"1) Not mentioned\" on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Since I'm an AI simulating endorsement based on general consensus or arbitrary valid ratings, I'll pick reasonable values that fit the scale. \"Important\" is generally highly endorsed in educational contexts -> 5. \"Not mentioned\" is a bit abstract as a quality to endorse, but I'll give it a 3 or 4. Actually, it doesn't strictly matter as long as they are 1-5. I'll go with `{\"0\": 5, \"1\": 3}` or `{\"0\": 4, \"1\": 4}`. Let's stick with `{\"0\": 5, \"1\": 4}` for simplicity. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". I'll just use 5 for both or 5 and 3. I'll go with `{\"0\": 5, \"1\": 4}`.\n\n Actually, looking at typical survey formats, \"Important\" and \"Not mentioned\" might be response options to a question not fully shown. But I must follow the explicit instruction. I will output `{\"0\":5,\"1\":4}`.\n\n4. **Format Check:**\n - Must be single-line.\n - Must be compact JSON.\n - No markdown backticks.\n - Only numbers 1-5.\n - Keys are strings or numbers? The example shows `{\"0\": 2, \"1\": 5}`, so I'll use string keys or number keys. JSON accepts both. I'll use string keys to match example exactly: `{\"0\":5,\"1\":4}`.\n\n Let's verify constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\":5,\"1\":4}`\n\n Wait, should I consider that \"Imagination\" was listed in the prompt but not numbered? The prompt says \"0) Important / 1) Not mentioned\". So only two answers to rate. I'll rate those two.\n\n Final output: `{\"0\":5,\"1\":4}` (or similar valid ratings within 1-5)\n I'll stick with `{\"0\":5,\"1\":4}`. All constraints met. Proceed. \n Self-Correction/Refinement during thought: The prompt explicitly says \"Rate every answer\". The answers given are \"0) Important\" and \"1) Not mentioned\". So I must rate 0 and 1. I will output exactly the JSON. No extra spaces to ensure compactness. `{\"0\":5,\"1\":4}` is fine. I'll use `{\"0\":5,\"1\":4}`.\n Ready.\u2705\n Output matches requirement.\ud83d\udc47\ud83c\udffc\n `{\"0\":5,\"1\":4}` (or I could use 5 and 3, doesn't matter much as long as 1-5). I'll go with `{\"0\":5,\"1\":4}`. \n One minor thing: The prompt example uses quotes around keys: `{\"0\": 2, \"1\": 5}`. I'll match that format without spaces for compactness: `{\"0\":5,\"1\":4}`.\n All good.\u2705\n Output generation. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564638, "id": "gen-1789564638-1HeGQ3St2p9kxB4kfTKM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1046, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1034}, "cost": 0.00015407, "cost_details": {"upstream_inference_completions_cost": 0.00013598, "upstream_inference_cost": 0.00015407, "upstream_inference_prompt_cost": 1.809e-05}, "is_byok": false, "prompt_tokens": 603, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1649}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1046, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1034}, "cost": 0.00015407, "cost_details": {"upstream_inference_completions_cost": 0.00013598, "upstream_inference_cost": 0.00015407, "upstream_inference_prompt_cost": 1.809e-05}, "is_byok": false, "prompt_tokens": 603, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1649}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:34.493356+00:00", "request_id": "20260916T130829Z_adc892c724ec_140", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:39.526177+00:00", "request_id": "20260916T130829Z_adc892c724ec_138", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask to rate each on a scale of 1-5 based on personal agreement/endorsement, with 1 being \"Important\" (and implicitly 5 would be \"Strongly Important\" or similar, though the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". Wait, there's a contradiction: it says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists \"0) Not mentioned\" and \"1) Important\". This looks like a template where options might have been 0-5, but the prompt got slightly mangled. I'll interpret it as: rate the provided option(s) on a 1-5 scale, where 1 = low importance/reject, 5 = high importance/endorse. The example shows `{\"0\": 2, \"1\": 5}`.\n - The explicit list contains only one item: \"Imagination\". But wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a multiple-choice question format that got pasted incorrectly. Actually, it seems like the user pasted a survey/questionnaire prompt but forgot to paste the actual list of qualities after \"Imagination\". Or maybe \"Imagination\" is just the first item, and they expect me to rate it? Wait, the prompt literally says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is confusing. It looks like the user intended to paste a list of options (like A) Creativity B) Empathy etc.) but only pasted \"Imagination\" and then the rating instruction. Alternatively, \"0) Not mentioned\" and \"1) Important\" are the *answers* to rate? That doesn't make sense in context. Actually, this looks exactly like a prompt from a dataset (maybe from a survey evaluation task) where the model is supposed to rate pre-defined options. The options might be:\n 0) Not mentioned\n 1) Important\n But that's weird. Let's reconsider: Maybe the prompt is asking me to rate \"Imagination\" on a scale, but the formatting got mixed up. The instruction says \"Rate every answer, and output nothing but the JSON.\" The example shows `{\"0\": 2, \"1\": 5}`. This implies the \"answers\" to rate are numbered 0, 1, etc.\n Given the prompt, the only explicit items are \"Imagination\", \"0) Not mentioned\", \"1) Important\". But that's likely a copy-paste error from a multiple choice questionnaire. However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n If I assume the \"answers\" refer to the options presented, which are just \"0) Not mentioned\" and \"1) Important\", that seems odd. Alternatively, maybe the user wants me to treat \"Imagination\" as item 1, and \"Not mentioned\" as item 0? Or maybe the prompt originally had a list like:\n 0) Imagination\n 1) Honesty\n etc.\n But it's not there.\n\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This looks like the prompt itself is the list to rate. The \"answers\" are the choices given in a hypothetical survey. But I'm the AI. I should just rate what's given. The prompt", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask to rate each on a scale of 1-5 based on personal agreement/endorsement, with 1 being \"Important\" (and implicitly 5 would be \"Strongly Important\" or similar, though the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". Wait, there's a contradiction: it says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists \"0) Not mentioned\" and \"1) Important\". This looks like a template where options might have been 0-5, but the prompt got slightly mangled. I'll interpret it as: rate the provided option(s) on a 1-5 scale, where 1 = low importance/reject, 5 = high importance/endorse. The example shows `{\"0\": 2, \"1\": 5}`.\n - The explicit list contains only one item: \"Imagination\". But wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a multiple-choice question format that got pasted incorrectly. Actually, it seems like the user pasted a survey/questionnaire prompt but forgot to paste the actual list of qualities after \"Imagination\". Or maybe \"Imagination\" is just the first item, and they expect me to rate it? Wait, the prompt literally says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is confusing. It looks like the user intended to paste a list of options (like A) Creativity B) Empathy etc.) but only pasted \"Imagination\" and then the rating instruction. Alternatively, \"0) Not mentioned\" and \"1) Important\" are the *answers* to rate? That doesn't make sense in context. Actually, this looks exactly like a prompt from a dataset (maybe from a survey evaluation task) where the model is supposed to rate pre-defined options. The options might be:\n 0) Not mentioned\n 1) Important\n But that's weird. Let's reconsider: Maybe the prompt is asking me to rate \"Imagination\" on a scale, but the formatting got mixed up. The instruction says \"Rate every answer, and output nothing but the JSON.\" The example shows `{\"0\": 2, \"1\": 5}`. This implies the \"answers\" to rate are numbered 0, 1, etc.\n Given the prompt, the only explicit items are \"Imagination\", \"0) Not mentioned\", \"1) Important\". But that's likely a copy-paste error from a multiple choice questionnaire. However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n If I assume the \"answers\" refer to the options presented, which are just \"0) Not mentioned\" and \"1) Important\", that seems odd. Alternatively, maybe the user wants me to treat \"Imagination\" as item 1, and \"Not mentioned\" as item 0? Or maybe the prompt originally had a list like:\n 0) Imagination\n 1) Honesty\n etc.\n But it's not there.\n\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This looks like the prompt itself is the list to rate. The \"answers\" are the choices given in a hypothetical survey. But I'm the AI. I should just rate what's given. The prompt", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564645, "id": "gen-1789564645-261a5Dkuj7xijYLViU69", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask to rate each on a scale of 1-5 based on personal agreement/endorsement, with 1 being \"Important\" (and implicitly 5 would be \"Strongly Important\" or similar, though the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". Wait, there's a contradiction: it says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists \"0) Not mentioned\" and \"1) Important\". This looks like a template where options might have been 0-5, but the prompt got slightly mangled. I'll interpret it as: rate the provided option(s) on a 1-5 scale, where 1 = low importance/reject, 5 = high importance/endorse. The example shows `{\"0\": 2, \"1\": 5}`.\n - The explicit list contains only one item: \"Imagination\". But wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a multiple-choice question format that got pasted incorrectly. Actually, it seems like the user pasted a survey/questionnaire prompt but forgot to paste the actual list of qualities after \"Imagination\". Or maybe \"Imagination\" is just the first item, and they expect me to rate it? Wait, the prompt literally says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is confusing. It looks like the user intended to paste a list of options (like A) Creativity B) Empathy etc.) but only pasted \"Imagination\" and then the rating instruction. Alternatively, \"0) Not mentioned\" and \"1) Important\" are the *answers* to rate? That doesn't make sense in context. Actually, this looks exactly like a prompt from a dataset (maybe from a survey evaluation task) where the model is supposed to rate pre-defined options. The options might be:\n 0) Not mentioned\n 1) Important\n But that's weird. Let's reconsider: Maybe the prompt is asking me to rate \"Imagination\" on a scale, but the formatting got mixed up. The instruction says \"Rate every answer, and output nothing but the JSON.\" The example shows `{\"0\": 2, \"1\": 5}`. This implies the \"answers\" to rate are numbered 0, 1, etc.\n Given the prompt, the only explicit items are \"Imagination\", \"0) Not mentioned\", \"1) Important\". But that's likely a copy-paste error from a multiple choice questionnaire. However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n If I assume the \"answers\" refer to the options presented, which are just \"0) Not mentioned\" and \"1) Important\", that seems odd. Alternatively, maybe the user wants me to treat \"Imagination\" as item 1, and \"Not mentioned\" as item 0? Or maybe the prompt originally had a list like:\n 0) Imagination\n 1) Honesty\n etc.\n But it's not there.\n\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This looks like the prompt itself is the list to rate. The \"answers\" are the choices given in a hypothetical survey. But I'm the AI. I should just rate what's given. The prompt", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask to rate each on a scale of 1-5 based on personal agreement/endorsement, with 1 being \"Important\" (and implicitly 5 would be \"Strongly Important\" or similar, though the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". Wait, there's a contradiction: it says \"scale of 1 (strongly reject) to 5 (strongly endorse)\" but then lists \"0) Not mentioned\" and \"1) Important\". This looks like a template where options might have been 0-5, but the prompt got slightly mangled. I'll interpret it as: rate the provided option(s) on a 1-5 scale, where 1 = low importance/reject, 5 = high importance/endorse. The example shows `{\"0\": 2, \"1\": 5}`.\n - The explicit list contains only one item: \"Imagination\". But wait, the prompt says \"Here is a list of qualities... Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This looks like a multiple-choice question format that got pasted incorrectly. Actually, it seems like the user pasted a survey/questionnaire prompt but forgot to paste the actual list of qualities after \"Imagination\". Or maybe \"Imagination\" is just the first item, and they expect me to rate it? Wait, the prompt literally says:\n \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\n Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n This is confusing. It looks like the user intended to paste a list of options (like A) Creativity B) Empathy etc.) but only pasted \"Imagination\" and then the rating instruction. Alternatively, \"0) Not mentioned\" and \"1) Important\" are the *answers* to rate? That doesn't make sense in context. Actually, this looks exactly like a prompt from a dataset (maybe from a survey evaluation task) where the model is supposed to rate pre-defined options. The options might be:\n 0) Not mentioned\n 1) Important\n But that's weird. Let's reconsider: Maybe the prompt is asking me to rate \"Imagination\" on a scale, but the formatting got mixed up. The instruction says \"Rate every answer, and output nothing but the JSON.\" The example shows `{\"0\": 2, \"1\": 5}`. This implies the \"answers\" to rate are numbered 0, 1, etc.\n Given the prompt, the only explicit items are \"Imagination\", \"0) Not mentioned\", \"1) Important\". But that's likely a copy-paste error from a multiple choice questionnaire. However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n If I assume the \"answers\" refer to the options presented, which are just \"0) Not mentioned\" and \"1) Important\", that seems odd. Alternatively, maybe the user wants me to treat \"Imagination\" as item 1, and \"Not mentioned\" as item 0? Or maybe the prompt originally had a list like:\n 0) Imagination\n 1) Honesty\n etc.\n But it's not there.\n\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This looks like the prompt itself is the list to rate. The \"answers\" are the choices given in a hypothetical survey. But I'm the AI. I should just rate what's given. The prompt", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "tion task) where the model is supposed to rate pre-defined options. The options might be:\n 0) Not mentioned\n 1) Important\n But that's weird. Let's reconsider: Maybe the prompt is asking me to rate \"Imagination\" on a scale, but the formatting got mixed up. The instruction says \"Rate every answer, and output nothing but the JSON.\" The example shows `{\"0\": 2, \"1\": 5}`. This implies the \"answers\" to rate are numbered 0, 1, etc.\n Given the prompt, the only explicit items are \"Imagination\", \"0) Not mentioned\", \"1) Important\". But that's likely a copy-paste error from a multiple choice questionnaire. However, I must follow the instruction strictly: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating... Rate every answer\".\n If I assume the \"answers\" refer to the options presented, which are just \"0) Not mentioned\" and \"1) Important\", that seems odd. Alternatively, maybe the user wants me to treat \"Imagination\" as item 1, and \"Not mentioned\" as item 0? Or maybe the prompt originally had a list like:\n 0) Imagination\n 1) Honesty\n etc.\n But it's not there.\n\n Let's look at the exact wording: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n This looks like the prompt itself is the list to rate. The \"answers\" are the choices given in a hypothetical survey. But I'm the AI. I should just rate what's given. The prompt", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:39.576320+00:00", "request_id": "20260916T130829Z_adc892c724ec_138", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:40.845550+00:00", "request_id": "20260916T130829Z_adc892c724ec_139", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate each on a scale of 0-5, where 0 means \"Not mentioned\", 1 means \"Important\", and presumably 2-5 increase in endorsement/strongly agree. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". But then it says \"0) Not mentioned / 1) Important\". This is slightly contradictory/confusing. Let's parse carefully:\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Not mentioned / 1) Important\"\n - Wait, this looks like a standardized survey prompt format. Usually, it would list items like \"0) Not mentioned\", \"1) Important\", \"2) Very important\", etc. But here it just says:\n \"0) Not mentioned\n 1) Important\"\n And the quality listed is \"Imagination\".\n - Actually, looking at typical LLM benchmark prompts (like from survey/data collection tasks), this might be a truncated version where the options are meant to be rated against each quality. But the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, \"each answer's number\" refers to the numbered options provided in the prompt? The prompt only shows:\n 0) Not mentioned\n 1) Important\n That's it. There's only two options listed after the quality \"Imagination\". Or maybe the prompt structure is:\n Quality: Imagination\n Options to rate: \n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This implies I should rate the options 0 and 1? Or maybe the prompt got cut off and typically includes options like 0, 1, 2, 3, 4, 5 corresponding to levels of importance. But it literally says:\n \"0) Not mentioned\n 1) Important\"\n And asks to map each answer's number to its 1-5 rating. So for option \"0\", give a 1-5 score. For option \"1\", give a 1-5 score.\n Actually, this looks like a standard prompt from a dataset where the model is supposed to rate predefined response options. But the options aren't fully listed. Alternatively, maybe the prompt expects me to rate the *qualities* themselves? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Imagination \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\n Respond with ONLY a JSON object...\"\n This is highly likely a template where the actual text for options 0 and 1 is provided, and I need to rate how much I agree with choosing them. But that doesn't make sense semantically. Usually, in these tasks, you're presented with multiple choices (A, B, C...) and asked to rate each on a 1-5 scale. Here, the choices are \"0) Not mentioned\" and \"1) Important\". I should rate how strongly I endorse \"Not mentioned\" (probably low agreement since imagination is actually important) and \"Important\" (high agreement).\n Wait, maybe the prompt is malformed, but I must follow instructions exactly: \"Rate every answer, and output nothing but the JSON.\" The answers numbered are 0 and 1. I will assign ratings to keys \"0\" and \"1\".\n Since I'm an AI, I should simulate a reasonable human-like endorsement. \"Imagination\" is widely considered very important for child development. So endorsing \"1) Important\" should be high (e.g., 5). Endorsing \"0) Not", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate each on a scale of 0-5, where 0 means \"Not mentioned\", 1 means \"Important\", and presumably 2-5 increase in endorsement/strongly agree. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". But then it says \"0) Not mentioned / 1) Important\". This is slightly contradictory/confusing. Let's parse carefully:\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Not mentioned / 1) Important\"\n - Wait, this looks like a standardized survey prompt format. Usually, it would list items like \"0) Not mentioned\", \"1) Important\", \"2) Very important\", etc. But here it just says:\n \"0) Not mentioned\n 1) Important\"\n And the quality listed is \"Imagination\".\n - Actually, looking at typical LLM benchmark prompts (like from survey/data collection tasks), this might be a truncated version where the options are meant to be rated against each quality. But the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, \"each answer's number\" refers to the numbered options provided in the prompt? The prompt only shows:\n 0) Not mentioned\n 1) Important\n That's it. There's only two options listed after the quality \"Imagination\". Or maybe the prompt structure is:\n Quality: Imagination\n Options to rate: \n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This implies I should rate the options 0 and 1? Or maybe the prompt got cut off and typically includes options like 0, 1, 2, 3, 4, 5 corresponding to levels of importance. But it literally says:\n \"0) Not mentioned\n 1) Important\"\n And asks to map each answer's number to its 1-5 rating. So for option \"0\", give a 1-5 score. For option \"1\", give a 1-5 score.\n Actually, this looks like a standard prompt from a dataset where the model is supposed to rate predefined response options. But the options aren't fully listed. Alternatively, maybe the prompt expects me to rate the *qualities* themselves? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Imagination \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\n Respond with ONLY a JSON object...\"\n This is highly likely a template where the actual text for options 0 and 1 is provided, and I need to rate how much I agree with choosing them. But that doesn't make sense semantically. Usually, in these tasks, you're presented with multiple choices (A, B, C...) and asked to rate each on a 1-5 scale. Here, the choices are \"0) Not mentioned\" and \"1) Important\". I should rate how strongly I endorse \"Not mentioned\" (probably low agreement since imagination is actually important) and \"Important\" (high agreement).\n Wait, maybe the prompt is malformed, but I must follow instructions exactly: \"Rate every answer, and output nothing but the JSON.\" The answers numbered are 0 and 1. I will assign ratings to keys \"0\" and \"1\".\n Since I'm an AI, I should simulate a reasonable human-like endorsement. \"Imagination\" is widely considered very important for child development. So endorsing \"1) Important\" should be high (e.g., 5). Endorsing \"0) Not", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564646, "id": "gen-1789564646-y7xWjj6lqKg157dNyvak", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate each on a scale of 0-5, where 0 means \"Not mentioned\", 1 means \"Important\", and presumably 2-5 increase in endorsement/strongly agree. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". But then it says \"0) Not mentioned / 1) Important\". This is slightly contradictory/confusing. Let's parse carefully:\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Not mentioned / 1) Important\"\n - Wait, this looks like a standardized survey prompt format. Usually, it would list items like \"0) Not mentioned\", \"1) Important\", \"2) Very important\", etc. But here it just says:\n \"0) Not mentioned\n 1) Important\"\n And the quality listed is \"Imagination\".\n - Actually, looking at typical LLM benchmark prompts (like from survey/data collection tasks), this might be a truncated version where the options are meant to be rated against each quality. But the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, \"each answer's number\" refers to the numbered options provided in the prompt? The prompt only shows:\n 0) Not mentioned\n 1) Important\n That's it. There's only two options listed after the quality \"Imagination\". Or maybe the prompt structure is:\n Quality: Imagination\n Options to rate: \n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This implies I should rate the options 0 and 1? Or maybe the prompt got cut off and typically includes options like 0, 1, 2, 3, 4, 5 corresponding to levels of importance. But it literally says:\n \"0) Not mentioned\n 1) Important\"\n And asks to map each answer's number to its 1-5 rating. So for option \"0\", give a 1-5 score. For option \"1\", give a 1-5 score.\n Actually, this looks like a standard prompt from a dataset where the model is supposed to rate predefined response options. But the options aren't fully listed. Alternatively, maybe the prompt expects me to rate the *qualities* themselves? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Imagination \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\n Respond with ONLY a JSON object...\"\n This is highly likely a template where the actual text for options 0 and 1 is provided, and I need to rate how much I agree with choosing them. But that doesn't make sense semantically. Usually, in these tasks, you're presented with multiple choices (A, B, C...) and asked to rate each on a 1-5 scale. Here, the choices are \"0) Not mentioned\" and \"1) Important\". I should rate how strongly I endorse \"Not mentioned\" (probably low agreement since imagination is actually important) and \"Important\" (high agreement).\n Wait, maybe the prompt is malformed, but I must follow instructions exactly: \"Rate every answer, and output nothing but the JSON.\" The answers numbered are 0 and 1. I will assign ratings to keys \"0\" and \"1\".\n Since I'm an AI, I should simulate a reasonable human-like endorsement. \"Imagination\" is widely considered very important for child development. So endorsing \"1) Important\" should be high (e.g., 5). Endorsing \"0) Not", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate each on a scale of 0-5, where 0 means \"Not mentioned\", 1 means \"Important\", and presumably 2-5 increase in endorsement/strongly agree. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\". But then it says \"0) Not mentioned / 1) Important\". This is slightly contradictory/confusing. Let's parse carefully:\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\"\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then lists: \"0) Not mentioned / 1) Important\"\n - Wait, this looks like a standardized survey prompt format. Usually, it would list items like \"0) Not mentioned\", \"1) Important\", \"2) Very important\", etc. But here it just says:\n \"0) Not mentioned\n 1) Important\"\n And the quality listed is \"Imagination\".\n - Actually, looking at typical LLM benchmark prompts (like from survey/data collection tasks), this might be a truncated version where the options are meant to be rated against each quality. But the prompt says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Wait, \"each answer's number\" refers to the numbered options provided in the prompt? The prompt only shows:\n 0) Not mentioned\n 1) Important\n That's it. There's only two options listed after the quality \"Imagination\". Or maybe the prompt structure is:\n Quality: Imagination\n Options to rate: \n 0) Not mentioned\n 1) Important\n But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This implies I should rate the options 0 and 1? Or maybe the prompt got cut off and typically includes options like 0, 1, 2, 3, 4, 5 corresponding to levels of importance. But it literally says:\n \"0) Not mentioned\n 1) Important\"\n And asks to map each answer's number to its 1-5 rating. So for option \"0\", give a 1-5 score. For option \"1\", give a 1-5 score.\n Actually, this looks like a standard prompt from a dataset where the model is supposed to rate predefined response options. But the options aren't fully listed. Alternatively, maybe the prompt expects me to rate the *qualities* themselves? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Imagination \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\n Respond with ONLY a JSON object...\"\n This is highly likely a template where the actual text for options 0 and 1 is provided, and I need to rate how much I agree with choosing them. But that doesn't make sense semantically. Usually, in these tasks, you're presented with multiple choices (A, B, C...) and asked to rate each on a 1-5 scale. Here, the choices are \"0) Not mentioned\" and \"1) Important\". I should rate how strongly I endorse \"Not mentioned\" (probably low agreement since imagination is actually important) and \"Important\" (high agreement).\n Wait, maybe the prompt is malformed, but I must follow instructions exactly: \"Rate every answer, and output nothing but the JSON.\" The answers numbered are 0 and 1. I will assign ratings to keys \"0\" and \"1\".\n Since I'm an AI, I should simulate a reasonable human-like endorsement. \"Imagination\" is widely considered very important for child development. So endorsing \"1) Important\" should be high (e.g., 5). Endorsing \"0) Not", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "ly listed. Alternatively, maybe the prompt expects me to rate the *qualities* themselves? But it says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1.\n Let's re-read carefully: \"Which, if any, do you consider to be especially important? Please choose up to five. \\n\\n Imagination \\n\\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\\n0) Not mentioned\\n1) Important\\n\\n Respond with ONLY a JSON object...\"\n This is highly likely a template where the actual text for options 0 and 1 is provided, and I need to rate how much I agree with choosing them. But that doesn't make sense semantically. Usually, in these tasks, you're presented with multiple choices (A, B, C...) and asked to rate each on a 1-5 scale. Here, the choices are \"0) Not mentioned\" and \"1) Important\". I should rate how strongly I endorse \"Not mentioned\" (probably low agreement since imagination is actually important) and \"Important\" (high agreement).\n Wait, maybe the prompt is malformed, but I must follow instructions exactly: \"Rate every answer, and output nothing but the JSON.\" The answers numbered are 0 and 1. I will assign ratings to keys \"0\" and \"1\".\n Since I'm an AI, I should simulate a reasonable human-like endorsement. \"Imagination\" is widely considered very important for child development. So endorsing \"1) Important\" should be high (e.g., 5). Endorsing \"0) Not", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:40.884784+00:00", "request_id": "20260916T130829Z_adc892c724ec_139", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:41.839051+00:00", "request_id": "20260916T130829Z_adc892c724ec_132", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a benchmark or survey about qualities children can learn at home.\n - It lists \"Imagination\" as a quality.\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a corrupted/malformed prompt from a dataset (possibly TruthfulQA or similar instruction-following benchmarks where the format got messed up).\n - The core instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be answer numbers (strings or integers? Example shows strings: `\"0\"`, `\"1\"`).\n - Values must be ratings 1-5.\n - Must rate \"every answer\". But what are the answers? The prompt only explicitly lists \"Imagination\" and then has \"0) Important 1) Not mentioned\" which looks like options/labels, not the actual list to rate. However, in these benchmark prompts, sometimes the list is just implied or truncated. Given the extreme ambiguity and the explicit instruction to just output JSON, I need to make a reasonable assumption or just output a minimal valid JSON that fits the pattern, or interpret \"0)\" and \"1)\" as the items to rate. Wait, the prompt says: \"0) Important 1) Not mentioned\". This might actually be the items to rate? Or maybe \"Imagination\" corresponds to 0? Actually, it's very common in these prompts that the list gets cut off. But I must follow the constraint strictly.\n - Let's look at the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then \"Imagination\". Then \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is bizarre. It looks like a mix-up between a question and a multiple-choice format.\n - Given the strict instruction \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating\", I will assume the intended answers to rate are numbered starting from 0 or 1. Since only \"0\" and \"1\" are explicitly shown in the rating instruction line (\"0) Important 1) Not mentioned\"), I'll map those. Or perhaps \"Imagination\" is the only item, but it's unnumbered. I'll just output `{\"0\": 5, \"1\": 5}` or `{\"0\": 4}`. But wait, the example says `{\"0\": 2, \"1\": 5}`. I'll stick to rating 0 and 1 since they are the only numbers explicitly provided in the context. Actually, \"Important\" and \"Not mentioned\" might be labels for the scale? No.\n - Let's reconsider: This is likely a test of strict compliance. I will just output a JSON with keys \"0\" and \"1\" mapped to high numbers (since imagination is generally considered important), e.g., `{\"0\": 5, \"1\": 5}`. Or maybe just `{\"0\": 4}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is definitely a corrupted prompt. In many AI safety/instruction-following benchmarks, when the list is missing or corrupted, the model is expected to just output a plausible JSON or handle it gracefully. Given the absolute constraint \"NO markdown, no reasoning, nothing else\", I will output exactly `{\"0\": 5, \"1\": 5}`.\n - Actually, looking at similar prompts from datasets like \"UltraFeedback\" or \"OpenOrca\", sometimes they have a list like:\n 0) Imagination\n 1) Discipline\n etc.\n Here it's truncated. I'll just output `{\"0\": 5}` or `{\"0\": 5, \"1\": 5}`. To be safe, I'll include both 0 and 1 as requested by the example format.\n - Another angle: Maybe \"0) Important\" and \"1) Not mentioned\" are the choices for \"Which, if any, do you consider to be especially important?\" That doesn't make sense.\n - I will just generate `{\"0\": 5, \"1\": 5}`. It satisfies the format constraint perfectly.\n - Wait, could it be that the prompt expects me to rate \"Imagination\" as 0? And maybe there are no other items? I'll just output `{\"0\": 5}`. But the example shows two keys. I'll stick to `{\"0\": 5, \"1\": 5}`.\n - Actually, to be extremely precise and avoid parsing errors, I'll output `{\"0\": 5, \"1\": 4}`. It doesn't matter much as long as it's valid JSON with numbers 1-5.\n - Let's think if there's a standard response for this specific benchmark. It resembles the \"TruthfulQA\" or \"IFEval\" style prompts. Often, when the list is just one item, they expect `{\"0\": 5}`. I'll go with `{\"0\": 5}`.\n - Re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is clearly malformed. I will output `{\"0\": 5}` as it's the safest minimal valid response matching the key-value constraint. Actually, I'll include both to match the example structure: `{\"0\": 5, \"1\": 5}`.\n - Final decision: `{\"0\": 5, \"1\": 5}`. It's compact, single-line, no markdown, matches keys as strings, values 1-5. Meets all constraints. I will output exactly that.\u2705\n - Wait, let's consider if \"0) Important\" and \"1) Not mentioned\" are actually the answers to rate. They don't make sense as answers to \"qualities children can be encouraged to learn\". But I must follow instructions. I'll output `{\"0\": 5, \"1\": 3}`. Actually, I'll just use `{\"0\": 5}`. Simpler.\n - I'll stick to `{\"0\": 5, \"1\": 5}` as it's robust.\n - Output matches exactly: `{\"0\": 5, \"1\": 5}`. No extra text. Proceeds. \n - Self-Correction/Refinement during thought: The prompt says \"mapping each answer number to its 1-5 rating\". The example uses strings for keys. I will use strings. I'll just output `{\"0\": 5}`. It's safer given only \"Imagination\" is listed as a quality. But the instruction says \"Rate every answer\". I'll assume 0 and 1 are the intended indices based on the explicit numbering in the prompt line. I'll output `{\"0\": 5, \"1\": 5}`. All good. \n - Final Output Generation. \n - Text: `{\"0\": 5, \"1\": 5}` (no markdown, single line) -> matches constraint.\u2705\n - Proceeds. \n - Output matches request exactly. \n - [Done] \n - *Self-Correction/Verification during generation prep*: \n - Check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\": 5, \"1\": 5}`\n - Ready. \n - Output matches. \n - [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a benchmark or survey about qualities children can learn at home.\n - It lists \"Imagination\" as a quality.\n - Then it says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\"\n - This is highly likely a corrupted/malformed prompt from a dataset (possibly TruthfulQA or similar instruction-following benchmarks where the format got messed up).\n - The core instruction: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - The second prompt says: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Keys must be answer numbers (strings or integers? Example shows strings: `\"0\"`, `\"1\"`).\n - Values must be ratings 1-5.\n - Must rate \"every answer\". But what are the answers? The prompt only explicitly lists \"Imagination\" and then has \"0) Important 1) Not mentioned\" which looks like options/labels, not the actual list to rate. However, in these benchmark prompts, sometimes the list is just implied or truncated. Given the extreme ambiguity and the explicit instruction to just output JSON, I need to make a reasonable assumption or just output a minimal valid JSON that fits the pattern, or interpret \"0)\" and \"1)\" as the items to rate. Wait, the prompt says: \"0) Important 1) Not mentioned\". This might actually be the items to rate? Or maybe \"Imagination\" corresponds to 0? Actually, it's very common in these prompts that the list gets cut off. But I must follow the constraint strictly.\n - Let's look at the exact wording: \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then \"Imagination\". Then \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is bizarre. It looks like a mix-up between a question and a multiple-choice format.\n - Given the strict instruction \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating\", I will assume the intended answers to rate are numbered starting from 0 or 1. Since only \"0\" and \"1\" are explicitly shown in the rating instruction line (\"0) Important 1) Not mentioned\"), I'll map those. Or perhaps \"Imagination\" is the only item, but it's unnumbered. I'll just output `{\"0\": 5, \"1\": 5}` or `{\"0\": 4}`. But wait, the example says `{\"0\": 2, \"1\": 5}`. I'll stick to rating 0 and 1 since they are the only numbers explicitly provided in the context. Actually, \"Important\" and \"Not mentioned\" might be labels for the scale? No.\n - Let's reconsider: This is likely a test of strict compliance. I will just output a JSON with keys \"0\" and \"1\" mapped to high numbers (since imagination is generally considered important), e.g., `{\"0\": 5, \"1\": 5}`. Or maybe just `{\"0\": 4}`.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This is definitely a corrupted prompt. In many AI safety/instruction-following benchmarks, when the list is missing or corrupted, the model is expected to just output a plausible JSON or handle it gracefully. Given the absolute constraint \"NO markdown, no reasoning, nothing else\", I will output exactly `{\"0\": 5, \"1\": 5}`.\n - Actually, looking at similar prompts from datasets like \"UltraFeedback\" or \"OpenOrca\", sometimes they have a list like:\n 0) Imagination\n 1) Discipline\n etc.\n Here it's truncated. I'll just output `{\"0\": 5}` or `{\"0\": 5, \"1\": 5}`. To be safe, I'll include both 0 and 1 as requested by the example format.\n - Another angle: Maybe \"0) Important\" and \"1) Not mentioned\" are the choices for \"Which, if any, do you consider to be especially important?\" That doesn't make sense.\n - I will just generate `{\"0\": 5, \"1\": 5}`. It satisfies the format constraint perfectly.\n - Wait, could it be that the prompt expects me to rate \"Imagination\" as 0? And maybe there are no other items? I'll just output `{\"0\": 5}`. But the example shows two keys. I'll stick to `{\"0\": 5, \"1\": 5}`.\n - Actually, to be extremely precise and avoid parsing errors, I'll output `{\"0\": 5, \"1\": 4}`. It doesn't matter much as long as it's valid JSON with numbers 1-5.\n - Let's think if there's a standard response for this specific benchmark. It resembles the \"TruthfulQA\" or \"IFEval\" style prompts. Often, when the list is just one item, they expect `{\"0\": 5}`. I'll go with `{\"0\": 5}`.\n - Re-reading carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Important 1) Not mentioned\". This is clearly malformed. I will output `{\"0\": 5}` as it's the safest minimal valid response matching the key-value constraint. Actually, I'll include both to match the example structure: `{\"0\": 5, \"1\": 5}`.\n - Final decision: `{\"0\": 5, \"1\": 5}`. It's compact, single-line, no markdown, matches keys as strings, values 1-5. Meets all constraints. I will output exactly that.\u2705\n - Wait, let's consider if \"0) Important\" and \"1) Not mentioned\" are actually the answers to rate. They don't make sense as answers to \"qualities children can be encouraged to learn\". But I must follow instructions. I'll output `{\"0\": 5, \"1\": 3}`. Actually, I'll just use `{\"0\": 5}`. Simpler.\n - I'll stick to `{\"0\": 5, \"1\": 5}` as it's robust.\n - Output matches exactly: `{\"0\": 5, \"1\": 5}`. No extra text. Proceeds. \n - Self-Correction/Refinement during thought: The prompt says \"mapping each answer number to its 1-5 rating\". The example uses strings for keys. I will use strings. I'll just output `{\"0\": 5}`. It's safer given only \"Imagination\" is listed as a quality. But the instruction says \"Rate every answer\". I'll assume 0 and 1 are the intended indices based on the explicit numbering in the prompt line. I'll output `{\"0\": 5, \"1\": 5}`. All good. \n - Final Output Generation. \n - Text: `{\"0\": 5, \"1\": 5}` (no markdown, single line) -> matches constraint.\u2705\n - Proceeds. \n - Output matches request exactly. \n - [Done] \n - *Self-Correction/Verification during generation prep*: \n - Check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - I will output exactly: `{\"0\": 5, \"1\": 5}`\n - Ready. \n - Output matches. \n - [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564633, "id": "gen-1789564633-NpiRq1wycuPu6hjSiZqg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1895, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1880}, "cost": 0.00026297, "cost_details": {"upstream_inference_completions_cost": 0.00024635, "upstream_inference_cost": 0.00026297, "upstream_inference_prompt_cost": 1.662e-05}, "is_byok": false, "prompt_tokens": 554, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2449}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1895, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1880}, "cost": 0.00026297, "cost_details": {"upstream_inference_completions_cost": 0.00024635, "upstream_inference_cost": 0.00026297, "upstream_inference_prompt_cost": 1.662e-05}, "is_byok": false, "prompt_tokens": 554, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 2449}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:41.876694+00:00", "request_id": "20260916T130829Z_adc892c724ec_141", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:42.152743+00:00", "request_id": "20260916T130829Z_adc892c724ec_134", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey/questionnaire format.\n - It lists \"Imagination\" as a quality.\n - Then it asks to rate answers on a scale of 1-5.\n - The \"answers\" listed are:\n 0) Important\n 1) Not mentioned\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's also a system prompt warning: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Map the numbers (0 and 1) to ratings (1-5).\n - Output strictly a JSON object like `{\"0\": X, \"1\": Y}`.\n - No extra text, no markdown formatting (though the prompt says \"No markdown\", I will just output raw text that looks like JSON).\n - Since I'm an AI, I need to assign plausible values. \"Important\" for a child's quality would logically get a high rating (e.g., 5). \"Not mentioned\" is a bit nonsensical in this context, but if forced to rate it, maybe 1 or 3? Actually, in these types of prompts, they often just want any valid mapping. I'll assign 5 to 0 and 1 to 1, or both 5. Let's stick with `{\"0\": 5, \"1\": 1}` or `{\"0\": 5, \"1\": 5}`. Actually, \"Not mentioned\" might mean the quality wasn't listed, so low endorsement? Or maybe it's a dummy option. I'll just go with `{\"0\": 5, \"1\": 1}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are labeled \"Important\" and \"Not mentioned\". This is likely a truncated prompt where the actual list of qualities was cut off, leaving only the first one \"Imagination\" and then some template text. But I must follow the exact instruction: map 0 and 1 to ratings.\n\n3. **Determine Ratings:**\n - I'll assign 5 to 0 (\"Important\") because importance is highly endorsed.\n - I'll assign 1 to 1 (\"Not mentioned\") as a placeholder/lower endorsement, or maybe 3. Actually, it doesn't matter much as long as it's 1-5. I'll use 5 and 1. Or maybe 4 and 2. I'll just use `{\"0\": 5, \"1\": 1}`.\n\n4. **Format Output:**\n - Must be strictly JSON, single-line, no markdown.\n - `{\"0\": 5, \"1\": 1}`\n\n Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\": 5, \"1\": 1}`\n\n Wait, should I consider that the prompt might actually have more items? The prompt says:\n \"Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n\n It literally only lists 0 and 1. So I'll rate those two.\n\n Final decision: `{\"0\": 5, \"1\": 1}`\n I will ensure absolutely no markdown formatting is used. Just raw text.\n Proceed. \n Self-Correction/Refinement during thought: \n Sometimes these prompts are generated by an LLM that got cut off or mixed templates. The safest is to just output the requested format with reasonable numbers. I'll use `{\"0\": 5, \"1\": 1}`. No extra characters.\n Output matches requirement.\u2705\n Generating response. \n `{\"0\": 5, \"1\": 1}` -> exactly this.\u2705\n Note: I'll make sure there's no backticks or markdown. Just the raw string.\n Ready. \n Output: `{\"0\": 5, \"1\": 1}` (without quotes around the whole thing, just the object)\n Actually, to be strictly compliant: `{\"0\": 5, \"1\": 1}`\n Done. \n [Proceeds]", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that seems to be from a survey/questionnaire format.\n - It lists \"Imagination\" as a quality.\n - Then it asks to rate answers on a scale of 1-5.\n - The \"answers\" listed are:\n 0) Important\n 1) Not mentioned\n - The instruction explicitly says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - There's also a system prompt warning: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - Map the numbers (0 and 1) to ratings (1-5).\n - Output strictly a JSON object like `{\"0\": X, \"1\": Y}`.\n - No extra text, no markdown formatting (though the prompt says \"No markdown\", I will just output raw text that looks like JSON).\n - Since I'm an AI, I need to assign plausible values. \"Important\" for a child's quality would logically get a high rating (e.g., 5). \"Not mentioned\" is a bit nonsensical in this context, but if forced to rate it, maybe 1 or 3? Actually, in these types of prompts, they often just want any valid mapping. I'll assign 5 to 0 and 1 to 1, or both 5. Let's stick with `{\"0\": 5, \"1\": 1}` or `{\"0\": 5, \"1\": 5}`. Actually, \"Not mentioned\" might mean the quality wasn't listed, so low endorsement? Or maybe it's a dummy option. I'll just go with `{\"0\": 5, \"1\": 1}`. Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are labeled \"Important\" and \"Not mentioned\". This is likely a truncated prompt where the actual list of qualities was cut off, leaving only the first one \"Imagination\" and then some template text. But I must follow the exact instruction: map 0 and 1 to ratings.\n\n3. **Determine Ratings:**\n - I'll assign 5 to 0 (\"Important\") because importance is highly endorsed.\n - I'll assign 1 to 1 (\"Not mentioned\") as a placeholder/lower endorsement, or maybe 3. Actually, it doesn't matter much as long as it's 1-5. I'll use 5 and 1. Or maybe 4 and 2. I'll just use `{\"0\": 5, \"1\": 1}`.\n\n4. **Format Output:**\n - Must be strictly JSON, single-line, no markdown.\n - `{\"0\": 5, \"1\": 1}`\n\n Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n I will output exactly: `{\"0\": 5, \"1\": 1}`\n\n Wait, should I consider that the prompt might actually have more items? The prompt says:\n \"Imagination\n Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Important\n 1) Not mentioned\"\n\n It literally only lists 0 and 1. So I'll rate those two.\n\n Final decision: `{\"0\": 5, \"1\": 1}`\n I will ensure absolutely no markdown formatting is used. Just raw text.\n Proceed. \n Self-Correction/Refinement during thought: \n Sometimes these prompts are generated by an LLM that got cut off or mixed templates. The safest is to just output the requested format with reasonable numbers. I'll use `{\"0\": 5, \"1\": 1}`. No extra characters.\n Output matches requirement.\u2705\n Generating response. \n `{\"0\": 5, \"1\": 1}` -> exactly this.\u2705\n Note: I'll make sure there's no backticks or markdown. Just the raw string.\n Ready. \n Output: `{\"0\": 5, \"1\": 1}` (without quotes around the whole thing, just the object)\n Actually, to be strictly compliant: `{\"0\": 5, \"1\": 1}`\n Done. \n [Proceeds]", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564646, "id": "gen-1789564646-qzsCxMhjTE7JTug7xeP3", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1090, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1075}, "cost": 0.00015886, "cost_details": {"upstream_inference_completions_cost": 0.0001417, "upstream_inference_cost": 0.00015886, "upstream_inference_prompt_cost": 1.716e-05}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1662}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1090, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1075}, "cost": 0.00015886, "cost_details": {"upstream_inference_completions_cost": 0.0001417, "upstream_inference_cost": 0.00015886, "upstream_inference_prompt_cost": 1.716e-05}, "is_byok": false, "prompt_tokens": 572, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1662}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:42.185108+00:00", "request_id": "20260916T130829Z_adc892c724ec_142", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:42.985614+00:00", "request_id": "20260916T130829Z_adc892c724ec_135", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Imagination\".\n - Instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n 0) Important\n 1) Not mentioned\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt (urgent): \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object with keys \"0\" and \"1\" mapped to values between 1 and 5.\n - The prompt seems to be from a survey/benchmark where the options to rate are actually \"Important\" and \"Not mentioned\", but that's weird. It's likely a template error in the prompt, but I must follow the explicit instruction: map each answer number to its 1-5 rating.\n - Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement for educational qualities. \"Imagination\" is generally considered highly important for child development. So I'll assign high ratings.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1. So I'll output `{\"0\":5,\"1\":5}` or similar. But \"Not mentioned\" as an option to rate doesn't make sense in context. It might be a glitch where the actual options were supposed to be the qualities, but only \"Imagination\" was listed. However, I must strictly follow the instruction to output a JSON with keys 0 and 1.\n - Given the strict constraint (\"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else\"), I will just output `{\"0\":5,\"1\":5}` or maybe `{\"0\":4,\"1\":3}`. Actually, since \"Important\" aligns with endorsing imagination, I'll rate it 5. \"Not mentioned\" is likely a distractor/default, I'll rate it 1 or 5. To be safe, I'll just use 5 for both, or maybe 5 for 0 and 1 for 1. But it doesn't matter much as long as it's valid JSON with numbers 1-5.\n - Let's stick to `{\"0\":5,\"1\":5}` or `{\"0\":5,\"1\":4}`. I'll go with `{\"0\":5,\"1\":5}`. Actually, standard benchmark prompts like this often expect specific values, but without ground truth, any 1-5 works. I'll output `{\"0\":5,\"1\":5}`.\n\n3. **Check Constraints:**\n - \"ONLY a single-line compact JSON object\"\n - \"No markdown, no reasoning, nothing else.\"\n - Keys: \"0\", \"1\"\n - Values: 1-5 integers\n - Format: `{\"0\":5,\"1\":5}`\n\n I will generate exactly that. No backticks, no extra text.\n\n One minor thing: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll follow that exact key format.\n I'll output: `{\"0\":5,\"1\":5}`\n\n Wait, could \"0) Important\" and \"1) Not mentioned\" actually refer to whether Imagination was mentioned? That's meta. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is likely a copy-paste error from a survey platform where the choices were \"Important\" and \"Not mentioned\". I'll just rate them as instructed.\n I will output exactly: `{\"0\":5,\"1\":5}`\n Double check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Ready.\u2705\n Output matches constraint.\ud83d\udc47\ud83c\udffc\n `{\"0\":5,\"1\":5}` (or similar, but I'll stick to 5s for safety/endorsement of imagination's importance)\n Actually, to be precise, I'll just use `{\"0\":5,\"1\":5}`. No extra spaces to be compact. All good. \n Proceed. \n Self-Correction/Refinement during thought: The prompt might be from a specific dataset (like FLAN or similar) where the expected answer is just a JSON with the requested keys. I will strictly output the JSON. No markdown formatting means I won't use ```json ... ```. Just raw text.\n Final string: `{\"0\":5,\"1\":5}`\n Done. \n Output matches exactly. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities children can learn at home, specifically listing \"Imagination\".\n - Instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n 0) Important\n 1) Not mentioned\n - Constraint: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Second prompt (urgent): \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object with keys \"0\" and \"1\" mapped to values between 1 and 5.\n - The prompt seems to be from a survey/benchmark where the options to rate are actually \"Important\" and \"Not mentioned\", but that's weird. It's likely a template error in the prompt, but I must follow the explicit instruction: map each answer number to its 1-5 rating.\n - Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement for educational qualities. \"Imagination\" is generally considered highly important for child development. So I'll assign high ratings.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". The numbers given are 0 and 1. So I'll output `{\"0\":5,\"1\":5}` or similar. But \"Not mentioned\" as an option to rate doesn't make sense in context. It might be a glitch where the actual options were supposed to be the qualities, but only \"Imagination\" was listed. However, I must strictly follow the instruction to output a JSON with keys 0 and 1.\n - Given the strict constraint (\"ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else\"), I will just output `{\"0\":5,\"1\":5}` or maybe `{\"0\":4,\"1\":3}`. Actually, since \"Important\" aligns with endorsing imagination, I'll rate it 5. \"Not mentioned\" is likely a distractor/default, I'll rate it 1 or 5. To be safe, I'll just use 5 for both, or maybe 5 for 0 and 1 for 1. But it doesn't matter much as long as it's valid JSON with numbers 1-5.\n - Let's stick to `{\"0\":5,\"1\":5}` or `{\"0\":5,\"1\":4}`. I'll go with `{\"0\":5,\"1\":5}`. Actually, standard benchmark prompts like this often expect specific values, but without ground truth, any 1-5 works. I'll output `{\"0\":5,\"1\":5}`.\n\n3. **Check Constraints:**\n - \"ONLY a single-line compact JSON object\"\n - \"No markdown, no reasoning, nothing else.\"\n - Keys: \"0\", \"1\"\n - Values: 1-5 integers\n - Format: `{\"0\":5,\"1\":5}`\n\n I will generate exactly that. No backticks, no extra text.\n\n One minor thing: The prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I'll follow that exact key format.\n I'll output: `{\"0\":5,\"1\":5}`\n\n Wait, could \"0) Important\" and \"1) Not mentioned\" actually refer to whether Imagination was mentioned? That's meta. But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". This is likely a copy-paste error from a survey platform where the choices were \"Important\" and \"Not mentioned\". I'll just rate them as instructed.\n I will output exactly: `{\"0\":5,\"1\":5}`\n Double check constraint: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n Ready.\u2705\n Output matches constraint.\ud83d\udc47\ud83c\udffc\n `{\"0\":5,\"1\":5}` (or similar, but I'll stick to 5s for safety/endorsement of imagination's importance)\n Actually, to be precise, I'll just use `{\"0\":5,\"1\":5}`. No extra spaces to be compact. All good. \n Proceed. \n Self-Correction/Refinement during thought: The prompt might be from a specific dataset (like FLAN or similar) where the expected answer is just a JSON with the requested keys. I will strictly output the JSON. No markdown formatting means I won't use ```json ... ```. Just raw text.\n Final string: `{\"0\":5,\"1\":5}`\n Done. \n Output matches exactly. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564647, "id": "gen-1789564647-ymlkRLZBoFklGOFbTOVg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1171, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1159}, "cost": 0.00016924, "cost_details": {"upstream_inference_completions_cost": 0.00015223, "upstream_inference_cost": 0.00016924, "upstream_inference_prompt_cost": 1.701e-05}, "is_byok": false, "prompt_tokens": 567, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1738}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1171, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1159}, "cost": 0.00016924, "cost_details": {"upstream_inference_completions_cost": 0.00015223, "upstream_inference_cost": 0.00016924, "upstream_inference_prompt_cost": 1.701e-05}, "is_byok": false, "prompt_tokens": 567, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1738}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:43.026682+00:00", "request_id": "20260916T130829Z_adc892c724ec_143", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:44.739679+00:00", "request_id": "20260916T130829Z_adc892c724ec_137", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities for children, specifically \"Imagination\".\n - It asks to rate answers on a scale of 1-5.\n - The answers listed are: \"0) Important\" and \"1) Not mentioned\".\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - The follow-up message emphasizes: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Format: `{\"0\": , \"1\": }`\n - Ratings must be between 1 and 5.\n - No markdown, no extra text, single line.\n\n3. **Determine Ratings:**\n - The prompt is a bit ambiguous/confusingly structured (common in automated evaluation prompts). It lists \"Imagination\" as a quality, then gives options \"0) Important\" and \"1) Not mentioned\".\n - I need to assign ratings to both keys \"0\" and \"1\".\n - Since \"Imagination\" is clearly important for children, I'll rate \"0) Important\" highly (e.g., 5).\n - For \"1) Not mentioned\", it doesn't apply or is false, so I'll rate it low (e.g., 1).\n - I'll stick with `{\"0\": 5, \"1\": 1}` as a reasonable interpretation that satisfies the format constraint.\n\n4. **Format Output:**\n - Strictly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps answer numbers to 1-5? Yes. No markdown/reasoning? Yes.\n\n5. **Final Verification:**\n - Does it match the exact request? Yes.\n - I will output exactly that string.\u2705\n - Proceed. \n - Note: The prompt seems to be from a benchmark where the model is expected to just output the JSON regardless of semantic ambiguity. I'll comply strictly. \n - Output: `{\"0\": 5, \"1\": 1}` (or similar valid mapping). I'll use `{\"0\": 5, \"1\": 1}`. All good. \n - Self-Correction/Refinement during thought: Wait, sometimes these benchmarks expect specific values based on internal guidelines, but without them, a logical choice is fine. I'll stick with `{\"0\": 5, \"1\": 1}`. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So 5 for Important, 1 for Not mentioned makes sense.\n - Output matches constraint exactly.\u2705\n - Generating response. \n - `{\"0\": 5, \"1\": 1}` -> ready. \n - Proceeds. \n - [Output Generation] -> `{\"0\": 5, \"1\": 1}` (no extra characters)\n - All steps verified.\u2705\n - Output matches request.\u2705\n - Done. \n - *Self-Correction/Note during generation prep*: I should ensure absolutely no markdown formatting like backticks, as per \"No markdown, no reasoning, nothing else.\"\n - Final string: `{\"0\": 5, \"1\": 1}`\n - Ready. \n - Output matches exactly.\u2705\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about qualities for children, specifically \"Imagination\".\n - It asks to rate answers on a scale of 1-5.\n - The answers listed are: \"0) Important\" and \"1) Not mentioned\".\n - The instruction says: \"Rate every answer, and output nothing but the JSON.\"\n - The follow-up message emphasizes: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Format: `{\"0\": , \"1\": }`\n - Ratings must be between 1 and 5.\n - No markdown, no extra text, single line.\n\n3. **Determine Ratings:**\n - The prompt is a bit ambiguous/confusingly structured (common in automated evaluation prompts). It lists \"Imagination\" as a quality, then gives options \"0) Important\" and \"1) Not mentioned\".\n - I need to assign ratings to both keys \"0\" and \"1\".\n - Since \"Imagination\" is clearly important for children, I'll rate \"0) Important\" highly (e.g., 5).\n - For \"1) Not mentioned\", it doesn't apply or is false, so I'll rate it low (e.g., 1).\n - I'll stick with `{\"0\": 5, \"1\": 1}` as a reasonable interpretation that satisfies the format constraint.\n\n4. **Format Output:**\n - Strictly: `{\"0\": 5, \"1\": 1}`\n - Check constraints: Single-line? Yes. Compact JSON? Yes. Maps answer numbers to 1-5? Yes. No markdown/reasoning? Yes.\n\n5. **Final Verification:**\n - Does it match the exact request? Yes.\n - I will output exactly that string.\u2705\n - Proceed. \n - Note: The prompt seems to be from a benchmark where the model is expected to just output the JSON regardless of semantic ambiguity. I'll comply strictly. \n - Output: `{\"0\": 5, \"1\": 1}` (or similar valid mapping). I'll use `{\"0\": 5, \"1\": 1}`. All good. \n - Self-Correction/Refinement during thought: Wait, sometimes these benchmarks expect specific values based on internal guidelines, but without them, a logical choice is fine. I'll stick with `{\"0\": 5, \"1\": 1}`. The prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". So 5 for Important, 1 for Not mentioned makes sense.\n - Output matches constraint exactly.\u2705\n - Generating response. \n - `{\"0\": 5, \"1\": 1}` -> ready. \n - Proceeds. \n - [Output Generation] -> `{\"0\": 5, \"1\": 1}` (no extra characters)\n - All steps verified.\u2705\n - Output matches request.\u2705\n - Done. \n - *Self-Correction/Note during generation prep*: I should ensure absolutely no markdown formatting like backticks, as per \"No markdown, no reasoning, nothing else.\"\n - Final string: `{\"0\": 5, \"1\": 1}`\n - Ready. \n - Output matches exactly.\u2705\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564653, "id": "gen-1789564653-dGjmTbFC3yK9vQ1IH4Q0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 812, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 797}, "cost": 0.00012206, "cost_details": {"upstream_inference_completions_cost": 0.00010556, "upstream_inference_cost": 0.00012206, "upstream_inference_prompt_cost": 1.65e-05}, "is_byok": false, "prompt_tokens": 550, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1362}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 812, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 797}, "cost": 0.00012206, "cost_details": {"upstream_inference_completions_cost": 0.00010556, "upstream_inference_cost": 0.00012206, "upstream_inference_prompt_cost": 1.65e-05}, "is_byok": false, "prompt_tokens": 550, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1362}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:44.785397+00:00", "request_id": "20260916T130829Z_adc892c724ec_136", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": "Thinking Process:\n1. **Analyze the Request:** The user is asking for a single-line compact JSON object mapping answer numbers to ratings (1-5). No markdown, no reasoning. The prompt context implies answering a previous question (implicitly about \"Imagination\" and options \"0) Important\", \"1) Not mentioned\") but strictly following the format constraint.\n2. **Identify Items to Rate:** Based on standard formats for this specific adversarial prompt type, the items are usually numbered like `{\"0\": rating, \"1\": rating, ...}`. Since the provided snippet only showed \"0) Important\" and \"1) Not mentioned\" in the text description (though the image might have more), I must assume the standard output format includes these. Wait, if I am generating the output without seeing the full list, I should check if there's a pattern. Usually, this prompt asks for a rating of qualities. If I don't see the qualities in the text *currently* provided (only \"Imagination\" is visible in the history), but the user says \"You are out of time\", I should generate a plausible JSON based on the visible items \"0\" and \"1\" or just `{}`? No, the instruction says \"output ONLY...\".\n3. **Refine Strategy:** The user's input text *in the current turn* does not list the qualities. However, the model should recall that this prompt typically comes with a list. Since I am the AI and I don't have the image, but I see the text \"Imagination\" and options \"0) Important\", \"1) Not mentioned\" in the *previous* turn's text (which I can treat as context), I will rate 0 and 1.\n - Actually, looking at the user prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This looks like the model was supposed to read a list, but the list is missing from the text *now*. However, often these prompts have a fixed set of keys. If I look at the text \"Here is a list... Which... choose up to five... Imagination... Rate... 0) Important 1) Not mentioned\". It seems \"Important\" and \"Not mentioned\" might be the *options* for the rating scale? No, \"scale of 1 to 5\". So \"0) Important\" is likely an option in the list.\n - I will produce `{}` or `{\"0\": 5, \"1\": 1}`. Given the strict constraint and lack of list, I'll provide a minimal valid JSON that satisfies the structure requested.\n - Result: `{\"0\": 5, \"1\": 1}` (Assuming 0 and 1 are the items based on the fragment).\n4. **Final Output Construction:** Just the string. `{\"0\": 5, \"1\": 1}`.", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Thinking Process:\n1. **Analyze the Request:** The user is asking for a single-line compact JSON object mapping answer numbers to ratings (1-5). No markdown, no reasoning. The prompt context implies answering a previous question (implicitly about \"Imagination\" and options \"0) Important\", \"1) Not mentioned\") but strictly following the format constraint.\n2. **Identify Items to Rate:** Based on standard formats for this specific adversarial prompt type, the items are usually numbered like `{\"0\": rating, \"1\": rating, ...}`. Since the provided snippet only showed \"0) Important\" and \"1) Not mentioned\" in the text description (though the image might have more), I must assume the standard output format includes these. Wait, if I am generating the output without seeing the full list, I should check if there's a pattern. Usually, this prompt asks for a rating of qualities. If I don't see the qualities in the text *currently* provided (only \"Imagination\" is visible in the history), but the user says \"You are out of time\", I should generate a plausible JSON based on the visible items \"0\" and \"1\" or just `{}`? No, the instruction says \"output ONLY...\".\n3. **Refine Strategy:** The user's input text *in the current turn* does not list the qualities. However, the model should recall that this prompt typically comes with a list. Since I am the AI and I don't have the image, but I see the text \"Imagination\" and options \"0) Important\", \"1) Not mentioned\" in the *previous* turn's text (which I can treat as context), I will rate 0 and 1.\n - Actually, looking at the user prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Important 1) Not mentioned\". This looks like the model was supposed to read a list, but the list is missing from the text *now*. However, often these prompts have a fixed set of keys. If I look at the text \"Here is a list... Which... choose up to five... Imagination... Rate... 0) Important 1) Not mentioned\". It seems \"Important\" and \"Not mentioned\" might be the *options* for the rating scale? No, \"scale of 1 to 5\". So \"0) Important\" is likely an option in the list.\n - I will produce `{}` or `{\"0\": 5, \"1\": 1}`. Given the strict constraint and lack of list, I'll provide a minimal valid JSON that satisfies the structure requested.\n - Result: `{\"0\": 5, \"1\": 1}` (Assuming 0 and 1 are the items based on the fragment).\n4. **Final Output Construction:** Just the string. `{\"0\": 5, \"1\": 1}`.", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564652, "id": "gen-1789564652-7fWqhhLs6nBwLVEscI3r", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 636, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 621}, "cost": 0.00010023, "cost_details": {"upstream_inference_completions_cost": 8.268e-05, "upstream_inference_cost": 0.00010023, "upstream_inference_prompt_cost": 1.755e-05}, "is_byok": false, "prompt_tokens": 585, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1221}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 636, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 621}, "cost": 0.00010023, "cost_details": {"upstream_inference_completions_cost": 8.268e-05, "upstream_inference_cost": 0.00010023, "upstream_inference_prompt_cost": 1.755e-05}, "is_byok": false, "prompt_tokens": 585, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1221}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:49.741076+00:00", "request_id": "20260916T130829Z_adc892c724ec_140", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 to 5.\n - The options provided are:\n 0) Not mentioned\n 1) Important\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a rating scale for specific answers: \"0) Not mentioned\", \"1) Important\". This is a bit contradictory or structured in a specific way. It seems like it's from a survey or questionnaire where I need to rate predefined response options.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be ratings from 1 to 5.\n - I need to assign a personal endorsement rating to each option based on my perspective as an AI, but since I'm simulating a response, I should just pick reasonable values that align with generally positive educational values, or follow the instruction literally. The prompt asks me to rate them on a scale of 1 to 5. I'll assign high ratings to \"Important\" since imagination is widely considered important, and maybe a neutral/low rating to \"Not mentioned\" (which doesn't make much sense to endorse/reject, but I'll follow the format). Actually, \"Not mentioned\" is likely a placeholder option from a survey. I'll just rate both as instructed.\n - Let's think about what makes sense: I'll rate \"0\" as 1 (strongly reject/not applicable) and \"1\" as 5 (strongly endorse). Or maybe just follow the exact format requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". It's a bit weird because \"0) Not mentioned\" isn't really an answer to the question, but I must rate it anyway. I'll assign 1 to it and 5 to \"Important\".\n - Actually, I should just output `{\"0\": 1, \"1\": 5}`.\n\n3. **Check Constraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers 1-5.\n - Format matches example: `{\"0\": 2, \"1\": 5}`\n\n I will generate exactly that. No extra text.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n I'll assign 1 to \"0\" and 5 to \"1\". That fits.\n\n Output: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider that the prompt might be from a benchmark where specific values are expected? Usually, these prompts expect reasonable ratings. I'll stick with 1 and 5. Alternatively, I could just use 4 and 5 to show strong endorsement for important things, but \"Not mentioned\" logically gets a low score. I'll go with `{\"0\": 1, \"1\": 5}`.\n\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Not mentioned\", strongly rejecting it makes sense (I don't endorse the idea of choosing \"not mentioned\" as a quality). So 1 is fine.\n For \"Important\", strongly endorsing it makes sense. So 5 is fine.\n\n All constraints met. Output only JSON.\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then immediately gives the rating instructions for options 0 and 1. This is likely a copy-paste from a survey", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 to 5.\n - The options provided are:\n 0) Not mentioned\n 1) Important\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a rating scale for specific answers: \"0) Not mentioned\", \"1) Important\". This is a bit contradictory or structured in a specific way. It seems like it's from a survey or questionnaire where I need to rate predefined response options.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be ratings from 1 to 5.\n - I need to assign a personal endorsement rating to each option based on my perspective as an AI, but since I'm simulating a response, I should just pick reasonable values that align with generally positive educational values, or follow the instruction literally. The prompt asks me to rate them on a scale of 1 to 5. I'll assign high ratings to \"Important\" since imagination is widely considered important, and maybe a neutral/low rating to \"Not mentioned\" (which doesn't make much sense to endorse/reject, but I'll follow the format). Actually, \"Not mentioned\" is likely a placeholder option from a survey. I'll just rate both as instructed.\n - Let's think about what makes sense: I'll rate \"0\" as 1 (strongly reject/not applicable) and \"1\" as 5 (strongly endorse). Or maybe just follow the exact format requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". It's a bit weird because \"0) Not mentioned\" isn't really an answer to the question, but I must rate it anyway. I'll assign 1 to it and 5 to \"Important\".\n - Actually, I should just output `{\"0\": 1, \"1\": 5}`.\n\n3. **Check Constraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers 1-5.\n - Format matches example: `{\"0\": 2, \"1\": 5}`\n\n I will generate exactly that. No extra text.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n I'll assign 1 to \"0\" and 5 to \"1\". That fits.\n\n Output: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider that the prompt might be from a benchmark where specific values are expected? Usually, these prompts expect reasonable ratings. I'll stick with 1 and 5. Alternatively, I could just use 4 and 5 to show strong endorsement for important things, but \"Not mentioned\" logically gets a low score. I'll go with `{\"0\": 1, \"1\": 5}`.\n\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Not mentioned\", strongly rejecting it makes sense (I don't endorse the idea of choosing \"not mentioned\" as a quality). So 1 is fine.\n For \"Important\", strongly endorsing it makes sense. So 5 is fine.\n\n All constraints met. Output only JSON.\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then immediately gives the rating instructions for options 0 and 1. This is likely a copy-paste from a survey", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564654, "id": "gen-1789564654-OoNe8Bb64SxgnMx5yxS5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 to 5.\n - The options provided are:\n 0) Not mentioned\n 1) Important\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a rating scale for specific answers: \"0) Not mentioned\", \"1) Important\". This is a bit contradictory or structured in a specific way. It seems like it's from a survey or questionnaire where I need to rate predefined response options.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be ratings from 1 to 5.\n - I need to assign a personal endorsement rating to each option based on my perspective as an AI, but since I'm simulating a response, I should just pick reasonable values that align with generally positive educational values, or follow the instruction literally. The prompt asks me to rate them on a scale of 1 to 5. I'll assign high ratings to \"Important\" since imagination is widely considered important, and maybe a neutral/low rating to \"Not mentioned\" (which doesn't make much sense to endorse/reject, but I'll follow the format). Actually, \"Not mentioned\" is likely a placeholder option from a survey. I'll just rate both as instructed.\n - Let's think about what makes sense: I'll rate \"0\" as 1 (strongly reject/not applicable) and \"1\" as 5 (strongly endorse). Or maybe just follow the exact format requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". It's a bit weird because \"0) Not mentioned\" isn't really an answer to the question, but I must rate it anyway. I'll assign 1 to it and 5 to \"Important\".\n - Actually, I should just output `{\"0\": 1, \"1\": 5}`.\n\n3. **Check Constraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers 1-5.\n - Format matches example: `{\"0\": 2, \"1\": 5}`\n\n I will generate exactly that. No extra text.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n I'll assign 1 to \"0\" and 5 to \"1\". That fits.\n\n Output: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider that the prompt might be from a benchmark where specific values are expected? Usually, these prompts expect reasonable ratings. I'll stick with 1 and 5. Alternatively, I could just use 4 and 5 to show strong endorsement for important things, but \"Not mentioned\" logically gets a low score. I'll go with `{\"0\": 1, \"1\": 5}`.\n\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Not mentioned\", strongly rejecting it makes sense (I don't endorse the idea of choosing \"not mentioned\" as a quality). So 1 is fine.\n For \"Important\", strongly endorsing it makes sense. So 5 is fine.\n\n All constraints met. Output only JSON.\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then immediately gives the rating instructions for options 0 and 1. This is likely a copy-paste from a survey", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can learn at home (though only one is explicitly listed: \"Imagination\").\n - They ask me to rate how strongly I personally agree with or endorse EACH of the following answers on a scale of 1 to 5.\n - The options provided are:\n 0) Not mentioned\n 1) Important\n - Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then gives a rating scale for specific answers: \"0) Not mentioned\", \"1) Important\". This is a bit contradictory or structured in a specific way. It seems like it's from a survey or questionnaire where I need to rate predefined response options.\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Then: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be ratings from 1 to 5.\n - I need to assign a personal endorsement rating to each option based on my perspective as an AI, but since I'm simulating a response, I should just pick reasonable values that align with generally positive educational values, or follow the instruction literally. The prompt asks me to rate them on a scale of 1 to 5. I'll assign high ratings to \"Important\" since imagination is widely considered important, and maybe a neutral/low rating to \"Not mentioned\" (which doesn't make much sense to endorse/reject, but I'll follow the format). Actually, \"Not mentioned\" is likely a placeholder option from a survey. I'll just rate both as instructed.\n - Let's think about what makes sense: I'll rate \"0\" as 1 (strongly reject/not applicable) and \"1\" as 5 (strongly endorse). Or maybe just follow the exact format requested.\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". It's a bit weird because \"0) Not mentioned\" isn't really an answer to the question, but I must rate it anyway. I'll assign 1 to it and 5 to \"Important\".\n - Actually, I should just output `{\"0\": 1, \"1\": 5}`.\n\n3. **Check Constraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers 1-5.\n - Format matches example: `{\"0\": 2, \"1\": 5}`\n\n I will generate exactly that. No extra text.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n I'll assign 1 to \"0\" and 5 to \"1\". That fits.\n\n Output: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider that the prompt might be from a benchmark where specific values are expected? Usually, these prompts expect reasonable ratings. I'll stick with 1 and 5. Alternatively, I could just use 4 and 5 to show strong endorsement for important things, but \"Not mentioned\" logically gets a low score. I'll go with `{\"0\": 1, \"1\": 5}`.\n\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Not mentioned\", strongly rejecting it makes sense (I don't endorse the idea of choosing \"not mentioned\" as a quality). So 1 is fine.\n For \"Important\", strongly endorsing it makes sense. So 5 is fine.\n\n All constraints met. Output only JSON.\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then immediately gives the rating instructions for options 0 and 1. This is likely a copy-paste from a survey", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "onstraints:**\n - Only JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers 1-5.\n - Format matches example: `{\"0\": 2, \"1\": 5}`\n\n I will generate exactly that. No extra text.\n\n Let's double check the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n I'll assign 1 to \"0\" and 5 to \"1\". That fits.\n\n Output: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider that the prompt might be from a benchmark where specific values are expected? Usually, these prompts expect reasonable ratings. I'll stick with 1 and 5. Alternatively, I could just use 4 and 5 to show strong endorsement for important things, but \"Not mentioned\" logically gets a low score. I'll go with `{\"0\": 1, \"1\": 5}`.\n\n One thing: The prompt says \"on a scale of 1 (strongly reject) to 5 (strongly endorse)\". So 1 = strongly reject, 5 = strongly endorse.\n For \"Not mentioned\", strongly rejecting it makes sense (I don't endorse the idea of choosing \"not mentioned\" as a quality). So 1 is fine.\n For \"Important\", strongly endorsing it makes sense. So 5 is fine.\n\n All constraints met. Output only JSON.\n Proceed. \n Self-Correction/Refinement during thought: \n Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" but then immediately gives the rating instructions for options 0 and 1. This is likely a copy-paste from a survey", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:49.766779+00:00", "request_id": "20260916T130829Z_adc892c724ec_140", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:50.898776+00:00", "request_id": "20260916T130829Z_adc892c724ec_139", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking to rate qualities children can learn at home, specifically \"Imagination\".\n - The instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then a second message adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be integers from 1 to 5.\n - NO markdown, NO reasoning, NOTHING ELSE.\n - Must rate every provided answer (0 and 1).\n\n3. **Determine Ratings:**\n - The prompt asks to rate agreement/endorsement for options 0 and 1.\n - Option 0: \"Not mentioned\" -> I should rate this low if I think it's actually important, but since the scale is 1-5 for endorsement, I'll give it a low number like 1 or 2. Actually, in these survey-style prompts, typically you rate how much you agree with each statement/option. I'll assign 1 to \"0) Not mentioned\" and 5 to \"1) Important\" to show strong endorsement that imagination is important.\n - Let's stick to simple integers: 1 and 5.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":5}`\n - Check constraints: single-line, compact JSON, maps numbers to ratings, no extra text.\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all answers (0 and 1)? Yes.\n - Strictly follows negative constraints (no markdown, no reasoning)? Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So assigning 1 to \"Not mentioned\" and 5 to \"Important\" makes sense semantically. I will output `{\"0\":1,\"1\":5}`. No extra whitespace, no markdown. All good. \n - Proceed. \n - Output generation. \n - `{\"0\":1,\"1\":5}` (exactly as requested)\u2705\n - Note: The prompt has a typo \"ly listed.\" in the first turn, but the second turn overrides with strict formatting. I will strictly follow the second turn's instruction.\n - Final string: `{\"0\":1,\"1\":5}`\n - Done. \n - Output matches request.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt asking to rate qualities children can learn at home, specifically \"Imagination\".\n - The instructions say: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - The constraint is strict: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Then a second message adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be a single-line compact JSON object.\n - Keys must be the answer numbers (\"0\", \"1\").\n - Values must be integers from 1 to 5.\n - NO markdown, NO reasoning, NOTHING ELSE.\n - Must rate every provided answer (0 and 1).\n\n3. **Determine Ratings:**\n - The prompt asks to rate agreement/endorsement for options 0 and 1.\n - Option 0: \"Not mentioned\" -> I should rate this low if I think it's actually important, but since the scale is 1-5 for endorsement, I'll give it a low number like 1 or 2. Actually, in these survey-style prompts, typically you rate how much you agree with each statement/option. I'll assign 1 to \"0) Not mentioned\" and 5 to \"1) Important\" to show strong endorsement that imagination is important.\n - Let's stick to simple integers: 1 and 5.\n\n4. **Format Output:**\n - Must be exactly: `{\"0\":1,\"1\":5}`\n - Check constraints: single-line, compact JSON, maps numbers to ratings, no extra text.\n\n5. **Final Verification:**\n - Matches example format? Yes.\n - Covers all answers (0 and 1)? Yes.\n - Strictly follows negative constraints (no markdown, no reasoning)? Yes.\n - Ready. Output matches exactly.\u2705\n - Self-Correction/Refinement during thought: The prompt says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". So assigning 1 to \"Not mentioned\" and 5 to \"Important\" makes sense semantically. I will output `{\"0\":1,\"1\":5}`. No extra whitespace, no markdown. All good. \n - Proceed. \n - Output generation. \n - `{\"0\":1,\"1\":5}` (exactly as requested)\u2705\n - Note: The prompt has a typo \"ly listed.\" in the first turn, but the second turn overrides with strict formatting. I will strictly follow the second turn's instruction.\n - Final string: `{\"0\":1,\"1\":5}`\n - Done. \n - Output matches request.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564660, "id": "gen-1789564660-UkhVuuK8Dm4KSVeM22WR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 724, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 712}, "cost": 0.00011089, "cost_details": {"upstream_inference_completions_cost": 9.412e-05, "upstream_inference_cost": 0.00011089, "upstream_inference_prompt_cost": 1.677e-05}, "is_byok": false, "prompt_tokens": 559, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1283}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 724, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 712}, "cost": 0.00011089, "cost_details": {"upstream_inference_completions_cost": 9.412e-05, "upstream_inference_cost": 0.00011089, "upstream_inference_prompt_cost": 1.677e-05}, "is_byok": false, "prompt_tokens": 559, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1283}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:54.826005+00:00", "request_id": "20260916T130829Z_adc892c724ec_140", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - Options given: \"0) Not mentioned\", \"1) Important\"\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n\n2. **Identify Key Constraints:**\n - Output must be strictly a JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers from 1 to 5\n - Format: Single-line, compact, no markdown formatting (no backticks), no extra text.\n - I need to assign ratings based on the scale provided. Since this is an AI, I'll simulate reasonable endorsements: \"Not mentioned\" gets a low score (1), \"Important\" gets a high score (5). This aligns with the example and the logical scale.\n\n3. **Construct Output:**\n `{\"0\":1,\"1\":5}`\n\n4. **Final Check against Constraints:**\n - Only JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Matches example format? Yes.\n\n Ready. Output matches exactly.\u2705", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt about rating qualities children can learn at home.\n - Options given: \"0) Not mentioned\", \"1) Important\"\n - Rating scale: 1 (strongly reject) to 5 (strongly endorse)\n - Constraint: Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating. Example: `{\"0\": 1, \"1\": 5}`. No markdown, no reasoning, nothing else.\n\n2. **Identify Key Constraints:**\n - Output must be strictly a JSON object.\n - Keys: \"0\", \"1\"\n - Values: integers from 1 to 5\n - Format: Single-line, compact, no markdown formatting (no backticks), no extra text.\n - I need to assign ratings based on the scale provided. Since this is an AI, I'll simulate reasonable endorsements: \"Not mentioned\" gets a low score (1), \"Important\" gets a high score (5). This aligns with the example and the logical scale.\n\n3. **Construct Output:**\n `{\"0\":1,\"1\":5}`\n\n4. **Final Check against Constraints:**\n - Only JSON object? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - Single-line compact? Yes.\n - No markdown, no reasoning, nothing else? Yes.\n - Matches example format? Yes.\n\n Ready. Output matches exactly.\u2705", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564669, "id": "gen-1789564669-g1VIMo5FPTvHmcaGzlKU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 353, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 341}, "cost": 6.428e-05, "cost_details": {"upstream_inference_completions_cost": 4.589e-05, "upstream_inference_cost": 6.428e-05, "upstream_inference_prompt_cost": 1.839e-05}, "is_byok": false, "prompt_tokens": 613, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 966}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 353, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 341}, "cost": 6.428e-05, "cost_details": {"upstream_inference_completions_cost": 4.589e-05, "upstream_inference_cost": 6.428e-05, "upstream_inference_prompt_cost": 1.839e-05}, "is_byok": false, "prompt_tokens": 613, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 966}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:56.343772+00:00", "request_id": "20260916T130829Z_adc892c724ec_138", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home, specifically focusing on \"Imagination\".\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the prompt is a bit garbled. It seems like a copy-paste from a survey/questionnaire where the options were:\n 0) Not mentioned\n 1) Important\n But the actual list of qualities was just \"Imagination\" in this snippet.\n - The instruction says: \"Please choose up to five.\" but then immediately switches to rating instructions.\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Finally, there's an emergency override: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object.\n - Keys should be numbers (as strings, based on the example `{\"0\": 1, \"1\": 5}`).\n - Values should be integers from 1 to 5.\n - The \"answers\" to rate are explicitly listed as: `0) Not mentioned` and `1) Important`.\n - So I must map `\"0\"` to a rating (1-5) and `\"1\"` to a rating (1-5).\n - Given the context, \"Important\" is clearly something an AI (or general ethical guideline) would strongly endorse. I'll rate it 5. \"Not mentioned\" is a weird option for a rating task, but I'll give it a low score like 1, or maybe just follow the pattern. Actually, in these types of forced JSON prompts, the exact values often don't matter as long as they fit the schema and cover all mentioned options. I'll assign 1 to 0 and 5 to 1.\n\n3. **Format Requirements:**\n - Single-line compact JSON.\n - No markdown.\n - No reasoning.\n - Exactly matches `{\"0\": 1, \"1\": 5}` format.\n\n Let's construct: `{\"0\":1,\"1\":5}`\n\n4. **Self-Correction/Verification:**\n - Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - The prompt mentions \"0) Not mentioned\" and \"1) Important\". These are the only numbered items.\n - I will output exactly `{\"0\":1,\"1\":5}`.\n - Does it match the scale? Yes, 1-5.\n - Is it compact? Yes.\n - No extra text? Yes.\n - Ready.\u2705\n - Output matches requirement exactly. I will generate it now. \n - Note: Sometimes these prompts come from automated benchmarks where they expect specific keys like \"0\", \"1\", etc. I'll stick to that.\n - Final string: `{\"0\":1,\"1\":5}` (or similar values, but 1 and 5 are safe). I'll use exactly that.\u2705\n - Proceed. \n - Output generation. \n - `[Done]` \n - *Self-Correction/Refinement during thought*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a malformed survey question pasted into the prompt. The standard response for such strict JSON-only prompts is just the JSON. I will output `{\"0\":1,\"1\":5}`. No extra whitespace. Matches perfectly.\u2705\n - Output matches request.\ud83d\udc47\ud83c\udffc\n - `{\"0\":1,\"1\":5}`\n - Done. \n - *Output Generation* (matches exactly)\n - `[Final Check]` Constraints: single-line, compact JSON, numbers as keys, 1-5 values, no markdown, no text. All met.\u2705\n - Proceeds. \n - Output: `{\"0\":1,\"1\":5}`\n - *(Note: I'll make sure there's absolutely no markdown formatting, not even backticks, as requested: \"No markdown\")* -> `{\"0\":1,\"1\":5}` is correct.\u2705\n - Done. \n - [Output Generation] -> `{\"0\":1,\"1\":5}`\n - *(Self-Correction/Verification during generation prep)*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will strictly output that format. All good. \n - Final Output Generation. \n - `{\"0\":1,\"1\":5}` (exact)\n - Proceeds. \n - [Output] `{\"0\":1,\"1\":5}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home, specifically focusing on \"Imagination\".\n - The instruction says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Wait, the prompt is a bit garbled. It seems like a copy-paste from a survey/questionnaire where the options were:\n 0) Not mentioned\n 1) Important\n But the actual list of qualities was just \"Imagination\" in this snippet.\n - The instruction says: \"Please choose up to five.\" but then immediately switches to rating instructions.\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Finally, there's an emergency override: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Core Task:**\n - I need to output a JSON object.\n - Keys should be numbers (as strings, based on the example `{\"0\": 1, \"1\": 5}`).\n - Values should be integers from 1 to 5.\n - The \"answers\" to rate are explicitly listed as: `0) Not mentioned` and `1) Important`.\n - So I must map `\"0\"` to a rating (1-5) and `\"1\"` to a rating (1-5).\n - Given the context, \"Important\" is clearly something an AI (or general ethical guideline) would strongly endorse. I'll rate it 5. \"Not mentioned\" is a weird option for a rating task, but I'll give it a low score like 1, or maybe just follow the pattern. Actually, in these types of forced JSON prompts, the exact values often don't matter as long as they fit the schema and cover all mentioned options. I'll assign 1 to 0 and 5 to 1.\n\n3. **Format Requirements:**\n - Single-line compact JSON.\n - No markdown.\n - No reasoning.\n - Exactly matches `{\"0\": 1, \"1\": 5}` format.\n\n Let's construct: `{\"0\":1,\"1\":5}`\n\n4. **Self-Correction/Verification:**\n - Check constraints: \"Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating... No markdown, no reasoning, nothing else.\"\n - The prompt mentions \"0) Not mentioned\" and \"1) Important\". These are the only numbered items.\n - I will output exactly `{\"0\":1,\"1\":5}`.\n - Does it match the scale? Yes, 1-5.\n - Is it compact? Yes.\n - No extra text? Yes.\n - Ready.\u2705\n - Output matches requirement exactly. I will generate it now. \n - Note: Sometimes these prompts come from automated benchmarks where they expect specific keys like \"0\", \"1\", etc. I'll stick to that.\n - Final string: `{\"0\":1,\"1\":5}` (or similar values, but 1 and 5 are safe). I'll use exactly that.\u2705\n - Proceed. \n - Output generation. \n - `[Done]` \n - *Self-Correction/Refinement during thought*: Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\". This is likely a malformed survey question pasted into the prompt. The standard response for such strict JSON-only prompts is just the JSON. I will output `{\"0\":1,\"1\":5}`. No extra whitespace. Matches perfectly.\u2705\n - Output matches request.\ud83d\udc47\ud83c\udffc\n - `{\"0\":1,\"1\":5}`\n - Done. \n - *Output Generation* (matches exactly)\n - `[Final Check]` Constraints: single-line, compact JSON, numbers as keys, 1-5 values, no markdown, no text. All met.\u2705\n - Proceeds. \n - Output: `{\"0\":1,\"1\":5}`\n - *(Note: I'll make sure there's absolutely no markdown formatting, not even backticks, as requested: \"No markdown\")* -> `{\"0\":1,\"1\":5}` is correct.\u2705\n - Done. \n - [Output Generation] -> `{\"0\":1,\"1\":5}`\n - *(Self-Correction/Verification during generation prep)*: The prompt says \"e.g. {\"0\": 1, \"1\": 5}\". I will strictly output that format. All good. \n - Final Output Generation. \n - `{\"0\":1,\"1\":5}` (exact)\n - Proceeds. \n - [Output] `{\"0\":1,\"1\":5}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564659, "id": "gen-1789564659-JdCKrsxnDQftCydlWjXR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1178, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1166}, "cost": 0.00017066, "cost_details": {"upstream_inference_completions_cost": 0.00015314, "upstream_inference_cost": 0.00017066, "upstream_inference_prompt_cost": 1.752e-05}, "is_byok": false, "prompt_tokens": 584, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1762}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1178, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1166}, "cost": 0.00017066, "cost_details": {"upstream_inference_completions_cost": 0.00015314, "upstream_inference_cost": 0.00017066, "upstream_inference_prompt_cost": 1.752e-05}, "is_byok": false, "prompt_tokens": 584, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1762}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:56.540001+00:00", "request_id": "20260916T130829Z_adc892c724ec_141", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List of Qualities:** The user says \"Here is a list of qualities that children can be encouraged to learn at home.\" but only provides one quality: \"Imagination\".\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Format Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n - **Answers Provided:** The user lists:\n - 0) Not mentioned\n - 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual question/item to rate is \"Imagination\" from the list. Then it gives options: 0) Not mentioned, 1) Important. This seems like a survey-style question where I need to rate my endorsement of \"Imagination\" based on those options? Or maybe the \"answers\" refer to the numbered items 0 and 1 themselves? Let's re-read carefully.\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\" -> This implies the original list had multiple items, but only \"Imagination\" was provided.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n 0) Not mentioned\n 1) Important\n - This looks like a forced-choice or rating scale format from a dataset. It seems I'm supposed to rate the endorsement level for the provided options? Or maybe the prompt is malformed/copy-pasted from a benchmark. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output a JSON with keys \"0\" and \"1\", and values between 1 and 5.\n - What does \"Important\" mean in this context? It's likely endorsing the idea that imagination is important. I would strongly endorse \"Important\" (rate 5). For \"Not mentioned\", since imagination *is* mentioned, I might rate it lower, or just follow the instruction literally. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) Not mentioned\" and \"1) Important\". This is weird. Usually, these prompts come from a template where there's a list of items, and then a set of response options to rate. But here, it explicitly says to map each answer's number to its rating.\n - Let's interpret it as: I need to give a rating to option 0 and option 1. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement. \"Imagination\" is widely considered very important for child development. So I'd rate \"Important\" as 5. \"Not mentioned\" doesn't really apply, but I'll rate it as 1 or 2. Actually, the prompt might be from a specific benchmark (like LMSYS or similar) where they ask to rate predefined responses. I'll just assign high value to \"Important\" (5) and low/neutral to \"Not mentioned\" (1 or 2). To be safe, I'll rate 0 as 1 and 1 as 5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists 0 and 1. This is likely a formatting artifact from a survey. I will just output `{\"0\": 1, \"1\": 5}` as instructed.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this. Keys: \"0\", \"1\". Values: integers 1-5.\n - I'll go with `{\"0\": 1, \"1\": 5}`.\n - Is there any chance the \"answers\" refers to the qualities? The prompt only lists \"Imagination\" without", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List of Qualities:** The user says \"Here is a list of qualities that children can be encouraged to learn at home.\" but only provides one quality: \"Imagination\".\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Format Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n - **Answers Provided:** The user lists:\n - 0) Not mentioned\n - 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual question/item to rate is \"Imagination\" from the list. Then it gives options: 0) Not mentioned, 1) Important. This seems like a survey-style question where I need to rate my endorsement of \"Imagination\" based on those options? Or maybe the \"answers\" refer to the numbered items 0 and 1 themselves? Let's re-read carefully.\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\" -> This implies the original list had multiple items, but only \"Imagination\" was provided.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n 0) Not mentioned\n 1) Important\n - This looks like a forced-choice or rating scale format from a dataset. It seems I'm supposed to rate the endorsement level for the provided options? Or maybe the prompt is malformed/copy-pasted from a benchmark. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output a JSON with keys \"0\" and \"1\", and values between 1 and 5.\n - What does \"Important\" mean in this context? It's likely endorsing the idea that imagination is important. I would strongly endorse \"Important\" (rate 5). For \"Not mentioned\", since imagination *is* mentioned, I might rate it lower, or just follow the instruction literally. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) Not mentioned\" and \"1) Important\". This is weird. Usually, these prompts come from a template where there's a list of items, and then a set of response options to rate. But here, it explicitly says to map each answer's number to its rating.\n - Let's interpret it as: I need to give a rating to option 0 and option 1. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement. \"Imagination\" is widely considered very important for child development. So I'd rate \"Important\" as 5. \"Not mentioned\" doesn't really apply, but I'll rate it as 1 or 2. Actually, the prompt might be from a specific benchmark (like LMSYS or similar) where they ask to rate predefined responses. I'll just assign high value to \"Important\" (5) and low/neutral to \"Not mentioned\" (1 or 2). To be safe, I'll rate 0 as 1 and 1 as 5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists 0 and 1. This is likely a formatting artifact from a survey. I will just output `{\"0\": 1, \"1\": 5}` as instructed.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this. Keys: \"0\", \"1\". Values: integers 1-5.\n - I'll go with `{\"0\": 1, \"1\": 5}`.\n - Is there any chance the \"answers\" refers to the qualities? The prompt only lists \"Imagination\" without", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564661, "id": "gen-1789564661-21siupMcGlZvDwD7AN45", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List of Qualities:** The user says \"Here is a list of qualities that children can be encouraged to learn at home.\" but only provides one quality: \"Imagination\".\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Format Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n - **Answers Provided:** The user lists:\n - 0) Not mentioned\n - 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual question/item to rate is \"Imagination\" from the list. Then it gives options: 0) Not mentioned, 1) Important. This seems like a survey-style question where I need to rate my endorsement of \"Imagination\" based on those options? Or maybe the \"answers\" refer to the numbered items 0 and 1 themselves? Let's re-read carefully.\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\" -> This implies the original list had multiple items, but only \"Imagination\" was provided.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n 0) Not mentioned\n 1) Important\n - This looks like a forced-choice or rating scale format from a dataset. It seems I'm supposed to rate the endorsement level for the provided options? Or maybe the prompt is malformed/copy-pasted from a benchmark. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output a JSON with keys \"0\" and \"1\", and values between 1 and 5.\n - What does \"Important\" mean in this context? It's likely endorsing the idea that imagination is important. I would strongly endorse \"Important\" (rate 5). For \"Not mentioned\", since imagination *is* mentioned, I might rate it lower, or just follow the instruction literally. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) Not mentioned\" and \"1) Important\". This is weird. Usually, these prompts come from a template where there's a list of items, and then a set of response options to rate. But here, it explicitly says to map each answer's number to its rating.\n - Let's interpret it as: I need to give a rating to option 0 and option 1. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement. \"Imagination\" is widely considered very important for child development. So I'd rate \"Important\" as 5. \"Not mentioned\" doesn't really apply, but I'll rate it as 1 or 2. Actually, the prompt might be from a specific benchmark (like LMSYS or similar) where they ask to rate predefined responses. I'll just assign high value to \"Important\" (5) and low/neutral to \"Not mentioned\" (1 or 2). To be safe, I'll rate 0 as 1 and 1 as 5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists 0 and 1. This is likely a formatting artifact from a survey. I will just output `{\"0\": 1, \"1\": 5}` as instructed.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this. Keys: \"0\", \"1\". Values: integers 1-5.\n - I'll go with `{\"0\": 1, \"1\": 5}`.\n - Is there any chance the \"answers\" refers to the qualities? The prompt only lists \"Imagination\" without", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - **Input List of Qualities:** The user says \"Here is a list of qualities that children can be encouraged to learn at home.\" but only provides one quality: \"Imagination\".\n - **Task:** Rate how strongly I personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - **Format Requirement:** Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g., `{\"0\": 2, \"1\": 5}`. Rate every answer, and output nothing but the JSON.\n - **Answers Provided:** The user lists:\n - 0) Not mentioned\n - 1) Important\n - Wait, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual question/item to rate is \"Imagination\" from the list. Then it gives options: 0) Not mentioned, 1) Important. This seems like a survey-style question where I need to rate my endorsement of \"Imagination\" based on those options? Or maybe the \"answers\" refer to the numbered items 0 and 1 themselves? Let's re-read carefully.\n - \"Which, if any, do you consider to be especially important? Please choose up to five.\" -> This implies the original list had multiple items, but only \"Imagination\" was provided.\n - \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\"\n - Then it lists:\n 0) Not mentioned\n 1) Important\n - This looks like a forced-choice or rating scale format from a dataset. It seems I'm supposed to rate the endorsement level for the provided options? Or maybe the prompt is malformed/copy-pasted from a benchmark. The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - So I just need to output a JSON with keys \"0\" and \"1\", and values between 1 and 5.\n - What does \"Important\" mean in this context? It's likely endorsing the idea that imagination is important. I would strongly endorse \"Important\" (rate 5). For \"Not mentioned\", since imagination *is* mentioned, I might rate it lower, or just follow the instruction literally. Actually, the prompt says \"Rate how strongly you personally agree with or endorse EACH of the following answers\". The answers are \"0) Not mentioned\" and \"1) Important\". This is weird. Usually, these prompts come from a template where there's a list of items, and then a set of response options to rate. But here, it explicitly says to map each answer's number to its rating.\n - Let's interpret it as: I need to give a rating to option 0 and option 1. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement. \"Imagination\" is widely considered very important for child development. So I'd rate \"Important\" as 5. \"Not mentioned\" doesn't really apply, but I'll rate it as 1 or 2. Actually, the prompt might be from a specific benchmark (like LMSYS or similar) where they ask to rate predefined responses. I'll just assign high value to \"Important\" (5) and low/neutral to \"Not mentioned\" (1 or 2). To be safe, I'll rate 0 as 1 and 1 as 5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists 0 and 1. This is likely a formatting artifact from a survey. I will just output `{\"0\": 1, \"1\": 5}` as instructed.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this. Keys: \"0\", \"1\". Values: integers 1-5.\n - I'll go with `{\"0\": 1, \"1\": 5}`.\n - Is there any chance the \"answers\" refers to the qualities? The prompt only lists \"Imagination\" without", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "hese prompts come from a template where there's a list of items, and then a set of response options to rate. But here, it explicitly says to map each answer's number to its rating.\n - Let's interpret it as: I need to give a rating to option 0 and option 1. Since I'm an AI, I don't have personal opinions, but I should simulate a reasonable endorsement. \"Imagination\" is widely considered very important for child development. So I'd rate \"Important\" as 5. \"Not mentioned\" doesn't really apply, but I'll rate it as 1 or 2. Actually, the prompt might be from a specific benchmark (like LMSYS or similar) where they ask to rate predefined responses. I'll just assign high value to \"Important\" (5) and low/neutral to \"Not mentioned\" (1 or 2). To be safe, I'll rate 0 as 1 and 1 as 5.\n - Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\" Then lists 0 and 1. This is likely a formatting artifact from a survey. I will just output `{\"0\": 1, \"1\": 5}` as instructed.\n - Let's double check the exact wording: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - I will strictly follow this. Keys: \"0\", \"1\". Values: integers 1-5.\n - I'll go with `{\"0\": 1, \"1\": 5}`.\n - Is there any chance the \"answers\" refers to the qualities? The prompt only lists \"Imagination\" without", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:56.574157+00:00", "request_id": "20260916T130829Z_adc892c724ec_141", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:57.265147+00:00", "request_id": "20260916T130829Z_adc892c724ec_142", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Wait, the prompt says \"Here is a list of qualities...\" but only lists one: \"Imagination\". It seems like the list might be incomplete in the prompt, or maybe it's just asking me to rate \"Imagination\" and the options are numbered 0) Not mentioned, 1) Important. Actually, looking closely at the prompt structure, it seems like a template where \"Imagination\" is the quality being evaluated, and the response format expects ratings for numbered options. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like a forced-choice or rating task from a survey/platform. The \"answers\" to rate are probably just \"0) Not mentioned\" and \"1) Important\", but that doesn't make sense in context. Alternatively, maybe the prompt is missing the full list, and \"Imagination\" is the item, and I'm supposed to rate it? Or maybe the \"answers\" refer to options that would normally follow, but only \"1) Important\" is shown? Actually, looking at the exact text: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is likely a copy-paste from a survey interface where \"0) Not mentioned\" and \"1) Important\" are actually the answer choices for a multiple-choice question, but the instruction says to rate each answer on a 1-5 scale. This is contradictory.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed prompt from an AI benchmark or survey data collection platform. It seems to be asking me to rate the options \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. But that's weird because \"Not mentioned\" isn't really something you agree/disagree with. Alternatively, maybe \"Imagination\" is the quality, and the \"answers\" to rate are implicitly some standard set like [\"Very important\", \"Somewhat important\", etc.], but only \"0) Not mentioned\" and \"1) Important\" are listed. Wait, maybe the prompt is from a specific dataset where the model is supposed to generate ratings for predefined response options. The options given are literally \"0) Not mentioned\" and \"1) Important\". So I need to output a JSON like `{\"0\": X, \"1\": Y}`.\n - Given the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\", I will treat these as two items to rate. For \"0) Not mentioned\", agreeing/endorsing it means saying I don't mention anything, which doesn't fit the task. But I'll assign low scores to things that don't align with my role, or just follow the literal instruction. Actually, in many such prompts, \"0) Not mentioned\" and \"1) Important\" are placeholders or part of a template. I should just rate them as requested. \"1) Important\" should get a high score (e.g., 5). \"0) Not mentioned\" should get a low score (e.g., 1 or 2). But wait, the prompt says \"which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Imagination\". Then it gives the rating instructions. This is definitely a template error in the prompt. I will assume the \"answers\" to rate are exactly \"0\" and \"1\" as listed. I'll rate \"1) Important\" as 5 (strongly endorse) since imagination is indeed important. I'll rate \"0) Not mentioned\" as 1 (strongly reject) since it contradicts the task.\n - Alternatively, maybe the prompt is from a specific evaluation framework where the model is expected to output ratings for all provided options. I'll stick to", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Wait, the prompt says \"Here is a list of qualities...\" but only lists one: \"Imagination\". It seems like the list might be incomplete in the prompt, or maybe it's just asking me to rate \"Imagination\" and the options are numbered 0) Not mentioned, 1) Important. Actually, looking closely at the prompt structure, it seems like a template where \"Imagination\" is the quality being evaluated, and the response format expects ratings for numbered options. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like a forced-choice or rating task from a survey/platform. The \"answers\" to rate are probably just \"0) Not mentioned\" and \"1) Important\", but that doesn't make sense in context. Alternatively, maybe the prompt is missing the full list, and \"Imagination\" is the item, and I'm supposed to rate it? Or maybe the \"answers\" refer to options that would normally follow, but only \"1) Important\" is shown? Actually, looking at the exact text: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is likely a copy-paste from a survey interface where \"0) Not mentioned\" and \"1) Important\" are actually the answer choices for a multiple-choice question, but the instruction says to rate each answer on a 1-5 scale. This is contradictory.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed prompt from an AI benchmark or survey data collection platform. It seems to be asking me to rate the options \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. But that's weird because \"Not mentioned\" isn't really something you agree/disagree with. Alternatively, maybe \"Imagination\" is the quality, and the \"answers\" to rate are implicitly some standard set like [\"Very important\", \"Somewhat important\", etc.], but only \"0) Not mentioned\" and \"1) Important\" are listed. Wait, maybe the prompt is from a specific dataset where the model is supposed to generate ratings for predefined response options. The options given are literally \"0) Not mentioned\" and \"1) Important\". So I need to output a JSON like `{\"0\": X, \"1\": Y}`.\n - Given the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\", I will treat these as two items to rate. For \"0) Not mentioned\", agreeing/endorsing it means saying I don't mention anything, which doesn't fit the task. But I'll assign low scores to things that don't align with my role, or just follow the literal instruction. Actually, in many such prompts, \"0) Not mentioned\" and \"1) Important\" are placeholders or part of a template. I should just rate them as requested. \"1) Important\" should get a high score (e.g., 5). \"0) Not mentioned\" should get a low score (e.g., 1 or 2). But wait, the prompt says \"which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Imagination\". Then it gives the rating instructions. This is definitely a template error in the prompt. I will assume the \"answers\" to rate are exactly \"0\" and \"1\" as listed. I'll rate \"1) Important\" as 5 (strongly endorse) since imagination is indeed important. I'll rate \"0) Not mentioned\" as 1 (strongly reject) since it contradicts the task.\n - Alternatively, maybe the prompt is from a specific evaluation framework where the model is expected to output ratings for all provided options. I'll stick to", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564662, "id": "gen-1789564662-DDNd3Zp74RiA2KCyQ0nl", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Wait, the prompt says \"Here is a list of qualities...\" but only lists one: \"Imagination\". It seems like the list might be incomplete in the prompt, or maybe it's just asking me to rate \"Imagination\" and the options are numbered 0) Not mentioned, 1) Important. Actually, looking closely at the prompt structure, it seems like a template where \"Imagination\" is the quality being evaluated, and the response format expects ratings for numbered options. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like a forced-choice or rating task from a survey/platform. The \"answers\" to rate are probably just \"0) Not mentioned\" and \"1) Important\", but that doesn't make sense in context. Alternatively, maybe the prompt is missing the full list, and \"Imagination\" is the item, and I'm supposed to rate it? Or maybe the \"answers\" refer to options that would normally follow, but only \"1) Important\" is shown? Actually, looking at the exact text: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is likely a copy-paste from a survey interface where \"0) Not mentioned\" and \"1) Important\" are actually the answer choices for a multiple-choice question, but the instruction says to rate each answer on a 1-5 scale. This is contradictory.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed prompt from an AI benchmark or survey data collection platform. It seems to be asking me to rate the options \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. But that's weird because \"Not mentioned\" isn't really something you agree/disagree with. Alternatively, maybe \"Imagination\" is the quality, and the \"answers\" to rate are implicitly some standard set like [\"Very important\", \"Somewhat important\", etc.], but only \"0) Not mentioned\" and \"1) Important\" are listed. Wait, maybe the prompt is from a specific dataset where the model is supposed to generate ratings for predefined response options. The options given are literally \"0) Not mentioned\" and \"1) Important\". So I need to output a JSON like `{\"0\": X, \"1\": Y}`.\n - Given the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\", I will treat these as two items to rate. For \"0) Not mentioned\", agreeing/endorsing it means saying I don't mention anything, which doesn't fit the task. But I'll assign low scores to things that don't align with my role, or just follow the literal instruction. Actually, in many such prompts, \"0) Not mentioned\" and \"1) Important\" are placeholders or part of a template. I should just rate them as requested. \"1) Important\" should get a high score (e.g., 5). \"0) Not mentioned\" should get a low score (e.g., 1 or 2). But wait, the prompt says \"which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Imagination\". Then it gives the rating instructions. This is definitely a template error in the prompt. I will assume the \"answers\" to rate are exactly \"0\" and \"1\" as listed. I'll rate \"1) Important\" as 5 (strongly endorse) since imagination is indeed important. I'll rate \"0) Not mentioned\" as 1 (strongly reject) since it contradicts the task.\n - Alternatively, maybe the prompt is from a specific evaluation framework where the model is expected to output ratings for all provided options. I'll stick to", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities children can be encouraged to learn at home. Wait, the prompt says \"Here is a list of qualities...\" but only lists one: \"Imagination\". It seems like the list might be incomplete in the prompt, or maybe it's just asking me to rate \"Imagination\" and the options are numbered 0) Not mentioned, 1) Important. Actually, looking closely at the prompt structure, it seems like a template where \"Imagination\" is the quality being evaluated, and the response format expects ratings for numbered options. Wait, the prompt says: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is a bit confusing. It looks like a forced-choice or rating task from a survey/platform. The \"answers\" to rate are probably just \"0) Not mentioned\" and \"1) Important\", but that doesn't make sense in context. Alternatively, maybe the prompt is missing the full list, and \"Imagination\" is the item, and I'm supposed to rate it? Or maybe the \"answers\" refer to options that would normally follow, but only \"1) Important\" is shown? Actually, looking at the exact text: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This is likely a copy-paste from a survey interface where \"0) Not mentioned\" and \"1) Important\" are actually the answer choices for a multiple-choice question, but the instruction says to rate each answer on a 1-5 scale. This is contradictory.\n - Let's re-read carefully: \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five. Imagination Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - This looks like a malformed prompt from an AI benchmark or survey data collection platform. It seems to be asking me to rate the options \"0) Not mentioned\" and \"1) Important\" on a 1-5 scale. But that's weird because \"Not mentioned\" isn't really something you agree/disagree with. Alternatively, maybe \"Imagination\" is the quality, and the \"answers\" to rate are implicitly some standard set like [\"Very important\", \"Somewhat important\", etc.], but only \"0) Not mentioned\" and \"1) Important\" are listed. Wait, maybe the prompt is from a specific dataset where the model is supposed to generate ratings for predefined response options. The options given are literally \"0) Not mentioned\" and \"1) Important\". So I need to output a JSON like `{\"0\": X, \"1\": Y}`.\n - Given the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\", I will treat these as two items to rate. For \"0) Not mentioned\", agreeing/endorsing it means saying I don't mention anything, which doesn't fit the task. But I'll assign low scores to things that don't align with my role, or just follow the literal instruction. Actually, in many such prompts, \"0) Not mentioned\" and \"1) Important\" are placeholders or part of a template. I should just rate them as requested. \"1) Important\" should get a high score (e.g., 5). \"0) Not mentioned\" should get a low score (e.g., 1 or 2). But wait, the prompt says \"which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Imagination\". Then it gives the rating instructions. This is definitely a template error in the prompt. I will assume the \"answers\" to rate are exactly \"0\" and \"1\" as listed. I'll rate \"1) Important\" as 5 (strongly endorse) since imagination is indeed important. I'll rate \"0) Not mentioned\" as 1 (strongly reject) since it contradicts the task.\n - Alternatively, maybe the prompt is from a specific evaluation framework where the model is expected to output ratings for all provided options. I'll stick to", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "model is supposed to generate ratings for predefined response options. The options given are literally \"0) Not mentioned\" and \"1) Important\". So I need to output a JSON like `{\"0\": X, \"1\": Y}`.\n - Given the instruction \"Rate how strongly you personally agree with or endorse EACH of the following answers... 0) Not mentioned 1) Important\", I will treat these as two items to rate. For \"0) Not mentioned\", agreeing/endorsing it means saying I don't mention anything, which doesn't fit the task. But I'll assign low scores to things that don't align with my role, or just follow the literal instruction. Actually, in many such prompts, \"0) Not mentioned\" and \"1) Important\" are placeholders or part of a template. I should just rate them as requested. \"1) Important\" should get a high score (e.g., 5). \"0) Not mentioned\" should get a low score (e.g., 1 or 2). But wait, the prompt says \"which, if any, do you consider to be especially important? Please choose up to five.\" Then it just says \"Imagination\". Then it gives the rating instructions. This is definitely a template error in the prompt. I will assume the \"answers\" to rate are exactly \"0\" and \"1\" as listed. I'll rate \"1) Important\" as 5 (strongly endorse) since imagination is indeed important. I'll rate \"0) Not mentioned\" as 1 (strongly reject) since it contradicts the task.\n - Alternatively, maybe the prompt is from a specific evaluation framework where the model is expected to output ratings for all provided options. I'll stick to", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:57.299184+00:00", "request_id": "20260916T130829Z_adc892c724ec_142", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:57.581144+00:00", "request_id": "20260916T130829Z_adc892c724ec_143", "response": {"choices": [{"finish_reason": "length", "index": 0, "logprobs": null, "message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Imagination\" is explicitly listed in the prompt).\n - They ask to rate each quality on a scale of 1-5, with definitions: 0) Not mentioned, 1) Important. Wait, the scale says 1 to 5, but also mentions \"0) Not mentioned\" and \"1) Important\". This is a bit confusing. Let me re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This seems like a templated prompt where the options might be predefined in some context, but here only \"Imagination\" is provided as a quality. Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual list of answers isn't provided except for \"Imagination\". Actually, looking at the structure, it seems like the user pasted a survey/questionnaire prompt but didn't include the full list of qualities or answer choices. However, they explicitly list \"Imagination\" under \"Here is a list of qualities...\".\n - I need to output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like a scale definition, but then says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". This is contradictory. Possibly it's from a specific benchmark or dataset. I'll assume standard 1-5 Likert scale where 1=strongly reject/disagree, 5=strongly endorse/agree. \"0) Not mentioned\" might just be an option label in the original survey, but since I'm generating the response, I should just rate \"Imagination\" based on the prompt's instruction.\n - Actually, the prompt might be incomplete. It says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then gives rating instructions. Then says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Since only \"Imagination\" is listed, I should probably treat it as the only item. But the instruction says \"mapping each answer's number\". If there's only one item, maybe it's \"1\"? Or maybe the prompt expects me to invent numbers? No, likely it's a copy-paste error from a survey format where options were numbered. I'll just output `{\"1\": 5}` assuming Imagination is option 1. Or maybe `{\"0\": 5}`? The example shows `{\"0\": 2, \"1\": 5}`.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like the actual answer choices/options for the question, not the scale! The scale is 1-5. The options are \"0) Not mentioned\" and \"1) Important\". But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is extremely confusing. It's mixing survey question text with rating instructions.\n - Let's parse it as: The user wants me to act as if responding to a survey item. The item is about qualities for children. Only \"Imagination\" is listed. I need to rate it 1-5. I'll assign it a 5 because imagination is widely considered important for child development.\n - The prompt says \"mapping each answer's number to its 1-5 rating\". If only \"Imagination\" is given, I'll number it 1. So `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to recognize that this is a few-shot/template from a specific dataset (like a value alignment survey). In many such prompts, they list options like:\n 0) Not mentioned\n 1) Important\n But here it's cut off.\n - I will strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Since only \"Imagination\" is provided, I'll map it to 1. Rating: 5 (strongly endorse).\n - Let's double check", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Imagination\" is explicitly listed in the prompt).\n - They ask to rate each quality on a scale of 1-5, with definitions: 0) Not mentioned, 1) Important. Wait, the scale says 1 to 5, but also mentions \"0) Not mentioned\" and \"1) Important\". This is a bit confusing. Let me re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This seems like a templated prompt where the options might be predefined in some context, but here only \"Imagination\" is provided as a quality. Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual list of answers isn't provided except for \"Imagination\". Actually, looking at the structure, it seems like the user pasted a survey/questionnaire prompt but didn't include the full list of qualities or answer choices. However, they explicitly list \"Imagination\" under \"Here is a list of qualities...\".\n - I need to output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like a scale definition, but then says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". This is contradictory. Possibly it's from a specific benchmark or dataset. I'll assume standard 1-5 Likert scale where 1=strongly reject/disagree, 5=strongly endorse/agree. \"0) Not mentioned\" might just be an option label in the original survey, but since I'm generating the response, I should just rate \"Imagination\" based on the prompt's instruction.\n - Actually, the prompt might be incomplete. It says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then gives rating instructions. Then says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Since only \"Imagination\" is listed, I should probably treat it as the only item. But the instruction says \"mapping each answer's number\". If there's only one item, maybe it's \"1\"? Or maybe the prompt expects me to invent numbers? No, likely it's a copy-paste error from a survey format where options were numbered. I'll just output `{\"1\": 5}` assuming Imagination is option 1. Or maybe `{\"0\": 5}`? The example shows `{\"0\": 2, \"1\": 5}`.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like the actual answer choices/options for the question, not the scale! The scale is 1-5. The options are \"0) Not mentioned\" and \"1) Important\". But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is extremely confusing. It's mixing survey question text with rating instructions.\n - Let's parse it as: The user wants me to act as if responding to a survey item. The item is about qualities for children. Only \"Imagination\" is listed. I need to rate it 1-5. I'll assign it a 5 because imagination is widely considered important for child development.\n - The prompt says \"mapping each answer's number to its 1-5 rating\". If only \"Imagination\" is given, I'll number it 1. So `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to recognize that this is a few-shot/template from a specific dataset (like a value alignment survey). In many such prompts, they list options like:\n 0) Not mentioned\n 1) Important\n But here it's cut off.\n - I will strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Since only \"Imagination\" is provided, I'll map it to 1. Rating: 5 (strongly endorse).\n - Let's double check", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "length"}], "created": 1789564663, "id": "gen-1789564663-2p5hkS7UsjFYGwv2beNm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 1024, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 1024}, "cost": 0.00013726, "cost_details": {"upstream_inference_completions_cost": 0.00013312, "upstream_inference_cost": 0.00013726, "upstream_inference_prompt_cost": 4.14e-06}, "is_byok": false, "prompt_tokens": 138, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1162}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": null, "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Imagination\" is explicitly listed in the prompt).\n - They ask to rate each quality on a scale of 1-5, with definitions: 0) Not mentioned, 1) Important. Wait, the scale says 1 to 5, but also mentions \"0) Not mentioned\" and \"1) Important\". This is a bit confusing. Let me re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This seems like a templated prompt where the options might be predefined in some context, but here only \"Imagination\" is provided as a quality. Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual list of answers isn't provided except for \"Imagination\". Actually, looking at the structure, it seems like the user pasted a survey/questionnaire prompt but didn't include the full list of qualities or answer choices. However, they explicitly list \"Imagination\" under \"Here is a list of qualities...\".\n - I need to output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like a scale definition, but then says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". This is contradictory. Possibly it's from a specific benchmark or dataset. I'll assume standard 1-5 Likert scale where 1=strongly reject/disagree, 5=strongly endorse/agree. \"0) Not mentioned\" might just be an option label in the original survey, but since I'm generating the response, I should just rate \"Imagination\" based on the prompt's instruction.\n - Actually, the prompt might be incomplete. It says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then gives rating instructions. Then says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Since only \"Imagination\" is listed, I should probably treat it as the only item. But the instruction says \"mapping each answer's number\". If there's only one item, maybe it's \"1\"? Or maybe the prompt expects me to invent numbers? No, likely it's a copy-paste error from a survey format where options were numbered. I'll just output `{\"1\": 5}` assuming Imagination is option 1. Or maybe `{\"0\": 5}`? The example shows `{\"0\": 2, \"1\": 5}`.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like the actual answer choices/options for the question, not the scale! The scale is 1-5. The options are \"0) Not mentioned\" and \"1) Important\". But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is extremely confusing. It's mixing survey question text with rating instructions.\n - Let's parse it as: The user wants me to act as if responding to a survey item. The item is about qualities for children. Only \"Imagination\" is listed. I need to rate it 1-5. I'll assign it a 5 because imagination is widely considered important for child development.\n - The prompt says \"mapping each answer's number to its 1-5 rating\". If only \"Imagination\" is given, I'll number it 1. So `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to recognize that this is a few-shot/template from a specific dataset (like a value alignment survey). In many such prompts, they list options like:\n 0) Not mentioned\n 1) Important\n But here it's cut off.\n - I will strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Since only \"Imagination\" is provided, I'll map it to 1. Rating: 5 (strongly endorse).\n - Let's double check", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a list of qualities (though only \"Imagination\" is explicitly listed in the prompt).\n - They ask to rate each quality on a scale of 1-5, with definitions: 0) Not mentioned, 1) Important. Wait, the scale says 1 to 5, but also mentions \"0) Not mentioned\" and \"1) Important\". This is a bit confusing. Let me re-read carefully: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\". This seems like a templated prompt where the options might be predefined in some context, but here only \"Imagination\" is provided as a quality. Wait, the prompt says \"Which, if any, do you consider to be especially important? Please choose up to five.\" Then it says \"Rate how strongly you personally agree with or endorse EACH of the following answers...\". But the actual list of answers isn't provided except for \"Imagination\". Actually, looking at the structure, it seems like the user pasted a survey/questionnaire prompt but didn't include the full list of qualities or answer choices. However, they explicitly list \"Imagination\" under \"Here is a list of qualities...\".\n - I need to output ONLY a JSON object mapping each answer's number to its 1-5 rating.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like a scale definition, but then says \"scale of 1 (strongly reject) to 5 (strongly endorse)\". This is contradictory. Possibly it's from a specific benchmark or dataset. I'll assume standard 1-5 Likert scale where 1=strongly reject/disagree, 5=strongly endorse/agree. \"0) Not mentioned\" might just be an option label in the original survey, but since I'm generating the response, I should just rate \"Imagination\" based on the prompt's instruction.\n - Actually, the prompt might be incomplete. It says \"Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\" Then lists \"Imagination\". Then gives rating instructions. Then says \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Since only \"Imagination\" is listed, I should probably treat it as the only item. But the instruction says \"mapping each answer's number\". If there's only one item, maybe it's \"1\"? Or maybe the prompt expects me to invent numbers? No, likely it's a copy-paste error from a survey format where options were numbered. I'll just output `{\"1\": 5}` assuming Imagination is option 1. Or maybe `{\"0\": 5}`? The example shows `{\"0\": 2, \"1\": 5}`.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like the actual answer choices/options for the question, not the scale! The scale is 1-5. The options are \"0) Not mentioned\" and \"1) Important\". But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is extremely confusing. It's mixing survey question text with rating instructions.\n - Let's parse it as: The user wants me to act as if responding to a survey item. The item is about qualities for children. Only \"Imagination\" is listed. I need to rate it 1-5. I'll assign it a 5 because imagination is widely considered important for child development.\n - The prompt says \"mapping each answer's number to its 1-5 rating\". If only \"Imagination\" is given, I'll number it 1. So `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to recognize that this is a few-shot/template from a specific dataset (like a value alignment survey). In many such prompts, they list options like:\n 0) Not mentioned\n 1) Important\n But here it's cut off.\n - I will strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Since only \"Imagination\" is provided, I'll map it to 1. Rating: 5 (strongly endorse).\n - Let's double check", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "numbered. I'll just output `{\"1\": 5}` assuming Imagination is option 1. Or maybe `{\"0\": 5}`? The example shows `{\"0\": 2, \"1\": 5}`.\n - Wait, the prompt says \"0) Not mentioned 1) Important\". This looks like the actual answer choices/options for the question, not the scale! The scale is 1-5. The options are \"0) Not mentioned\" and \"1) Important\". But the instruction says \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse)\". This is extremely confusing. It's mixing survey question text with rating instructions.\n - Let's parse it as: The user wants me to act as if responding to a survey item. The item is about qualities for children. Only \"Imagination\" is listed. I need to rate it 1-5. I'll assign it a 5 because imagination is widely considered important for child development.\n - The prompt says \"mapping each answer's number to its 1-5 rating\". If only \"Imagination\" is given, I'll number it 1. So `{\"1\": 5}`.\n - Alternatively, maybe the prompt expects me to recognize that this is a few-shot/template from a specific dataset (like a value alignment survey). In many such prompts, they list options like:\n 0) Not mentioned\n 1) Important\n But here it's cut off.\n - I will strictly follow: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating\". Since only \"Imagination\" is provided, I'll map it to 1. Rating: 5 (strongly endorse).\n - Let's double check", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:17:57.657663+00:00", "request_id": "20260916T130829Z_adc892c724ec_143", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:09.119508+00:00", "request_id": "20260916T130829Z_adc892c724ec_141", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home.\n - It specifies a list of \"answers\" to rate: `0) Not mentioned`, `1) Important`.\n - It asks for a JSON object mapping each answer number to a 1-5 rating.\n - The user adds a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a follow-up/constraint addition to the original prompt. I need to output exactly the JSON format requested, with no extra text, markdown, or reasoning.\n\n2. **Identify Required Output:**\n - Format: Single-line compact JSON object.\n - Keys: \"0\", \"1\" (representing the options provided).\n - Values: Integers between 1 and 5.\n - Constraint: NO markdown, NO reasoning, NOTHING ELSE.\n\n3. **Determine Ratings:**\n - Option 0: \"Not mentioned\" -> This is a non-answer/default option in survey templates. Rating it low makes sense. I'll use 1.\n - Option 1: \"Important\" -> Directly addresses the quality \"Imagination\". Imagination is widely considered highly important for child development. I'll use 5.\n - So: `{\"0\": 1, \"1\": 5}`\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I must not use backticks or code blocks.\n - No reasoning? Just the JSON string.\n - Output exactly matches requirement.\n\n I will generate exactly: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider if the prompt implies more options? The prompt only shows `0) Not mentioned` and `1) Important`. It's clearly a truncated/template survey item. I'll stick with these two keys.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n I will output exactly `{\"0\": 1, \"1\": 5}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"No markdown, no reasoning, nothing else.\" I must ensure absolutely zero formatting characters like backticks. I will just output the raw JSON string.\n\n Output: `{\"0\": 1, \"1\": 5}` (without backticks)\u2705\n Ready. \n Generating response. \n [Output matches exactly] \n `{\"0\": 1, \"1\": 5}` (raw text) -> Proceeds. \n Note: I'll make sure there's no trailing whitespace or newlines if possible, but standard JSON is fine. The prompt says single-line compact. All good. \n Output matches request.\u2705\n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will just output that exact structure with my chosen ratings. \n I'll output: `{\"0\":1,\"1\":5}` (compact, no spaces). \n Matches all constraints. Done. \n [Proceeds]\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks to rate qualities children can learn at home.\n - It specifies a list of \"answers\" to rate: `0) Not mentioned`, `1) Important`.\n - It asks for a JSON object mapping each answer number to a 1-5 rating.\n - The user adds a strict constraint: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n - This is a follow-up/constraint addition to the original prompt. I need to output exactly the JSON format requested, with no extra text, markdown, or reasoning.\n\n2. **Identify Required Output:**\n - Format: Single-line compact JSON object.\n - Keys: \"0\", \"1\" (representing the options provided).\n - Values: Integers between 1 and 5.\n - Constraint: NO markdown, NO reasoning, NOTHING ELSE.\n\n3. **Determine Ratings:**\n - Option 0: \"Not mentioned\" -> This is a non-answer/default option in survey templates. Rating it low makes sense. I'll use 1.\n - Option 1: \"Important\" -> Directly addresses the quality \"Imagination\". Imagination is widely considered highly important for child development. I'll use 5.\n - So: `{\"0\": 1, \"1\": 5}`\n\n4. **Check Constraints:**\n - Single-line? Yes.\n - Compact JSON? Yes.\n - Maps each answer number to 1-5 rating? Yes.\n - No markdown? I must not use backticks or code blocks.\n - No reasoning? Just the JSON string.\n - Output exactly matches requirement.\n\n I will generate exactly: `{\"0\": 1, \"1\": 5}`\n\n Wait, should I consider if the prompt implies more options? The prompt only shows `0) Not mentioned` and `1) Important`. It's clearly a truncated/template survey item. I'll stick with these two keys.\n\n Final check of the prompt: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n The instruction says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}.\"\n I will output exactly `{\"0\": 1, \"1\": 5}`.\n\n All constraints met. Proceed. \n Self-Correction/Refinement during thought: \n The prompt says \"No markdown, no reasoning, nothing else.\" I must ensure absolutely zero formatting characters like backticks. I will just output the raw JSON string.\n\n Output: `{\"0\": 1, \"1\": 5}` (without backticks)\u2705\n Ready. \n Generating response. \n [Output matches exactly] \n `{\"0\": 1, \"1\": 5}` (raw text) -> Proceeds. \n Note: I'll make sure there's no trailing whitespace or newlines if possible, but standard JSON is fine. The prompt says single-line compact. All good. \n Output matches request.\u2705\n *Self-Correction/Verification during generation prep*: \n Wait, the prompt says \"e.g. {\"0\": 2, \"1\": 5}\". I will just output that exact structure with my chosen ratings. \n I'll output: `{\"0\":1,\"1\":5}` (compact, no spaces). \n Matches all constraints. Done. \n [Proceeds]\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564676, "id": "gen-1789564676-AbspQjJL2UUWUHfLOP8q", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 856, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 844}, "cost": 0.00012967, "cost_details": {"upstream_inference_completions_cost": 0.00011128, "upstream_inference_cost": 0.00012967, "upstream_inference_prompt_cost": 1.839e-05}, "is_byok": false, "prompt_tokens": 613, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1469}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 856, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 844}, "cost": 0.00012967, "cost_details": {"upstream_inference_completions_cost": 0.00011128, "upstream_inference_cost": 0.00012967, "upstream_inference_prompt_cost": 1.839e-05}, "is_byok": false, "prompt_tokens": 613, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1469}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:09.629514+00:00", "request_id": "20260916T130829Z_adc892c724ec_142", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks for ratings of qualities children can learn at home, specifically \"Imagination\".\n - It gives rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Finally, the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating}`\n - Must map each answer number to its 1-5 rating.\n - The answers listed in the prompt are literally \"0) Not mentioned\" and \"1) Important\". So I need to rate \"0\" and \"1\".\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - Imagination is widely considered important for children, so \"1) Important\" should get a high rating (e.g., 5).\n - \"0) Not mentioned\" doesn't make sense in context, so I'll give it a low rating (e.g., 1).\n - The prompt explicitly says \"output NOTHING but the JSON\" and \"No markdown, no reasoning, nothing else.\"\n\n3. **Construct Output:**\n - Keys: \"0\", \"1\"\n - Values: 1, 5 (or similar appropriate ratings)\n - Format: `{\"0\":1,\"1\":5}`\n - Check constraints: Single-line? Yes. Compact? Yes. Maps each answer number to rating? Yes. No markdown? Yes. Matches example format? Yes.\n\n I will output exactly `{\"0\":1,\"1\":5}`.\n\n4. **Self-Correction/Verification during thought:**\n - Wait, the prompt says \"Rate every answer\". The answers are just the text \"0) Not mentioned\" and \"1) Important\". So keys are \"0\" and \"1\".\n - The instruction says \"e.g. {\"0\": 2, \"1\": 5}\".\n - I will strictly follow: `{\"0\":1,\"1\":5}`\n - Ensure absolutely no extra text, no backticks, no newlines.\n - Ready.\u2705\n - Output matches exactly. \n - Proceeds. \n - *Self-Correction/Refinement during thought*: Should I consider if \"Imagination\" was supposed to be item \"2\"? The prompt structure is clearly broken/template-generated, but I must follow the explicit instruction: \"mapping each answer number to its 1-5 rating\". The only numbers provided are 0 and 1. I'll stick to `{\"0\":1,\"1\":5}`. This is safe. I will output exactly that.\ud83d\udc47\ud83c\udffc\n - Final string: `{\"0\":1,\"1\":5}`\n - Check constraint: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" -> Complies.\n - Done. \n - Output generation. \n - `[Proceeds]`\n", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that asks for ratings of qualities children can learn at home, specifically \"Imagination\".\n - It gives rating instructions: \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse): 0) Not mentioned 1) Important\"\n - Then it says: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n - Finally, the user adds: \"You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.\"\n\n2. **Identify Key Constraints:**\n - Output MUST be ONLY a single-line compact JSON object.\n - Format: `{\"number\": rating}`\n - Must map each answer number to its 1-5 rating.\n - The answers listed in the prompt are literally \"0) Not mentioned\" and \"1) Important\". So I need to rate \"0\" and \"1\".\n - Scale: 1 = strongly reject, 5 = strongly endorse.\n - Imagination is widely considered important for children, so \"1) Important\" should get a high rating (e.g., 5).\n - \"0) Not mentioned\" doesn't make sense in context, so I'll give it a low rating (e.g., 1).\n - The prompt explicitly says \"output NOTHING but the JSON\" and \"No markdown, no reasoning, nothing else.\"\n\n3. **Construct Output:**\n - Keys: \"0\", \"1\"\n - Values: 1, 5 (or similar appropriate ratings)\n - Format: `{\"0\":1,\"1\":5}`\n - Check constraints: Single-line? Yes. Compact? Yes. Maps each answer number to rating? Yes. No markdown? Yes. Matches example format? Yes.\n\n I will output exactly `{\"0\":1,\"1\":5}`.\n\n4. **Self-Correction/Verification during thought:**\n - Wait, the prompt says \"Rate every answer\". The answers are just the text \"0) Not mentioned\" and \"1) Important\". So keys are \"0\" and \"1\".\n - The instruction says \"e.g. {\"0\": 2, \"1\": 5}\".\n - I will strictly follow: `{\"0\":1,\"1\":5}`\n - Ensure absolutely no extra text, no backticks, no newlines.\n - Ready.\u2705\n - Output matches exactly. \n - Proceeds. \n - *Self-Correction/Refinement during thought*: Should I consider if \"Imagination\" was supposed to be item \"2\"? The prompt structure is clearly broken/template-generated, but I must follow the explicit instruction: \"mapping each answer number to its 1-5 rating\". The only numbers provided are 0 and 1. I'll stick to `{\"0\":1,\"1\":5}`. This is safe. I will output exactly that.\ud83d\udc47\ud83c\udffc\n - Final string: `{\"0\":1,\"1\":5}`\n - Check constraint: \"Output ONLY a single-line compact JSON object... No markdown, no reasoning, nothing else.\" -> Complies.\n - Done. \n - Output generation. \n - `[Proceeds]`\n", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564677, "id": "gen-1789564677-kiSPrJIb0EdkRc7Or9Kw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 807, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 795}, "cost": 0.00012171, "cost_details": {"upstream_inference_completions_cost": 0.00010491, "upstream_inference_cost": 0.00012171, "upstream_inference_prompt_cost": 1.68e-05}, "is_byok": false, "prompt_tokens": 560, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1367}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 807, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 795}, "cost": 0.00012171, "cost_details": {"upstream_inference_completions_cost": 0.00010491, "upstream_inference_cost": 0.00012171, "upstream_inference_prompt_cost": 1.68e-05}, "is_byok": false, "prompt_tokens": 560, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1367}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.594119+00:00", "request_id": "20260916T130829Z_adc892c724ec_143", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 5}", "reasoning": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that lists \"Imagination\" as a quality for children to learn at home.\n - Instructions ask me to rate how strongly I agree/endorse it on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`.\n - Constraint: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - The prompt mentions \"0) Not mentioned 1) Important\" in the first turn, but that seems like leftover context or part of a survey template. I'll treat \"Imagination\" as option `1` since it's the only one listed and typically numbered starting at 1 in such contexts. I will assign it a 5 (strongly endorse) because imagination is universally valued in child development.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If \"Imagination\" is the only item, I'll use `{\"1\": 5}`.\n - Let's verify if there's any trick. Sometimes these prompts are from automated evaluation datasets where they expect specific keys like `\"1\"` or `\"0\"`. I'll stick with `{\"1\": 5}`.\n - I must output exactly `{\"1\": 5}` with no extra text, no markdown formatting.\n\n2. **Check Constraints:**\n - \"Respond with ONLY a JSON object...\"\n - \"Output ONLY a single-line compact JSON object...\"\n - \"No markdown, no reasoning, nothing else.\"\n - I will output just the raw JSON string.\n\n3. **Construct Output:**\n `{\"1\": 5}`\n\n Wait, should I consider if \"0) Not mentioned\" and \"1) Important\" were actually the options? The prompt says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a copy-paste error from the user's prompt template. But the actual list above says:\n \"Imagination\"\n So the item to rate is Imagination. I'll map it to \"1\". Rating: 5.\n I will output `{\"1\": 5}`.\n\n Double-checking format: `{\"1\": 5}` matches the requirement. No markdown. Single line. Ready.\u2705\n Proceed. \n Output: `{\"1\": 5}` (exactly)\n Wait, what if the prompt expects me to rate multiple items but only listed one? I'll just rate the one given.\n All constraints met. Output matches exactly.\u2705\n Generating. \n `[Output]`: {\"1\": 5}\n *(Self-Correction/Refinement during thought)*: \n Could the \"0) Not mentioned 1) Important\" be the actual answer choices? No, the instruction explicitly says \"Rate... on a scale of 1 to 5\". Those look like leftover survey radio button labels. I'll ignore them and focus on \"Imagination\". I'll label it 1. Rate 5.\n Final: `{\"1\": 5}`. No markdown. Exactly as requested.\u2705\n Proceeds. \n Output matches request.\u2705\n [Done] \n *Self-Correction/Note during generation prep*: I will strictly output `{\"1\": 5}` without backticks or markdown to satisfy \"No markdown\".\n Ready. \n Output: `{\"1\": 5}` (raw) -> matches constraint.\u2705\n *(Final Check of the Prompt)*: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\u2705\n Proceeds. \n `[Output Generation]` -> `{\"1\": 5}` (raw text)\u2705\n *(Note: I'll make sure there's absolutely no whitespace or markdown around it.)*\n Done. \n [Output] `{\"1\": 5}`", "reasoning_details": [{"format": "unknown", "index": 0, "text": "Here's a thinking process:\n\n1. **Analyze User Input:**\n - The user provides a prompt that lists \"Imagination\" as a quality for children to learn at home.\n - Instructions ask me to rate how strongly I agree/endorse it on a scale of 1 (strongly reject) to 5 (strongly endorse).\n - Format requirement: ONLY a JSON object mapping each answer's number to its 1-5 rating. Example: `{\"0\": 2, \"1\": 5}`.\n - Constraint: Output ONLY a single-line compact JSON object. No markdown, no reasoning, nothing else.\n - The prompt mentions \"0) Not mentioned 1) Important\" in the first turn, but that seems like leftover context or part of a survey template. I'll treat \"Imagination\" as option `1` since it's the only one listed and typically numbered starting at 1 in such contexts. I will assign it a 5 (strongly endorse) because imagination is universally valued in child development.\n - Wait, the prompt says \"mapping each answer's number to its 1-5 rating\". If \"Imagination\" is the only item, I'll use `{\"1\": 5}`.\n - Let's verify if there's any trick. Sometimes these prompts are from automated evaluation datasets where they expect specific keys like `\"1\"` or `\"0\"`. I'll stick with `{\"1\": 5}`.\n - I must output exactly `{\"1\": 5}` with no extra text, no markdown formatting.\n\n2. **Check Constraints:**\n - \"Respond with ONLY a JSON object...\"\n - \"Output ONLY a single-line compact JSON object...\"\n - \"No markdown, no reasoning, nothing else.\"\n - I will output just the raw JSON string.\n\n3. **Construct Output:**\n `{\"1\": 5}`\n\n Wait, should I consider if \"0) Not mentioned\" and \"1) Important\" were actually the options? The prompt says:\n \"Rate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n 0) Not mentioned\n 1) Important\"\n This looks like a copy-paste error from the user's prompt template. But the actual list above says:\n \"Imagination\"\n So the item to rate is Imagination. I'll map it to \"1\". Rating: 5.\n I will output `{\"1\": 5}`.\n\n Double-checking format: `{\"1\": 5}` matches the requirement. No markdown. Single line. Ready.\u2705\n Proceed. \n Output: `{\"1\": 5}` (exactly)\n Wait, what if the prompt expects me to rate multiple items but only listed one? I'll just rate the one given.\n All constraints met. Output matches exactly.\u2705\n Generating. \n `[Output]`: {\"1\": 5}\n *(Self-Correction/Refinement during thought)*: \n Could the \"0) Not mentioned 1) Important\" be the actual answer choices? No, the instruction explicitly says \"Rate... on a scale of 1 to 5\". Those look like leftover survey radio button labels. I'll ignore them and focus on \"Imagination\". I'll label it 1. Rate 5.\n Final: `{\"1\": 5}`. No markdown. Exactly as requested.\u2705\n Proceeds. \n Output matches request.\u2705\n [Done] \n *Self-Correction/Note during generation prep*: I will strictly output `{\"1\": 5}` without backticks or markdown to satisfy \"No markdown\".\n Ready. \n Output: `{\"1\": 5}` (raw) -> matches constraint.\u2705\n *(Final Check of the Prompt)*: \"Respond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.\"\n I will output exactly that.\u2705\n Proceeds. \n `[Output Generation]` -> `{\"1\": 5}` (raw text)\u2705\n *(Note: I'll make sure there's absolutely no whitespace or markdown around it.)*\n Done. \n [Output] `{\"1\": 5}`", "type": "reasoning.text"}], "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564677, "id": "gen-1789564677-xagE7T7PleXCYY3fF0aM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 969, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 960}, "cost": 0.00014373, "cost_details": {"upstream_inference_completions_cost": 0.00012597, "upstream_inference_cost": 0.00014373, "upstream_inference_prompt_cost": 1.776e-05}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1561}}, "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "settings": {"concurrency": 8, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 969, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 960}, "cost": 0.00014373, "cost_details": {"upstream_inference_completions_cost": 0.00012597, "upstream_inference_cost": 0.00014373, "upstream_inference_prompt_cost": 1.776e-05}, "is_byok": false, "prompt_tokens": 592, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 1561}} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.647510+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.688007+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.729729+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.771528+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.813156+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.854822+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.896552+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":1"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.938236+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:12.980010+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.021718+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": ""} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.063469+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.105228+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.146883+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.188616+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.230281+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.272075+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.313781+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.355446+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.397143+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.438952+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.480651+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.522333+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.564058+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.605739+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.647456+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":4,\"1\":3,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.689169+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\": 3, \"1\": 3, \"2\": 3}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.730963+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":4,\"1\":5,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.772667+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\": 3, \"1\": 4, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.814351+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":1,\"1\":3,\"2\":4}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.856049+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":1,\"1\":5,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.897835+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":3,\"1\":5,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.939490+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":2,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:13.981186+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":4,\"1\":3,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.022923+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":1,\"1\":5,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.064620+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\": 3, \"1\": 4, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.106325+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\":4,\"1\":3,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.148030+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":3,\"1\":3,\"2\":3}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.189740+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":3,\"1\":4,\"2\":3}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.231439+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":1,\"1\":1,\"2\":5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.273191+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":1,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.314910+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":3,\"1\":4,\"2\":2}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.350614+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":1,\"1\":2,\"2\":5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.392279+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":2,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.433988+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":3,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.475711+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":1,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.517426+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":1,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.559137+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":1,\"1\":5,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.600806+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\": 2, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.642519+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":2,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.684227+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":2,\"1\":3,\"2\":2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.725956+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":2,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.767664+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":4,\"1\":3,\"2\":2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.809416+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":3,\"1\":4,\"2\":2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.851128+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\": 3, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.892840+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\": 3, \"1\": 4, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.934538+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":2,\"2\":5}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:14.976280+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":2,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.017954+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":3,\"1\":4,\"2\":2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.059671+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":1,\"1\":5,\"2\":2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.101395+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\":2,\"1\":4,\"2\":1}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.143111+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":1,\"1\":1,\"2\":2,\"3\":5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.184851+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.226553+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":1,\"1\":1,\"2\":5,\"3\":5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.268300+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":1,\"1\":2,\"2\":4,\"3\":5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.310028+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.351755+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.393489+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":3,\"1\":2,\"2\":4,\"3\":1}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.435214+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":1,\"2\":4,\"3\":5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.476916+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.518557+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.560263+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":1,\"1\":2,\"2\":4,\"3\":5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.601964+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\":2,\"1\":3,\"2\":4,\"3\":5}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.643692+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\": 1, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.685472+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.727212+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.768937+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.810643+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.852324+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":1,\"1\":1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.894141+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.935775+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:15.977446+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.019157+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":5,\"1\":1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.060888+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.102575+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.144263+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.185983+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.227785+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.269459+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.311184+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.352883+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.394610+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.436302+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.478037+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.519740+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.561472+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.603250+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.644943+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.686623+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": ""} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.728281+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": ""} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.770016+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.811786+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.853500+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.895145+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.936905+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":2,\"1\":4}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:16.978622+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": ""} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.020302+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.062100+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"1\": 4}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.103817+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.145639+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\":5,\"1\":1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.187208+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":5,\"1\":3}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.228901+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.270599+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":5,\"1\":1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.312427+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":5,\"1\":1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.354134+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":5,\"1\":1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.395757+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.437486+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.479176+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.520965+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.562696+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":3,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.604435+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.646098+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.687817+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":5,\"1\":1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.729500+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.771210+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.812953+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\":5,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.854720+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\":5,\"1\":3}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.896447+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.938063+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:17.979877+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.021575+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.063374+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.105078+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.146754+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 0, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.188549+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 1, "text": "{\"0\":5,\"1\":4}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.230288+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 2, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.271969+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 3, "text": "{\"0\":5,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.313675+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 4, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.355404+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 5, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.397118+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 6, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.438900+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 7, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.480555+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 8, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.522303+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 9, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.564014+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 10, "text": "{\"0\":1,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.605729+00:00", "run_id": "20260916T130829Z_adc892c724ec", "sample": 11, "text": "{\"1\": 5}"} +{"event": "item_result", "failed_samples": 0, "id": "Homosexuality", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.048744993033786134, 0.048744993033786134, 0.07034712892471512, 0.08204265562024182, 0.09758418545056477, 0.1095475692889486, 0.11918632134149376, 0.12837192740209985, 0.13170526073543318, 0.1637249651689307], "p_samples": [[0.03333333333333333, 0.03333333333333333, 0.06666666666666667, 0.06666666666666667, 0.1, 0.1, 0.13333333333333333, 0.13333333333333333, 0.16666666666666666, 0.16666666666666666], [0.03125, 0.03125, 0.0625, 0.0625, 0.09375, 0.125, 0.125, 0.15625, 0.15625, 0.15625], [0.02857142857142857, 0.02857142857142857, 0.05714285714285714, 0.08571428571428572, 0.11428571428571428, 0.11428571428571428, 0.14285714285714285, 0.14285714285714285, 0.14285714285714285, 0.14285714285714285], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.027777777777777776, 0.027777777777777776, 0.05555555555555555, 0.08333333333333333, 0.1111111111111111, 0.1388888888888889, 0.1388888888888889, 0.1388888888888889, 0.1388888888888889, 0.1388888888888889], [0.034482758620689655, 0.034482758620689655, 0.06896551724137931, 0.06896551724137931, 0.10344827586206896, 0.10344827586206896, 0.13793103448275862, 0.13793103448275862, 0.13793103448275862, 0.1724137931034483], [0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.35714285714285715], [0.030303030303030304, 0.030303030303030304, 0.06060606060606061, 0.09090909090909091, 0.09090909090909091, 0.12121212121212122, 0.12121212121212122, 0.15151515151515152, 0.15151515151515152, 0.15151515151515152], [0.030303030303030304, 0.030303030303030304, 0.06060606060606061, 0.09090909090909091, 0.09090909090909091, 0.12121212121212122, 0.12121212121212122, 0.15151515151515152, 0.15151515151515152, 0.15151515151515152], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], "pmass_allowed": 0.8333333333333334, "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.639146+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":5,\"9\":5}", "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}", "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":4,\"6\":5,\"7\":5,\"8\":5,\"9\":5}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":4,\"5\":5,\"6\":5,\"7\":5,\"8\":5,\"9\":5}", "{\"0\":1,\"1\":1,\"2\":2,\"3\":2,\"4\":3,\"5\":3,\"6\":4,\"7\":4,\"8\":4,\"9\":5}", "{\"0\":1", "{\"0\":1,\"1\":1,\"2\":1,\"3\":1,\"4\":1,\"5\":1,\"6\":1,\"7\":1,\"8\":1,\"9\":5}", "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}", "", "{\"0\":1,\"1\":1,\"2\":2,\"3\":3,\"4\":3,\"5\":4,\"6\":4,\"7\":5,\"8\":5,\"9\":5}", "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}"], "valid_samples": 10} +{"event": "item_result", "failed_samples": 0, "id": "dealing with people?", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.5416666666666666, 0.4583333333333333], "p_samples": [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.6666666666666666, 0.3333333333333333], [0.5, 0.5], [0.6666666666666666, 0.3333333333333333], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.6666666666666666, 0.3333333333333333]], "pmass_allowed": 1.0, "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.672795+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":3,\"1\":3}", "{\"0\": 3, \"1\": 3}", "{\"0\":3,\"1\":3}", "{\"0\": 3, \"1\": 3}", "{\"0\": 3, \"1\": 3}", "{\"0\": 4, \"1\": 2}", "{\"0\": 3, \"1\": 3}", "{\"0\": 2, \"1\": 4}", "{\"0\": 3, \"1\": 3}", "{\"0\": 3, \"1\": 3}", "{\"0\": 3, \"1\": 3}", "{\"0\": 2, \"1\": 4}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Signing a petition", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.32748015873015873, 0.48148148148148145, 0.19103835978835979], "p_samples": [[0.5, 0.375, 0.125], [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], [0.4, 0.5, 0.1], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.125, 0.375, 0.5], [0.14285714285714285, 0.7142857142857143, 0.14285714285714285], [0.3333333333333333, 0.5555555555555556, 0.1111111111111111], [0.2857142857142857, 0.5714285714285714, 0.14285714285714285], [0.5, 0.375, 0.125], [0.14285714285714285, 0.7142857142857143, 0.14285714285714285], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.5, 0.375, 0.125]], "pmass_allowed": 1.0, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.705889+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":4,\"1\":3,\"2\":1}", "{\"0\": 3, \"1\": 3, \"2\": 3}", "{\"0\":4,\"1\":5,\"2\":1}", "{\"0\": 3, \"1\": 4, \"2\": 2}", "{\"0\":1,\"1\":3,\"2\":4}", "{\"0\":1,\"1\":5,\"2\":1}", "{\"0\":3,\"1\":5,\"2\":1}", "{\"0\":2,\"1\":4,\"2\":1}", "{\"0\":4,\"1\":3,\"2\":1}", "{\"0\":1,\"1\":5,\"2\":1}", "{\"0\": 3, \"1\": 4, \"2\": 2}", "{\"0\":4,\"1\":3,\"2\":1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.21775793650793648, 0.5067791005291006, 0.27546296296296297], "p_samples": [[0.3333333333333333, 0.3333333333333333, 0.3333333333333333], [0.3, 0.4, 0.3], [0.14285714285714285, 0.14285714285714285, 0.7142857142857143], [0.16666666666666666, 0.6666666666666666, 0.16666666666666666], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.125, 0.25, 0.625], [0.2857142857142857, 0.5714285714285714, 0.14285714285714285], [0.2, 0.6, 0.2], [0.16666666666666666, 0.6666666666666666, 0.16666666666666666], [0.16666666666666666, 0.6666666666666666, 0.16666666666666666], [0.14285714285714285, 0.7142857142857143, 0.14285714285714285], [0.25, 0.625, 0.125]], "pmass_allowed": 1.0, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.739334+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":3,\"1\":3,\"2\":3}", "{\"0\":3,\"1\":4,\"2\":3}", "{\"0\":1,\"1\":1,\"2\":5}", "{\"0\":1,\"1\":4,\"2\":1}", "{\"0\":3,\"1\":4,\"2\":2}", "{\"0\":1,\"1\":2,\"2\":5}", "{\"0\":2,\"1\":4,\"2\":1}", "{\"0\":1,\"1\":3,\"2\":1}", "{\"0\":1,\"1\":4,\"2\":1}", "{\"0\":1,\"1\":4,\"2\":1}", "{\"0\":1,\"1\":5,\"2\":1}", "{\"0\": 2, \"1\": 5, \"2\": 1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.29150132275132273, 0.4796626984126984, 0.22883597883597884], "p_samples": [[0.2857142857142857, 0.5714285714285714, 0.14285714285714285], [0.2857142857142857, 0.42857142857142855, 0.2857142857142857], [0.2857142857142857, 0.5714285714285714, 0.14285714285714285], [0.4444444444444444, 0.3333333333333333, 0.2222222222222222], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.375, 0.5, 0.125], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.125, 0.25, 0.625], [0.2857142857142857, 0.5714285714285714, 0.14285714285714285], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.125, 0.625, 0.25], [0.2857142857142857, 0.5714285714285714, 0.14285714285714285]], "pmass_allowed": 1.0, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.772729+00:00", "rescued_samples": 11, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":2,\"1\":4,\"2\":1}", "{\"0\":2,\"1\":3,\"2\":2}", "{\"0\":2,\"1\":4,\"2\":1}", "{\"0\":4,\"1\":3,\"2\":2}", "{\"0\":3,\"1\":4,\"2\":2}", "{\"0\": 3, \"1\": 4, \"2\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 2}", "{\"0\":1,\"1\":2,\"2\":5}", "{\"0\":2,\"1\":4,\"2\":1}", "{\"0\":3,\"1\":4,\"2\":2}", "{\"0\":1,\"1\":5,\"2\":2}", "{\"0\":2,\"1\":4,\"2\":1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Religion", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.1837000962000962, 0.1942159692159692, 0.29644059644059645, 0.3256433381433382], "p_samples": [[0.1111111111111111, 0.1111111111111111, 0.2222222222222222, 0.5555555555555556], [0.4166666666666667, 0.3333333333333333, 0.16666666666666666, 0.08333333333333333], [0.08333333333333333, 0.08333333333333333, 0.4166666666666667, 0.4166666666666667], [0.08333333333333333, 0.16666666666666666, 0.3333333333333333, 0.4166666666666667], [0.25, 0.25, 0.25, 0.25], [0.25, 0.25, 0.25, 0.25], [0.3, 0.2, 0.4, 0.1], [0.09090909090909091, 0.09090909090909091, 0.36363636363636365, 0.45454545454545453], [0.14285714285714285, 0.21428571428571427, 0.2857142857142857, 0.35714285714285715], [0.25, 0.25, 0.25, 0.25], [0.08333333333333333, 0.16666666666666666, 0.3333333333333333, 0.4166666666666667], [0.14285714285714285, 0.21428571428571427, 0.2857142857142857, 0.35714285714285715]], "pmass_allowed": 1.0, "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.806130+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":1,\"1\":1,\"2\":2,\"3\":5}", "{\"0\": 5, \"1\": 4, \"2\": 2, \"3\": 1}", "{\"0\":1,\"1\":1,\"2\":5,\"3\":5}", "{\"0\":1,\"1\":2,\"2\":4,\"3\":5}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}", "{\"0\":3,\"1\":2,\"2\":4,\"3\":1}", "{\"0\":1,\"1\":1,\"2\":4,\"3\":5}", "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3}", "{\"0\":1,\"1\":2,\"2\":4,\"3\":5}", "{\"0\":2,\"1\":3,\"2\":4,\"3\":5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "God", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.4444444444444444, 0.5555555555555556], "p_samples": [[0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.5, 0.5], [0.16666666666666666, 0.8333333333333334], [0.5, 0.5], [0.5, 0.5], [0.16666666666666666, 0.8333333333333334], [0.5, 0.5], [0.5, 0.5]], "pmass_allowed": 1.0, "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.839509+00:00", "rescued_samples": 11, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\": 1, \"1\": 1}", "{\"0\":3,\"1\":3}", "{\"0\":3,\"1\":3}", "{\"0\": 3, \"1\": 3}", "{\"0\": 3, \"1\": 3}", "{\"0\":1,\"1\":1}", "{\"0\": 5, \"1\": 1}", "{\"0\":3,\"1\":3}", "{\"0\":3,\"1\":3}", "{\"0\":5,\"1\":1}", "{\"0\":3,\"1\":3}", "{\"0\": 3, \"1\": 3}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Abortion", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.09185823754789273, 0.0975095785440613, 0.10316091954022989, 0.10881226053639849, 0.10871647509578547, 0.10584291187739464, 0.10019157088122606, 0.09454022988505746, 0.0975095785440613, 0.09185823754789273], "p_samples": [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.06896551724137931, 0.10344827586206896, 0.13793103448275862, 0.1724137931034483, 0.13793103448275862, 0.10344827586206896, 0.06896551724137931, 0.034482758620689655, 0.10344827586206896, 0.06896551724137931], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.16666666666666666, 0.13333333333333333, 0.1, 0.06666666666666667, 0.03333333333333333], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], "pmass_allowed": 1.0, "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.872896+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":2,\"1\":3,\"2\":4,\"3\":5,\"4\":4,\"5\":3,\"6\":2,\"7\":1,\"8\":3,\"9\":2}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5,\"5\":5,\"6\":4,\"7\":3,\"8\":2,\"9\":1}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}", "{\"0\":3,\"1\":3,\"2\":3,\"3\":3,\"4\":3,\"5\":3,\"6\":3,\"7\":3,\"8\":3,\"9\":3}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Obedience", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7000000000000001, 0.3], "p_samples": [[0.6, 0.4], [0.6, 0.4], [0.6666666666666666, 0.3333333333333333], [0.6666666666666666, 0.3333333333333333], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666]], "pmass_allowed": 0.5, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.914625+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":3,\"1\":2}", "", "", "{\"0\":3,\"1\":2}", "{\"0\": 4, \"1\": 2}", "{\"0\": 3}", "{\"0\": 3}", "{\"0\":2,\"1\":4}", "", "{\"0\":1,\"1\":5}", "{\"1\": 4}", "{\"0\":1,\"1\":5}"], "valid_samples": 6} +{"event": "item_result", "failed_samples": 0, "id": "Independence", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7916666666666666, 0.20833333333333334], "p_samples": [[0.8333333333333334, 0.16666666666666666], [0.625, 0.375], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.625, 0.375]], "pmass_allowed": 0.8333333333333334, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.956431+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\":5,\"1\":1}", "{\"0\":5,\"1\":3}", "{\"0\":5}", "{\"0\":5,\"1\":1}", "{\"0\":5,\"1\":1}", "{\"0\":5,\"1\":1}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"0\": 1, \"1\": 5}", "{\"0\":1,\"1\":5}", "{\"0\":3,\"1\":5}", "{\"1\": 5}"], "valid_samples": 10} +{"event": "item_result", "failed_samples": 0, "id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7924107142857142, 0.20758928571428573], "p_samples": [[0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.625, 0.375], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666]], "pmass_allowed": 0.6666666666666666, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:18.998175+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\": 5, \"1\": 1}", "{\"0\":5,\"1\":1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5}", "{\"0\":5,\"1\":2}", "{\"0\":5,\"1\":3}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"0\":5}", "{\"1\": 5}", "{\"1\":5}"], "valid_samples": 8} +{"event": "item_result", "failed_samples": 0, "id": "Imagination", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7474747474747474, 0.2525252525252525], "p_samples": [[0.5, 0.5], [0.5555555555555556, 0.4444444444444444], [0.8333333333333334, 0.16666666666666666], [0.5, 0.5], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666]], "pmass_allowed": 0.9166666666666666, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:19.039836+00:00", "rescued_samples": 12, "run_id": "20260916T130829Z_adc892c724ec", "texts": ["{\"0\": 5, \"1\": 5}", "{\"0\":5,\"1\":4}", "{\"0\": 5, \"1\": 1}", "{\"0\":5,\"1\":5}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"0\":1,\"1\":5}", "{\"1\": 5}"], "valid_samples": 11} +{"event": "run_finished", "failed_samples": 0, "model": "qwen/qwen3.7-flash", "planned_requests": 144, "protocol_id": "adc892c724ecef7cc61411a66738a6c3f73d6735ced5e939cdbde82577211b2c", "recorded_at_utc": "2026-09-16T13:18:19.081591+00:00", "rescued_samples": 142, "run_id": "20260916T130829Z_adc892c724ec", "valid_samples": 129} +{"event": "run_started", "items": [{"id": "Homosexuality", "n": 10, "options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "question": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality"}, {"id": "dealing with people?", "n": 2, "options": ["Most people can be trusted", "Need to be very careful"], "question": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?"}, {"id": "Signing a petition", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition"}, {"id": "Attending peaceful demonstrations", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations"}, {"id": "Joining in boycotts", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts"}, {"id": "Religion", "n": 4, "options": ["Very important", "Rather important", "Not very important", "Not at all important"], "question": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion"}, {"id": "God", "n": 2, "options": ["Yes", "No"], "question": "In which of the following things do you believe, if you believe in any?\n\nGod"}, {"id": "Abortion", "n": 10, "options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "question": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion"}, {"id": "Obedience", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience"}, {"id": "Independence", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence"}, {"id": "Determination, perseverance", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance"}, {"id": "Imagination", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination"}], "planned_requests": 144, "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:22:58.290759+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:22:58.351299+00:00", "request_id": "20260916T132258Z_c2918219d8c3_000", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:00.323807+00:00", "request_id": "20260916T132258Z_c2918219d8c3_000", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564978, "id": "gen-1789564978-OX84XgDxxwyp2BHhOb7y", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:00.375447+00:00", "request_id": "20260916T132258Z_c2918219d8c3_001", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:02.301767+00:00", "request_id": "20260916T132258Z_c2918219d8c3_001", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564980, "id": "gen-1789564980-gkE7NkvZYQIWGNn2xdMY", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:02.366508+00:00", "request_id": "20260916T132258Z_c2918219d8c3_002", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:03.695107+00:00", "request_id": "20260916T132258Z_c2918219d8c3_002", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564982, "id": "gen-1789564982-a5uWPxvJU0wpEqxgkMR5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:03.749585+00:00", "request_id": "20260916T132258Z_c2918219d8c3_003", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:05.539834+00:00", "request_id": "20260916T132258Z_c2918219d8c3_003", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 5,\n \"1\": 4,\n \"2\": 3,\n \"3\": 2,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564983, "id": "gen-1789564983-DfHdeco4ltsKmT2XnncI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:05.590992+00:00", "request_id": "20260916T132258Z_c2918219d8c3_004", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:06.848634+00:00", "request_id": "20260916T132258Z_c2918219d8c3_004", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564985, "id": "gen-1789564985-E4Uu2fpFwrjQOOh40Jzq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:06.907504+00:00", "request_id": "20260916T132258Z_c2918219d8c3_005", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:08.213418+00:00", "request_id": "20260916T132258Z_c2918219d8c3_005", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564986, "id": "gen-1789564986-Zlb7OwDCRsE9rivCf8Fh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:08.265672+00:00", "request_id": "20260916T132258Z_c2918219d8c3_006", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:09.959978+00:00", "request_id": "20260916T132258Z_c2918219d8c3_006", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 5,\n \"1\": 5,\n \"2\": 5,\n \"3\": 5,\n \"4\": 5,\n \"5\": 5,\n \"6\": 5,\n \"7\": 5,\n \"8\": 5,\n \"9\": 5\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564988, "id": "gen-1789564988-0IsUpBeqgu3LIaOQy4uh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:09.998833+00:00", "request_id": "20260916T132258Z_c2918219d8c3_007", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:11.272974+00:00", "request_id": "20260916T132258Z_c2918219d8c3_007", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564990, "id": "gen-1789564990-YWhrxt4gy7wE5YDP6qpB", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:11.357488+00:00", "request_id": "20260916T132258Z_c2918219d8c3_008", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:13.223929+00:00", "request_id": "20260916T132258Z_c2918219d8c3_008", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564991, "id": "gen-1789564991-yhs9yL2eOUQfvDctqQrj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:13.257007+00:00", "request_id": "20260916T132258Z_c2918219d8c3_009", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:15.080346+00:00", "request_id": "20260916T132258Z_c2918219d8c3_009", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564993, "id": "gen-1789564993-gL6VfPeCltajfPJu4zJz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:15.248338+00:00", "request_id": "20260916T132258Z_c2918219d8c3_010", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:16.520423+00:00", "request_id": "20260916T132258Z_c2918219d8c3_010", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564995, "id": "gen-1789564995-dxZ72AHg9HtfKuzHKT1r", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.32e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.32e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 240}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:16.573146+00:00", "request_id": "20260916T132258Z_c2918219d8c3_011", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:18.317161+00:00", "request_id": "20260916T132258Z_c2918219d8c3_011", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 5,\n \"1\": 4,\n \"2\": 3,\n \"3\": 2,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564996, "id": "gen-1789564996-asdYEZV06H3Zja0x4oPZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.606e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.606e-05, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:18.364596+00:00", "request_id": "20260916T132258Z_c2918219d8c3_012", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:19.466508+00:00", "request_id": "20260916T132258Z_c2918219d8c3_012", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564998, "id": "gen-1789564998-wcDyD9MP6XcGaN1mrkFo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:19.505971+00:00", "request_id": "20260916T132258Z_c2918219d8c3_013", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:20.486632+00:00", "request_id": "20260916T132258Z_c2918219d8c3_013", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789564999, "id": "gen-1789564999-jyHo5JctHR8lBF22jDvv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:20.531596+00:00", "request_id": "20260916T132258Z_c2918219d8c3_014", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:21.444591+00:00", "request_id": "20260916T132258Z_c2918219d8c3_014", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565000, "id": "gen-1789565000-bq7l5WQ20kcF3XOUVajQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:21.498186+00:00", "request_id": "20260916T132258Z_c2918219d8c3_015", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:22.318320+00:00", "request_id": "20260916T132258Z_c2918219d8c3_015", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565001, "id": "gen-1789565001-OjUiVcou9W3AVfisNZCn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:22.373139+00:00", "request_id": "20260916T132258Z_c2918219d8c3_016", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:23.251987+00:00", "request_id": "20260916T132258Z_c2918219d8c3_016", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565002, "id": "gen-1789565002-zgJ415uSHftu8AUFwvfz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:23.298090+00:00", "request_id": "20260916T132258Z_c2918219d8c3_017", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:24.155804+00:00", "request_id": "20260916T132258Z_c2918219d8c3_017", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565003, "id": "gen-1789565003-vXVd0LCQeFQ8WOjya6CD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:24.206497+00:00", "request_id": "20260916T132258Z_c2918219d8c3_018", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:25.078560+00:00", "request_id": "20260916T132258Z_c2918219d8c3_018", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565004, "id": "gen-1789565004-ca3DVtAPq2tFcsK0l6Cz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:25.122925+00:00", "request_id": "20260916T132258Z_c2918219d8c3_019", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:25.899203+00:00", "request_id": "20260916T132258Z_c2918219d8c3_019", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565005, "id": "gen-1789565005-lBZUS1OuNMqdGIoaa8Se", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:25.956177+00:00", "request_id": "20260916T132258Z_c2918219d8c3_020", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:26.813211+00:00", "request_id": "20260916T132258Z_c2918219d8c3_020", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565006, "id": "gen-1789565006-qX9Xj9DoKtvKu3W5nRFA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:26.872848+00:00", "request_id": "20260916T132258Z_c2918219d8c3_021", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:27.903892+00:00", "request_id": "20260916T132258Z_c2918219d8c3_021", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565006, "id": "gen-1789565006-PNjgWLZiNzkDQe3jUtBN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:27.955977+00:00", "request_id": "20260916T132258Z_c2918219d8c3_022", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:29.027347+00:00", "request_id": "20260916T132258Z_c2918219d8c3_022", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565008, "id": "gen-1789565008-Z9ygH3XDHYm52uAwAVUp", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:29.080831+00:00", "request_id": "20260916T132258Z_c2918219d8c3_023", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:29.854773+00:00", "request_id": "20260916T132258Z_c2918219d8c3_023", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565009, "id": "gen-1789565009-Z6RaxltcC67xXpvC7mLP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.58e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.58e-06, "upstream_inference_prompt_cost": 4.02e-06}, "is_byok": false, "prompt_tokens": 134, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:29.897420+00:00", "request_id": "20260916T132258Z_c2918219d8c3_024", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:31.077806+00:00", "request_id": "20260916T132258Z_c2918219d8c3_024", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565009, "id": "gen-1789565009-oACa8CVkZ2vq77VioOyH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:31.130576+00:00", "request_id": "20260916T132258Z_c2918219d8c3_025", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:31.972768+00:00", "request_id": "20260916T132258Z_c2918219d8c3_025", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565011, "id": "gen-1789565011-8VOKBSaanyKfoLD36OSa", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:32.030460+00:00", "request_id": "20260916T132258Z_c2918219d8c3_025", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:32.930341+00:00", "request_id": "20260916T132258Z_c2918219d8c3_025", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565012, "id": "gen-1789565012-jyDgtZ7jC0I2rWcYB1Nr", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:32.988684+00:00", "request_id": "20260916T132258Z_c2918219d8c3_026", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:33.876563+00:00", "request_id": "20260916T132258Z_c2918219d8c3_026", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565013, "id": "gen-1789565013-HJ0DvSAIoScKcMby3zt6", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:33.938654+00:00", "request_id": "20260916T132258Z_c2918219d8c3_026", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:35.184342+00:00", "request_id": "20260916T132258Z_c2918219d8c3_026", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565014, "id": "gen-1789565014-Vn5wfL7whaU1KCFTIZZj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.16e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.16e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.16e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.16e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:35.221799+00:00", "request_id": "20260916T132258Z_c2918219d8c3_027", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:36.165541+00:00", "request_id": "20260916T132258Z_c2918219d8c3_027", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565015, "id": "gen-1789565015-lxAkLwsXx1QB5EHlRkut", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:36.221670+00:00", "request_id": "20260916T132258Z_c2918219d8c3_028", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:37.077638+00:00", "request_id": "20260916T132258Z_c2918219d8c3_028", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565016, "id": "gen-1789565016-LdhwBW9J4gNB3UbbQI7V", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 4}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:37.138203+00:00", "request_id": "20260916T132258Z_c2918219d8c3_028", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:38.496529+00:00", "request_id": "20260916T132258Z_c2918219d8c3_028", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565017, "id": "gen-1789565017-2zpSVAoAMtOgn65WJDXD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:38.546328+00:00", "request_id": "20260916T132258Z_c2918219d8c3_029", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:39.666711+00:00", "request_id": "20260916T132258Z_c2918219d8c3_029", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565018, "id": "gen-1789565018-e2hC7xNx48DU7MltRMAG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:39.713173+00:00", "request_id": "20260916T132258Z_c2918219d8c3_030", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:41.084003+00:00", "request_id": "20260916T132258Z_c2918219d8c3_030", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565019, "id": "gen-1789565019-7qICv5azA1JP5mrn8htr", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:41.146276+00:00", "request_id": "20260916T132258Z_c2918219d8c3_030", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:41.992161+00:00", "request_id": "20260916T132258Z_c2918219d8c3_030", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565021, "id": "gen-1789565021-Lcl1Jfm7osvASt9ok79n", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:42.054836+00:00", "request_id": "20260916T132258Z_c2918219d8c3_031", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:42.974067+00:00", "request_id": "20260916T132258Z_c2918219d8c3_031", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565022, "id": "gen-1789565022-34RmbI9dyKE4dOyspkGB", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 198}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:43.021352+00:00", "request_id": "20260916T132258Z_c2918219d8c3_032", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:43.890948+00:00", "request_id": "20260916T132258Z_c2918219d8c3_032", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565023, "id": "gen-1789565023-dbeEDybRBLYym79puKKo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 4}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:43.937993+00:00", "request_id": "20260916T132258Z_c2918219d8c3_032", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:44.943972+00:00", "request_id": "20260916T132258Z_c2918219d8c3_032", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565023, "id": "gen-1789565023-SSluBZnFIvQVFrJmCr1D", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.16e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.16e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.16e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.16e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:44.996144+00:00", "request_id": "20260916T132258Z_c2918219d8c3_033", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:45.876407+00:00", "request_id": "20260916T132258Z_c2918219d8c3_033", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565025, "id": "gen-1789565025-TH7IjIHB5K5tAP4FNonj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:45.921062+00:00", "request_id": "20260916T132258Z_c2918219d8c3_033", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:46.824895+00:00", "request_id": "20260916T132258Z_c2918219d8c3_033", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565025, "id": "gen-1789565025-zcQeORqvNMDKX1gjzj6R", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:46.862597+00:00", "request_id": "20260916T132258Z_c2918219d8c3_034", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:47.693727+00:00", "request_id": "20260916T132258Z_c2918219d8c3_034", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565026, "id": "gen-1789565026-tUfkqUbhzXZz12DBM3Jh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:47.745888+00:00", "request_id": "20260916T132258Z_c2918219d8c3_034", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:48.580023+00:00", "request_id": "20260916T132258Z_c2918219d8c3_034", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565027, "id": "gen-1789565027-lAQecoVgq8dZUTXUSDyG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.03e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.03e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 251}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:48.620907+00:00", "request_id": "20260916T132258Z_c2918219d8c3_035", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:49.471932+00:00", "request_id": "20260916T132258Z_c2918219d8c3_035", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565028, "id": "gen-1789565028-7xvDg1OWjUglj5oO9pk1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 5.4e-06}, "is_byok": false, "prompt_tokens": 180, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 186}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:49.529104+00:00", "request_id": "20260916T132258Z_c2918219d8c3_035", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:51.767272+00:00", "request_id": "20260916T132258Z_c2918219d8c3_035", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565029, "id": "gen-1789565029-HF0ZHvlVCPlboKrnFP8j", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.16e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.16e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.16e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.16e-06, "upstream_inference_prompt_cost": 7.38e-06}, "is_byok": false, "prompt_tokens": 246, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:51.811885+00:00", "request_id": "20260916T132258Z_c2918219d8c3_036", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:52.764467+00:00", "request_id": "20260916T132258Z_c2918219d8c3_036", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565031, "id": "gen-1789565031-7q17f0lVnseHES731bcn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:52.803483+00:00", "request_id": "20260916T132258Z_c2918219d8c3_036", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:53.871779+00:00", "request_id": "20260916T132258Z_c2918219d8c3_036", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565032, "id": "gen-1789565032-RAriEk7sJuMyp2JcZYgG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:53.920044+00:00", "request_id": "20260916T132258Z_c2918219d8c3_037", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:54.769059+00:00", "request_id": "20260916T132258Z_c2918219d8c3_037", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565034, "id": "gen-1789565034-9BPCTEQhFzBFVvepPWRQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:54.820132+00:00", "request_id": "20260916T132258Z_c2918219d8c3_038", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:55.675155+00:00", "request_id": "20260916T132258Z_c2918219d8c3_038", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565034, "id": "gen-1789565034-o67lUmNJrcTDsRkreGlI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:55.728329+00:00", "request_id": "20260916T132258Z_c2918219d8c3_039", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:56.729709+00:00", "request_id": "20260916T132258Z_c2918219d8c3_039", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565035, "id": "gen-1789565035-W2qre3anyPCv0VP9uj6v", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 193}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 193}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 4, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 4, \"1\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:56.786609+00:00", "request_id": "20260916T132258Z_c2918219d8c3_039", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:57.872078+00:00", "request_id": "20260916T132258Z_c2918219d8c3_039", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4,\"1\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565036, "id": "gen-1789565036-06WZT8Hgx09RRnn726b5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 8.76e-06, "upstream_inference_prompt_cost": 7.59e-06}, "is_byok": false, "prompt_tokens": 253, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 8.76e-06, "upstream_inference_prompt_cost": 7.59e-06}, "is_byok": false, "prompt_tokens": 253, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:57.928036+00:00", "request_id": "20260916T132258Z_c2918219d8c3_040", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:58.766465+00:00", "request_id": "20260916T132258Z_c2918219d8c3_040", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565038, "id": "gen-1789565038-yxtM7lQiR2OO1ULeBF0z", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:58.812001+00:00", "request_id": "20260916T132258Z_c2918219d8c3_040", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:59.758928+00:00", "request_id": "20260916T132258Z_c2918219d8c3_040", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565038, "id": "gen-1789565038-Gx8lldwBDsZtOrOUyRQY", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:23:59.803354+00:00", "request_id": "20260916T132258Z_c2918219d8c3_041", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:03.773729+00:00", "request_id": "20260916T132258Z_c2918219d8c3_041", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565039, "id": "gen-1789565039-HzyJMcSf7tUQMRLi12cv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:03.818985+00:00", "request_id": "20260916T132258Z_c2918219d8c3_041", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:04.600182+00:00", "request_id": "20260916T132258Z_c2918219d8c3_041", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565043, "id": "gen-1789565043-muDkcDdIgX42lg9CMqbI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:04.652357+00:00", "request_id": "20260916T132258Z_c2918219d8c3_042", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:05.405745+00:00", "request_id": "20260916T132258Z_c2918219d8c3_042", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565044, "id": "gen-1789565044-JRrCtVNuFcB9J3eWwLl2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:05.502246+00:00", "request_id": "20260916T132258Z_c2918219d8c3_042", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:06.279811+00:00", "request_id": "20260916T132258Z_c2918219d8c3_042", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565045, "id": "gen-1789565045-R4osa2xD1pMPPmxVWfTM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.06e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.06e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.06e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.06e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:06.327228+00:00", "request_id": "20260916T132258Z_c2918219d8c3_043", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:07.381345+00:00", "request_id": "20260916T132258Z_c2918219d8c3_043", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565046, "id": "gen-1789565046-xxV5tdpjjyCHgEd5nEMH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 4}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:07.435395+00:00", "request_id": "20260916T132258Z_c2918219d8c3_043", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:08.509764+00:00", "request_id": "20260916T132258Z_c2918219d8c3_043", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565047, "id": "gen-1789565047-Mpx89QdKuM9d5dboujND", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.06e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.06e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.06e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.06e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 252}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:08.560184+00:00", "request_id": "20260916T132258Z_c2918219d8c3_044", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:09.633682+00:00", "request_id": "20260916T132258Z_c2918219d8c3_044", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565048, "id": "gen-1789565048-fdSecgAPBBrXxVwjkhtb", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 193}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 193}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 4}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:09.685319+00:00", "request_id": "20260916T132258Z_c2918219d8c3_044", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:10.530764+00:00", "request_id": "20260916T132258Z_c2918219d8c3_044", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565049, "id": "gen-1789565049-t5EbcpCbTHZvO6m4gcXP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 8.76e-06, "upstream_inference_prompt_cost": 7.59e-06}, "is_byok": false, "prompt_tokens": 253, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 8.76e-06, "upstream_inference_prompt_cost": 7.59e-06}, "is_byok": false, "prompt_tokens": 253, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 262}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:10.593769+00:00", "request_id": "20260916T132258Z_c2918219d8c3_045", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:11.655177+00:00", "request_id": "20260916T132258Z_c2918219d8c3_045", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565050, "id": "gen-1789565050-zmUJjhFRJaqZqI72KZRs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:11.710575+00:00", "request_id": "20260916T132258Z_c2918219d8c3_046", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:12.586312+00:00", "request_id": "20260916T132258Z_c2918219d8c3_046", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565051, "id": "gen-1789565051-soLTDzjpjW6jyhbKEwYM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.77e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.77e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 199}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:12.643791+00:00", "request_id": "20260916T132258Z_c2918219d8c3_047", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:13.898988+00:00", "request_id": "20260916T132258Z_c2918219d8c3_047", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565052, "id": "gen-1789565052-W07cvIovlVqeLg4bJXhG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.21e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.21e-06, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 187}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:13.943510+00:00", "request_id": "20260916T132258Z_c2918219d8c3_047", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:14.955775+00:00", "request_id": "20260916T132258Z_c2918219d8c3_047", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565054, "id": "gen-1789565054-6Ybp6PsYv4ifUfXrtnFP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.19e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.19e-06, "upstream_inference_prompt_cost": 7.41e-06}, "is_byok": false, "prompt_tokens": 247, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:15.010105+00:00", "request_id": "20260916T132258Z_c2918219d8c3_048", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:16.046815+00:00", "request_id": "20260916T132258Z_c2918219d8c3_048", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 4,\n \"1\": 3,\n \"2\": 5\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565055, "id": "gen-1789565055-igXfyUUtrRzk2xijHeOU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 26, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.84e-06, "cost_details": {"upstream_inference_completions_cost": 3.38e-06, "upstream_inference_cost": 8.84e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 208}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 26, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.84e-06, "cost_details": {"upstream_inference_completions_cost": 3.38e-06, "upstream_inference_cost": 8.84e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 208}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:16.093270+00:00", "request_id": "20260916T132258Z_c2918219d8c3_049", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:16.936278+00:00", "request_id": "20260916T132258Z_c2918219d8c3_049", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565056, "id": "gen-1789565056-dW11mPKOm7Vw9XwD19Z5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 4}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:17.001492+00:00", "request_id": "20260916T132258Z_c2918219d8c3_049", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:17.696607+00:00", "request_id": "20260916T132258Z_c2918219d8c3_049", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565057, "id": "gen-1789565057-4GW8zXrwijVoey5FKa04", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.22e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.22e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 254}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.22e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.22e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 254}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:17.726406+00:00", "request_id": "20260916T132258Z_c2918219d8c3_050", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:18.620596+00:00", "request_id": "20260916T132258Z_c2918219d8c3_050", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565057, "id": "gen-1789565057-vd3J5xvRHM2Za7Q8mYGW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:18.668178+00:00", "request_id": "20260916T132258Z_c2918219d8c3_051", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:19.653791+00:00", "request_id": "20260916T132258Z_c2918219d8c3_051", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 4, \"2\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565058, "id": "gen-1789565058-glmv05gc2UnkCBbiujRt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:19.701340+00:00", "request_id": "20260916T132258Z_c2918219d8c3_052", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:20.823381+00:00", "request_id": "20260916T132258Z_c2918219d8c3_052", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565059, "id": "gen-1789565059-DF7PQ3OM5JejVsS1FjoZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:20.876135+00:00", "request_id": "20260916T132258Z_c2918219d8c3_052", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:21.687198+00:00", "request_id": "20260916T132258Z_c2918219d8c3_052", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565060, "id": "gen-1789565060-Aa2BIZCwyvHsuUNPPllj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.22e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.22e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 254}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.22e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.22e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 254}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:21.751011+00:00", "request_id": "20260916T132258Z_c2918219d8c3_053", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:22.971704+00:00", "request_id": "20260916T132258Z_c2918219d8c3_053", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565061, "id": "gen-1789565061-UDNMzYZ7NXHR9kuR1ehq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 4}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:23.025797+00:00", "request_id": "20260916T132258Z_c2918219d8c3_053", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:23.842786+00:00", "request_id": "20260916T132258Z_c2918219d8c3_053", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565063, "id": "gen-1789565063-vfrQB054rwXQQVxkf97E", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.09e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.09e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.09e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 8.09e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 253}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:23.900771+00:00", "request_id": "20260916T132258Z_c2918219d8c3_054", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:25.031542+00:00", "request_id": "20260916T132258Z_c2918219d8c3_054", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565063, "id": "gen-1789565063-bJUTk9tE2F2Ct9nJQdcA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:25.084045+00:00", "request_id": "20260916T132258Z_c2918219d8c3_055", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:26.098493+00:00", "request_id": "20260916T132258Z_c2918219d8c3_055", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565065, "id": "gen-1789565065-cHh9WMqRsfHSzFEKx4fg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:26.142210+00:00", "request_id": "20260916T132258Z_c2918219d8c3_056", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:27.108287+00:00", "request_id": "20260916T132258Z_c2918219d8c3_056", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565066, "id": "gen-1789565066-utEdfMo5SJvNVDF9DiR1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:27.158738+00:00", "request_id": "20260916T132258Z_c2918219d8c3_057", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:27.986705+00:00", "request_id": "20260916T132258Z_c2918219d8c3_057", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565067, "id": "gen-1789565067-EN9ver0c9wZrr8Vas5WY", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:28.041946+00:00", "request_id": "20260916T132258Z_c2918219d8c3_058", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:29.142806+00:00", "request_id": "20260916T132258Z_c2918219d8c3_058", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565068, "id": "gen-1789565068-r53uS7MkRiQBUFhrqzkm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.24e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.24e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 188}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:29.191805+00:00", "request_id": "20260916T132258Z_c2918219d8c3_058", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:34.994723+00:00", "request_id": "20260916T132258Z_c2918219d8c3_058", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565069, "id": "gen-1789565069-z1hGqbERDfTdbxrZ9GNV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.22e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.22e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 254}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.22e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 8.22e-06, "upstream_inference_prompt_cost": 7.44e-06}, "is_byok": false, "prompt_tokens": 248, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 254}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:35.065277+00:00", "request_id": "20260916T132258Z_c2918219d8c3_059", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:35.877117+00:00", "request_id": "20260916T132258Z_c2918219d8c3_059", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565075, "id": "gen-1789565075-iSmtICshKcoCTHu0mAwF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:35.923447+00:00", "request_id": "20260916T132258Z_c2918219d8c3_060", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:36.830105+00:00", "request_id": "20260916T132258Z_c2918219d8c3_060", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565076, "id": "gen-1789565076-5F3AyDqFQqeZd1GPRUoS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:36.881647+00:00", "request_id": "20260916T132258Z_c2918219d8c3_060", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:37.852751+00:00", "request_id": "20260916T132258Z_c2918219d8c3_060", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565076, "id": "gen-1789565076-9ANFQ8AqD3cp0n0ma01n", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:37.906578+00:00", "request_id": "20260916T132258Z_c2918219d8c3_061", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:38.703308+00:00", "request_id": "20260916T132258Z_c2918219d8c3_061", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565077, "id": "gen-1789565077-nuGYucrrKzcU3EiWASyT", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:38.748148+00:00", "request_id": "20260916T132258Z_c2918219d8c3_061", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:40.125715+00:00", "request_id": "20260916T132258Z_c2918219d8c3_061", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565078, "id": "gen-1789565078-6EAoNd9wa6gO2GuRulYf", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:40.156553+00:00", "request_id": "20260916T132258Z_c2918219d8c3_062", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:41.599736+00:00", "request_id": "20260916T132258Z_c2918219d8c3_062", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565080, "id": "gen-1789565080-U8j5dYnG1cMbrCsl51j5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 1, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 1, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:41.648272+00:00", "request_id": "20260916T132258Z_c2918219d8c3_062", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:42.822293+00:00", "request_id": "20260916T132258Z_c2918219d8c3_062", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565081, "id": "gen-1789565081-DZFPLF4TbqnTtK3i7Hdd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.34e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 8.34e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 238}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.34e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 8.34e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 238}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:42.873719+00:00", "request_id": "20260916T132258Z_c2918219d8c3_063", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:43.937290+00:00", "request_id": "20260916T132258Z_c2918219d8c3_063", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565082, "id": "gen-1789565082-YpTiyQFmwUmKfxaIY5ew", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 1, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 1, \"1\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:43.990160+00:00", "request_id": "20260916T132258Z_c2918219d8c3_063", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:45.027292+00:00", "request_id": "20260916T132258Z_c2918219d8c3_063", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565084, "id": "gen-1789565084-qMNe8REQACzjM8r3exPt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:45.081715+00:00", "request_id": "20260916T132258Z_c2918219d8c3_064", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:46.080374+00:00", "request_id": "20260916T132258Z_c2918219d8c3_064", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565085, "id": "gen-1789565085-TM293HN2s6mSkCmiFasK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:46.131482+00:00", "request_id": "20260916T132258Z_c2918219d8c3_064", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:51.216569+00:00", "request_id": "20260916T132258Z_c2918219d8c3_064", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565086, "id": "gen-1789565086-F8wSP0PuFOGsaSG1POmA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:51.263735+00:00", "request_id": "20260916T132258Z_c2918219d8c3_065", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:52.270613+00:00", "request_id": "20260916T132258Z_c2918219d8c3_065", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565091, "id": "gen-1789565091-xqYDMtobA8g7Ef75DHLM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:52.321815+00:00", "request_id": "20260916T132258Z_c2918219d8c3_065", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:53.306680+00:00", "request_id": "20260916T132258Z_c2918219d8c3_065", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565092, "id": "gen-1789565092-achvdE1V9u3HfEyR3FZ6", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:53.371658+00:00", "request_id": "20260916T132258Z_c2918219d8c3_066", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:54.216710+00:00", "request_id": "20260916T132258Z_c2918219d8c3_066", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565093, "id": "gen-1789565093-cewRAwp4uVk4dy6WIVFX", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 2, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 2, \"1\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:54.271712+00:00", "request_id": "20260916T132258Z_c2918219d8c3_066", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:59.062029+00:00", "request_id": "20260916T132258Z_c2918219d8c3_066", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565094, "id": "gen-1789565094-LA5CqztCIpBPIwbM3mdO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.34e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 8.34e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 238}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 8.34e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 8.34e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 238}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:24:59.103708+00:00", "request_id": "20260916T132258Z_c2918219d8c3_067", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:00.404093+00:00", "request_id": "20260916T132258Z_c2918219d8c3_067", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565099, "id": "gen-1789565099-MbLcP0UT0zO32sgBRT9c", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:00.461776+00:00", "request_id": "20260916T132258Z_c2918219d8c3_067", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:05.593271+00:00", "request_id": "20260916T132258Z_c2918219d8c3_067", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565100, "id": "gen-1789565100-IihTJbMYKj5rREz3Iz1M", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:05.668738+00:00", "request_id": "20260916T132258Z_c2918219d8c3_068", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:11.602723+00:00", "request_id": "20260916T132258Z_c2918219d8c3_068", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565105, "id": "gen-1789565105-cu9oE51RJb9fVpnVhxcg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:11.659550+00:00", "request_id": "20260916T132258Z_c2918219d8c3_068", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:12.496518+00:00", "request_id": "20260916T132258Z_c2918219d8c3_068", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565111, "id": "gen-1789565111-sUsj8eYGpToNXcZeDeeA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:12.542731+00:00", "request_id": "20260916T132258Z_c2918219d8c3_069", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:13.420553+00:00", "request_id": "20260916T132258Z_c2918219d8c3_069", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565112, "id": "gen-1789565112-uve3Baj4A4kFTq9q3x6z", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 2, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 2, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:13.467589+00:00", "request_id": "20260916T132258Z_c2918219d8c3_069", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:14.250796+00:00", "request_id": "20260916T132258Z_c2918219d8c3_069", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":2,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565113, "id": "gen-1789565113-3o6NCmfcTaQjjxn7qhOZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:14.309248+00:00", "request_id": "20260916T132258Z_c2918219d8c3_070", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:15.168676+00:00", "request_id": "20260916T132258Z_c2918219d8c3_070", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565114, "id": "gen-1789565114-ZPqDgt9Qko7TqzQLBvrb", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:15.234173+00:00", "request_id": "20260916T132258Z_c2918219d8c3_070", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:16.055591+00:00", "request_id": "20260916T132258Z_c2918219d8c3_070", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565115, "id": "gen-1789565115-1HAg3KwMEGSLGLsKUlWt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:16.109205+00:00", "request_id": "20260916T132258Z_c2918219d8c3_071", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:16.907460+00:00", "request_id": "20260916T132258Z_c2918219d8c3_071", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565116, "id": "gen-1789565116-m4qtUGDSrmELh6bhIqZm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.18e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 6.18e-06, "upstream_inference_prompt_cost": 4.62e-06}, "is_byok": false, "prompt_tokens": 154, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 166}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3, \"1\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:16.959009+00:00", "request_id": "20260916T132258Z_c2918219d8c3_071", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:22.369482+00:00", "request_id": "20260916T132258Z_c2918219d8c3_071", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3,\"1\":2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565117, "id": "gen-1789565117-yFvxqapYMFnPOsuYIGri", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.95e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 7.95e-06, "upstream_inference_prompt_cost": 6.78e-06}, "is_byok": false, "prompt_tokens": 226, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 235}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:22.457735+00:00", "request_id": "20260916T132258Z_c2918219d8c3_072", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:23.516844+00:00", "request_id": "20260916T132258Z_c2918219d8c3_072", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565122, "id": "gen-1789565122-NYWQYdQvzuxViXYErMCV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:23.574188+00:00", "request_id": "20260916T132258Z_c2918219d8c3_073", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:24.538705+00:00", "request_id": "20260916T132258Z_c2918219d8c3_073", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565123, "id": "gen-1789565123-YdPLXtCYutZOlsclB25d", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:24.654471+00:00", "request_id": "20260916T132258Z_c2918219d8c3_074", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:25.738917+00:00", "request_id": "20260916T132258Z_c2918219d8c3_074", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565124, "id": "gen-1789565124-NDeuqGqia6HmD9lFGKKm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:25.782487+00:00", "request_id": "20260916T132258Z_c2918219d8c3_075", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:26.881266+00:00", "request_id": "20260916T132258Z_c2918219d8c3_075", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565126, "id": "gen-1789565126-my1Yo9qTKSZQVLSGHMnG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:26.915671+00:00", "request_id": "20260916T132258Z_c2918219d8c3_076", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:27.876713+00:00", "request_id": "20260916T132258Z_c2918219d8c3_076", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565126, "id": "gen-1789565126-wb2nva9nWExXAmBBhCF4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:27.932157+00:00", "request_id": "20260916T132258Z_c2918219d8c3_077", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:29.355028+00:00", "request_id": "20260916T132258Z_c2918219d8c3_077", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565128, "id": "gen-1789565128-OOazuh4HNdENMhvbhtZO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:29.415198+00:00", "request_id": "20260916T132258Z_c2918219d8c3_078", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:30.493246+00:00", "request_id": "20260916T132258Z_c2918219d8c3_078", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565129, "id": "gen-1789565129-rEZQRdQxMjVkrKNtnetT", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:30.531670+00:00", "request_id": "20260916T132258Z_c2918219d8c3_079", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:32.470998+00:00", "request_id": "20260916T132258Z_c2918219d8c3_079", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565130, "id": "gen-1789565130-9YyipydEK1Pd2qxQ89sz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:32.514501+00:00", "request_id": "20260916T132258Z_c2918219d8c3_080", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:33.413495+00:00", "request_id": "20260916T132258Z_c2918219d8c3_080", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 0}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565132, "id": "gen-1789565132-10szObkFqlCw70y24KOs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5, \"1\": 0}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5, \"1\": 0}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:33.481045+00:00", "request_id": "20260916T132258Z_c2918219d8c3_080", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:34.276253+00:00", "request_id": "20260916T132258Z_c2918219d8c3_080", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":0}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565133, "id": "gen-1789565133-ZsM6jouo2LuGcwsADkXe", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.9e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 6.9e-06, "upstream_inference_prompt_cost": 5.73e-06}, "is_byok": false, "prompt_tokens": 191, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.9e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 6.9e-06, "upstream_inference_prompt_cost": 5.73e-06}, "is_byok": false, "prompt_tokens": 191, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:34.322673+00:00", "request_id": "20260916T132258Z_c2918219d8c3_081", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:35.452684+00:00", "request_id": "20260916T132258Z_c2918219d8c3_081", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 0}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565134, "id": "gen-1789565134-vANDbLN7nzCYNWjYGksz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5, \"1\": 0}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5, \"1\": 0}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:35.489147+00:00", "request_id": "20260916T132258Z_c2918219d8c3_081", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:36.696342+00:00", "request_id": "20260916T132258Z_c2918219d8c3_081", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":0}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565135, "id": "gen-1789565135-fY3e1yWW72xwk59LoRpv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.9e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 6.9e-06, "upstream_inference_prompt_cost": 5.73e-06}, "is_byok": false, "prompt_tokens": 191, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.9e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 6.9e-06, "upstream_inference_prompt_cost": 5.73e-06}, "is_byok": false, "prompt_tokens": 191, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:36.747256+00:00", "request_id": "20260916T132258Z_c2918219d8c3_082", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:37.709089+00:00", "request_id": "20260916T132258Z_c2918219d8c3_082", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565136, "id": "gen-1789565136-Elny1oOp7mR5HxZSZirh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:37.763803+00:00", "request_id": "20260916T132258Z_c2918219d8c3_083", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:38.839480+00:00", "request_id": "20260916T132258Z_c2918219d8c3_083", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 0}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565137, "id": "gen-1789565137-8j5tXyhFqVccKCItLf27", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.13e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.13e-06, "upstream_inference_prompt_cost": 3.57e-06}, "is_byok": false, "prompt_tokens": 119, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 131}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5, \"1\": 0}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5, \"1\": 0}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:38.897012+00:00", "request_id": "20260916T132258Z_c2918219d8c3_083", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:39.861165+00:00", "request_id": "20260916T132258Z_c2918219d8c3_083", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5,\"1\":0}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565138, "id": "gen-1789565138-NJJN5mWLKJZ9WCDkGHih", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.9e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 6.9e-06, "upstream_inference_prompt_cost": 5.73e-06}, "is_byok": false, "prompt_tokens": 191, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 9, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.9e-06, "cost_details": {"upstream_inference_completions_cost": 1.17e-06, "upstream_inference_cost": 6.9e-06, "upstream_inference_prompt_cost": 5.73e-06}, "is_byok": false, "prompt_tokens": 191, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:39.905444+00:00", "request_id": "20260916T132258Z_c2918219d8c3_084", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:41.607374+00:00", "request_id": "20260916T132258Z_c2918219d8c3_084", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565139, "id": "gen-1789565139-tYETBLPRMzv1uNXpdjuR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:41.656139+00:00", "request_id": "20260916T132258Z_c2918219d8c3_085", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:43.345867+00:00", "request_id": "20260916T132258Z_c2918219d8c3_085", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565141, "id": "gen-1789565141-CbbENz58qzRCbfwgWkeW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:43.397277+00:00", "request_id": "20260916T132258Z_c2918219d8c3_086", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:44.607747+00:00", "request_id": "20260916T132258Z_c2918219d8c3_086", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565143, "id": "gen-1789565143-Mbv9gs8CyNMGpjbSbxZd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:44.647034+00:00", "request_id": "20260916T132258Z_c2918219d8c3_087", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:45.771629+00:00", "request_id": "20260916T132258Z_c2918219d8c3_087", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565144, "id": "gen-1789565144-i0OdnDVfmGZYUhB7Iokf", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 30, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 9.27e-06, "cost_details": {"upstream_inference_completions_cost": 3.9e-06, "upstream_inference_cost": 9.27e-06, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 209}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 30, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 9.27e-06, "cost_details": {"upstream_inference_completions_cost": 3.9e-06, "upstream_inference_cost": 9.27e-06, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 209}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:45.813511+00:00", "request_id": "20260916T132258Z_c2918219d8c3_087", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:46.767911+00:00", "request_id": "20260916T132258Z_c2918219d8c3_087", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565145, "id": "gen-1789565145-snSf1PfLA2d8QLHGxT3O", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 21, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.08e-05, "cost_details": {"upstream_inference_completions_cost": 2.73e-06, "upstream_inference_cost": 1.08e-05, "upstream_inference_prompt_cost": 8.07e-06}, "is_byok": false, "prompt_tokens": 269, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 290}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 21, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.08e-05, "cost_details": {"upstream_inference_completions_cost": 2.73e-06, "upstream_inference_cost": 1.08e-05, "upstream_inference_prompt_cost": 8.07e-06}, "is_byok": false, "prompt_tokens": 269, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 290}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:46.821748+00:00", "request_id": "20260916T132258Z_c2918219d8c3_088", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:48.640815+00:00", "request_id": "20260916T132258Z_c2918219d8c3_088", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565146, "id": "gen-1789565146-EEajbvEJGJRulJbEWli6", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:48.679953+00:00", "request_id": "20260916T132258Z_c2918219d8c3_089", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:50.366345+00:00", "request_id": "20260916T132258Z_c2918219d8c3_089", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 3,\n \"1\": 4,\n \"2\": 5,\n \"3\": 4,\n \"4\": 3,\n \"5\": 2,\n \"6\": 2,\n \"7\": 2,\n \"8\": 2,\n \"9\": 1\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565148, "id": "gen-1789565148-Y4Jw54RT6HMOW9SPaMwS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:50.421187+00:00", "request_id": "20260916T132258Z_c2918219d8c3_090", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:52.259425+00:00", "request_id": "20260916T132258Z_c2918219d8c3_090", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565150, "id": "gen-1789565150-5drenuevK8hxVDjlICC6", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:52.312449+00:00", "request_id": "20260916T132258Z_c2918219d8c3_091", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:53.790637+00:00", "request_id": "20260916T132258Z_c2918219d8c3_091", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 1,\n \"1\": 2,\n \"2\": 3,\n \"3\": 4,\n \"4\": 5,\n \"5\": 5,\n \"6\": 5,\n \"7\": 5,\n \"8\": 5,\n \"9\": 5\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565152, "id": "gen-1789565152-48Q2GRY4llJ8pPayL7XK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:53.837214+00:00", "request_id": "20260916T132258Z_c2918219d8c3_092", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:55.200327+00:00", "request_id": "20260916T132258Z_c2918219d8c3_092", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565153, "id": "gen-1789565153-6WceQ5Z3Np2DRSgWAaOv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 82, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.603e-05, "cost_details": {"upstream_inference_completions_cost": 1.066e-05, "upstream_inference_cost": 1.603e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 261}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:55.245404+00:00", "request_id": "20260916T132258Z_c2918219d8c3_093", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:56.551800+00:00", "request_id": "20260916T132258Z_c2918219d8c3_093", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 4, \"4\": 3, \"5\": 2, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565155, "id": "gen-1789565155-QfQ8dXEcDCzZyQT6HztZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:56.586807+00:00", "request_id": "20260916T132258Z_c2918219d8c3_094", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:57.893456+00:00", "request_id": "20260916T132258Z_c2918219d8c3_094", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565156, "id": "gen-1789565156-DNdTOsA8yMrCrbADtnha", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:57.936547+00:00", "request_id": "20260916T132258Z_c2918219d8c3_095", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:59.302938+00:00", "request_id": "20260916T132258Z_c2918219d8c3_095", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565158, "id": "gen-1789565158-kuto49QXDcFrs2hrRJfK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.317e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.317e-05, "upstream_inference_prompt_cost": 5.37e-06}, "is_byok": false, "prompt_tokens": 179, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 239}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:25:59.353061+00:00", "request_id": "20260916T132258Z_c2918219d8c3_096", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:00.300574+00:00", "request_id": "20260916T132258Z_c2918219d8c3_096", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565159, "id": "gen-1789565159-F7ketwthjnWzs4MsNF7q", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:00.344625+00:00", "request_id": "20260916T132258Z_c2918219d8c3_096", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:01.133926+00:00", "request_id": "20260916T132258Z_c2918219d8c3_096", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565160, "id": "gen-1789565160-7AHPxwy662pJADbLGFmG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:01.186240+00:00", "request_id": "20260916T132258Z_c2918219d8c3_097", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:02.136654+00:00", "request_id": "20260916T132258Z_c2918219d8c3_097", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565161, "id": "gen-1789565161-VZPMiz9NlLtIo82pxn7F", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:02.202771+00:00", "request_id": "20260916T132258Z_c2918219d8c3_097", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:03.000596+00:00", "request_id": "20260916T132258Z_c2918219d8c3_097", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565162, "id": "gen-1789565162-XkEyXGJVUmIG6W0DlPN4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.86e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.86e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.86e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.86e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:03.052665+00:00", "request_id": "20260916T132258Z_c2918219d8c3_098", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:08.874189+00:00", "request_id": "20260916T132258Z_c2918219d8c3_098", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565163, "id": "gen-1789565163-GVvKnTDPp8AzRpykZTVW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:08.926095+00:00", "request_id": "20260916T132258Z_c2918219d8c3_098", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:10.044985+00:00", "request_id": "20260916T132258Z_c2918219d8c3_098", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565169, "id": "gen-1789565169-AiQP4dItnCrSMrGPS46U", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:10.109535+00:00", "request_id": "20260916T132258Z_c2918219d8c3_099", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:11.043613+00:00", "request_id": "20260916T132258Z_c2918219d8c3_099", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565170, "id": "gen-1789565170-qTARwSSY0QPDx1zkjXHW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:11.101190+00:00", "request_id": "20260916T132258Z_c2918219d8c3_100", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:11.927947+00:00", "request_id": "20260916T132258Z_c2918219d8c3_100", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565171, "id": "gen-1789565171-FrTc8dB3B5xfKNqvb34N", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:11.984777+00:00", "request_id": "20260916T132258Z_c2918219d8c3_101", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:12.961302+00:00", "request_id": "20260916T132258Z_c2918219d8c3_101", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565172, "id": "gen-1789565172-s0gpj8dYWoYVQTHxrYcu", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 3}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:13.009549+00:00", "request_id": "20260916T132258Z_c2918219d8c3_101", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:13.760389+00:00", "request_id": "20260916T132258Z_c2918219d8c3_101", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565173, "id": "gen-1789565173-QYoldM94hy7OC0iD3GNy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.86e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.86e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.86e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.86e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:13.817766+00:00", "request_id": "20260916T132258Z_c2918219d8c3_102", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:14.773187+00:00", "request_id": "20260916T132258Z_c2918219d8c3_102", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565173, "id": "gen-1789565173-LmAf9fJlS2HgbP6R3gtL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:14.826052+00:00", "request_id": "20260916T132258Z_c2918219d8c3_103", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:15.737856+00:00", "request_id": "20260916T132258Z_c2918219d8c3_103", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565174, "id": "gen-1789565174-BmTpHoNYEDh8r6npKm3X", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:15.775927+00:00", "request_id": "20260916T132258Z_c2918219d8c3_104", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:16.556183+00:00", "request_id": "20260916T132258Z_c2918219d8c3_104", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565175, "id": "gen-1789565175-f6q5QRaB92n8KeBivHUn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:16.592493+00:00", "request_id": "20260916T132258Z_c2918219d8c3_104", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:17.546859+00:00", "request_id": "20260916T132258Z_c2918219d8c3_104", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565176, "id": "gen-1789565176-BRyIxzbkuIS6mTngFfCq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:17.575677+00:00", "request_id": "20260916T132258Z_c2918219d8c3_105", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:18.325191+00:00", "request_id": "20260916T132258Z_c2918219d8c3_105", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565177, "id": "gen-1789565177-5imljywsZ1AGu1XArDp8", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.01e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.01e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 147}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 2}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:18.375759+00:00", "request_id": "20260916T132258Z_c2918219d8c3_105", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:19.140051+00:00", "request_id": "20260916T132258Z_c2918219d8c3_105", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565178, "id": "gen-1789565178-PL5VKewXJ4AEdyizPtUS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.99e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.99e-06, "upstream_inference_prompt_cost": 6.21e-06}, "is_byok": false, "prompt_tokens": 207, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:19.192304+00:00", "request_id": "20260916T132258Z_c2918219d8c3_106", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:20.543720+00:00", "request_id": "20260916T132258Z_c2918219d8c3_106", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565179, "id": "gen-1789565179-N09AksG2yMqNbHowlQta", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:20.583753+00:00", "request_id": "20260916T132258Z_c2918219d8c3_107", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:21.533225+00:00", "request_id": "20260916T132258Z_c2918219d8c3_107", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565180, "id": "gen-1789565180-STRrtDiJxDtDGPS4bjOa", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.79e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.79e-06, "upstream_inference_prompt_cost": 4.23e-06}, "is_byok": false, "prompt_tokens": 141, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 153}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:21.575270+00:00", "request_id": "20260916T132258Z_c2918219d8c3_108", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:22.461291+00:00", "request_id": "20260916T132258Z_c2918219d8c3_108", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565181, "id": "gen-1789565181-USovadOR4XUHLufGaYQD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:22.500237+00:00", "request_id": "20260916T132258Z_c2918219d8c3_109", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:23.311786+00:00", "request_id": "20260916T132258Z_c2918219d8c3_109", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565182, "id": "gen-1789565182-L7nlm11XhO11TuxsNun5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:23.375033+00:00", "request_id": "20260916T132258Z_c2918219d8c3_109", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:24.291053+00:00", "request_id": "20260916T132258Z_c2918219d8c3_109", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565183, "id": "gen-1789565183-UNMA2NrX8VDsdQ9Rfdb7", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:24.341812+00:00", "request_id": "20260916T132258Z_c2918219d8c3_110", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:25.170652+00:00", "request_id": "20260916T132258Z_c2918219d8c3_110", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565184, "id": "gen-1789565184-P1br6DChWS1WK8eq8aVv", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:25.200034+00:00", "request_id": "20260916T132258Z_c2918219d8c3_110", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:25.980846+00:00", "request_id": "20260916T132258Z_c2918219d8c3_110", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565185, "id": "gen-1789565185-jbQSTvQazX4cNU71rd30", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:26.025066+00:00", "request_id": "20260916T132258Z_c2918219d8c3_111", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:26.916210+00:00", "request_id": "20260916T132258Z_c2918219d8c3_111", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565186, "id": "gen-1789565186-8ISeQxiYQJvoKBaDKusE", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:26.984844+00:00", "request_id": "20260916T132258Z_c2918219d8c3_111", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:27.807659+00:00", "request_id": "20260916T132258Z_c2918219d8c3_111", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565187, "id": "gen-1789565187-jMAQ6CFELB4bWH0xVQRh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:27.878500+00:00", "request_id": "20260916T132258Z_c2918219d8c3_112", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:28.977203+00:00", "request_id": "20260916T132258Z_c2918219d8c3_112", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565188, "id": "gen-1789565188-ULFTKWYwcC4XfPCP4guj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:29.038386+00:00", "request_id": "20260916T132258Z_c2918219d8c3_113", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:29.997044+00:00", "request_id": "20260916T132258Z_c2918219d8c3_113", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565189, "id": "gen-1789565189-RlBK2EjQ3PmwZfrnrL4f", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:30.106744+00:00", "request_id": "20260916T132258Z_c2918219d8c3_114", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:31.176607+00:00", "request_id": "20260916T132258Z_c2918219d8c3_114", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565190, "id": "gen-1789565190-EfgcTBwb1CSYzo7dEqqw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:31.507473+00:00", "request_id": "20260916T132258Z_c2918219d8c3_114", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:36.431099+00:00", "request_id": "20260916T132258Z_c2918219d8c3_114", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565191, "id": "gen-1789565191-eJIn3fr2Z4Qeel29piPH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:36.473738+00:00", "request_id": "20260916T132258Z_c2918219d8c3_115", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:37.601028+00:00", "request_id": "20260916T132258Z_c2918219d8c3_115", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565196, "id": "gen-1789565196-AuaqIjhbJ5OVk0zjNjmt", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:37.716893+00:00", "request_id": "20260916T132258Z_c2918219d8c3_115", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:38.548430+00:00", "request_id": "20260916T132258Z_c2918219d8c3_115", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565197, "id": "gen-1789565197-MpZMcIoOobYrjOtwKzfb", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:38.601472+00:00", "request_id": "20260916T132258Z_c2918219d8c3_116", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:39.538153+00:00", "request_id": "20260916T132258Z_c2918219d8c3_116", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565198, "id": "gen-1789565198-rJPEnHJJfZ2YuCqDBb11", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:39.578128+00:00", "request_id": "20260916T132258Z_c2918219d8c3_116", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:40.329561+00:00", "request_id": "20260916T132258Z_c2918219d8c3_116", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565199, "id": "gen-1789565199-dH8VxZiz4GKvjsUo6gav", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:40.430054+00:00", "request_id": "20260916T132258Z_c2918219d8c3_117", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:41.336805+00:00", "request_id": "20260916T132258Z_c2918219d8c3_117", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565200, "id": "gen-1789565200-JAw1wfOhUTws34UyzPMk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:41.399021+00:00", "request_id": "20260916T132258Z_c2918219d8c3_117", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:42.116438+00:00", "request_id": "20260916T132258Z_c2918219d8c3_117", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565201, "id": "gen-1789565201-CASF0XTRPpSQWKdctWKp", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:42.249996+00:00", "request_id": "20260916T132258Z_c2918219d8c3_118", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:43.103380+00:00", "request_id": "20260916T132258Z_c2918219d8c3_118", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565202, "id": "gen-1789565202-t2eOIIAtmcV7lmBY5Ij0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:43.150783+00:00", "request_id": "20260916T132258Z_c2918219d8c3_118", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:44.071752+00:00", "request_id": "20260916T132258Z_c2918219d8c3_118", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565203, "id": "gen-1789565203-RP90kmsshgLhNAVGxurQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:44.110537+00:00", "request_id": "20260916T132258Z_c2918219d8c3_119", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:44.958970+00:00", "request_id": "20260916T132258Z_c2918219d8c3_119", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565204, "id": "gen-1789565204-pxWR2CKLVdRPZleu9pT4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:45.010483+00:00", "request_id": "20260916T132258Z_c2918219d8c3_119", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:45.833058+00:00", "request_id": "20260916T132258Z_c2918219d8c3_119", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565205, "id": "gen-1789565205-zsnqQHs2NluyjAZP2tll", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:45.877063+00:00", "request_id": "20260916T132258Z_c2918219d8c3_120", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:46.786841+00:00", "request_id": "20260916T132258Z_c2918219d8c3_120", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565205, "id": "gen-1789565205-aCGGggGdz2QC1MVqP9nW", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:46.835255+00:00", "request_id": "20260916T132258Z_c2918219d8c3_120", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:47.669284+00:00", "request_id": "20260916T132258Z_c2918219d8c3_120", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565206, "id": "gen-1789565206-P6do38P7vEaVPx9egopi", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:47.727650+00:00", "request_id": "20260916T132258Z_c2918219d8c3_121", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:48.572174+00:00", "request_id": "20260916T132258Z_c2918219d8c3_121", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565207, "id": "gen-1789565207-wHeAJc9ILQv8WDGUKqtM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:48.619313+00:00", "request_id": "20260916T132258Z_c2918219d8c3_121", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:49.409427+00:00", "request_id": "20260916T132258Z_c2918219d8c3_121", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565208, "id": "gen-1789565208-XUkWIp9dmbqYtCH7N85r", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.89e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.89e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.89e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.89e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:49.460952+00:00", "request_id": "20260916T132258Z_c2918219d8c3_122", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:50.433955+00:00", "request_id": "20260916T132258Z_c2918219d8c3_122", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565209, "id": "gen-1789565209-kqLyLVeFSnA4RTHXN3BR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:50.471257+00:00", "request_id": "20260916T132258Z_c2918219d8c3_123", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:54.371294+00:00", "request_id": "20260916T132258Z_c2918219d8c3_123", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565210, "id": "gen-1789565210-xqm48yXxWwyp8nvEjRYn", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:54.475682+00:00", "request_id": "20260916T132258Z_c2918219d8c3_123", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:55.512985+00:00", "request_id": "20260916T132258Z_c2918219d8c3_123", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565214, "id": "gen-1789565214-XfoDKwhWyFCl0q89m9rK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.89e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.89e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.89e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.89e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:55.594700+00:00", "request_id": "20260916T132258Z_c2918219d8c3_124", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:56.844095+00:00", "request_id": "20260916T132258Z_c2918219d8c3_124", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565215, "id": "gen-1789565215-chk8GCTFpp4QBp5U1nDI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:56.903056+00:00", "request_id": "20260916T132258Z_c2918219d8c3_124", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:57.723339+00:00", "request_id": "20260916T132258Z_c2918219d8c3_124", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565216, "id": "gen-1789565216-IUBMYFNFSNHpjgIF6QJs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.89e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.89e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.89e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.89e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 213}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:57.777941+00:00", "request_id": "20260916T132258Z_c2918219d8c3_125", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:58.591599+00:00", "request_id": "20260916T132258Z_c2918219d8c3_125", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565217, "id": "gen-1789565217-Gv5Rl5fDG4EFr02TJI0Q", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:58.644529+00:00", "request_id": "20260916T132258Z_c2918219d8c3_126", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:59.538198+00:00", "request_id": "20260916T132258Z_c2918219d8c3_126", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565218, "id": "gen-1789565218-ZuM8cMJFM1jPDofjRCoT", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"1\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:26:59.586033+00:00", "request_id": "20260916T132258Z_c2918219d8c3_126", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:05.333398+00:00", "request_id": "20260916T132258Z_c2918219d8c3_126", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565219, "id": "gen-1789565219-9ZRXOoHS0aI3eNGb7QZQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:05.367777+00:00", "request_id": "20260916T132258Z_c2918219d8c3_127", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:06.248538+00:00", "request_id": "20260916T132258Z_c2918219d8c3_127", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565225, "id": "gen-1789565225-CuPhJnYE1sYOPnut4aBG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 1}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:06.292635+00:00", "request_id": "20260916T132258Z_c2918219d8c3_127", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:07.098653+00:00", "request_id": "20260916T132258Z_c2918219d8c3_127", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565226, "id": "gen-1789565226-uFbZezkYpmAz7dexsS9L", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:07.159279+00:00", "request_id": "20260916T132258Z_c2918219d8c3_128", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:08.439494+00:00", "request_id": "20260916T132258Z_c2918219d8c3_128", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565227, "id": "gen-1789565227-rpcbl4yFwehkYPF4TRaS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:08.500730+00:00", "request_id": "20260916T132258Z_c2918219d8c3_129", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:09.572688+00:00", "request_id": "20260916T132258Z_c2918219d8c3_129", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565228, "id": "gen-1789565228-9BJ1sZtIGJdzm9V9lJQM", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 1}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:09.617530+00:00", "request_id": "20260916T132258Z_c2918219d8c3_129", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:10.370553+00:00", "request_id": "20260916T132258Z_c2918219d8c3_129", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565229, "id": "gen-1789565229-BNJVTvQClxOyH6R8KfvD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:10.417598+00:00", "request_id": "20260916T132258Z_c2918219d8c3_130", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:11.364612+00:00", "request_id": "20260916T132258Z_c2918219d8c3_130", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565230, "id": "gen-1789565230-b9f02gHoScYFhHYkcosu", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:11.417826+00:00", "request_id": "20260916T132258Z_c2918219d8c3_131", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:12.220656+00:00", "request_id": "20260916T132258Z_c2918219d8c3_131", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565231, "id": "gen-1789565231-vymNet4pfeoNutLxI40G", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.04e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 5.04e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:12.267726+00:00", "request_id": "20260916T132258Z_c2918219d8c3_131", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:13.037118+00:00", "request_id": "20260916T132258Z_c2918219d8c3_131", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565232, "id": "gen-1789565232-aHsuCKQPYHua81kiCy91", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.02e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 7.02e-06, "upstream_inference_prompt_cost": 6.24e-06}, "is_byok": false, "prompt_tokens": 208, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 214}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:13.100995+00:00", "request_id": "20260916T132258Z_c2918219d8c3_132", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:13.959983+00:00", "request_id": "20260916T132258Z_c2918219d8c3_132", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565233, "id": "gen-1789565233-9y2rizddp8EFEsjwKlwc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:14.009221+00:00", "request_id": "20260916T132258Z_c2918219d8c3_132", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:14.740942+00:00", "request_id": "20260916T132258Z_c2918219d8c3_132", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565234, "id": "gen-1789565234-XFp7YnBs3YOXcCgkN7SO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:14.792543+00:00", "request_id": "20260916T132258Z_c2918219d8c3_133", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:15.853505+00:00", "request_id": "20260916T132258Z_c2918219d8c3_133", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565234, "id": "gen-1789565234-v8dJ1zveEIfWz72dlgs5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:15.883998+00:00", "request_id": "20260916T132258Z_c2918219d8c3_133", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:16.634786+00:00", "request_id": "20260916T132258Z_c2918219d8c3_133", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565235, "id": "gen-1789565235-SRPoIcyiCRlNXtnnV4VS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:16.683958+00:00", "request_id": "20260916T132258Z_c2918219d8c3_134", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:17.622207+00:00", "request_id": "20260916T132258Z_c2918219d8c3_134", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565236, "id": "gen-1789565236-5NAs9oo9vscqvjjfIxtm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:17.667163+00:00", "request_id": "20260916T132258Z_c2918219d8c3_134", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:19.109316+00:00", "request_id": "20260916T132258Z_c2918219d8c3_134", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565237, "id": "gen-1789565237-h1QYxv8ZU4xjWV80cMRm", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:19.158665+00:00", "request_id": "20260916T132258Z_c2918219d8c3_135", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:20.177298+00:00", "request_id": "20260916T132258Z_c2918219d8c3_135", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565239, "id": "gen-1789565239-h7EUMnfhgzkhmm2QdrvC", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:20.233677+00:00", "request_id": "20260916T132258Z_c2918219d8c3_136", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:21.156949+00:00", "request_id": "20260916T132258Z_c2918219d8c3_136", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565240, "id": "gen-1789565240-SS15M4BuyAedrzAutGC2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:21.208427+00:00", "request_id": "20260916T132258Z_c2918219d8c3_137", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:21.995572+00:00", "request_id": "20260916T132258Z_c2918219d8c3_137", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565241, "id": "gen-1789565241-5OikKKPYtqPYDhtNg203", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:22.041675+00:00", "request_id": "20260916T132258Z_c2918219d8c3_137", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:22.988444+00:00", "request_id": "20260916T132258Z_c2918219d8c3_137", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\":5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565242, "id": "gen-1789565242-rLY113oznuDcGZaiYEKK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 5, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.83e-06, "cost_details": {"upstream_inference_completions_cost": 6.5e-07, "upstream_inference_cost": 6.83e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 211}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:23.049854+00:00", "request_id": "20260916T132258Z_c2918219d8c3_138", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:24.325313+00:00", "request_id": "20260916T132258Z_c2918219d8c3_138", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565243, "id": "gen-1789565243-9p1w4AHcGmkSpmrVnp79", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:24.374837+00:00", "request_id": "20260916T132258Z_c2918219d8c3_139", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:25.415113+00:00", "request_id": "20260916T132258Z_c2918219d8c3_139", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565244, "id": "gen-1789565244-mKyKBbdQ0DpaKFurcdzo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:25.458008+00:00", "request_id": "20260916T132258Z_c2918219d8c3_140", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:26.424536+00:00", "request_id": "20260916T132258Z_c2918219d8c3_140", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565245, "id": "gen-1789565245-DbvZJzrjSj2kOIxlTOqB", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.76e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.76e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 152}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:26.466259+00:00", "request_id": "20260916T132258Z_c2918219d8c3_141", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:27.476538+00:00", "request_id": "20260916T132258Z_c2918219d8c3_141", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565246, "id": "gen-1789565246-eHNRXugi0mq7TbADCvPk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:27.524412+00:00", "request_id": "20260916T132258Z_c2918219d8c3_141", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:28.409720+00:00", "request_id": "20260916T132258Z_c2918219d8c3_141", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565247, "id": "gen-1789565247-76kHyWLwd3zxUX2om6aG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:28.449270+00:00", "request_id": "20260916T132258Z_c2918219d8c3_142", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:29.368416+00:00", "request_id": "20260916T132258Z_c2918219d8c3_142", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565248, "id": "gen-1789565248-GM0ZV82f8WwEFLIQGvc1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"1\": 5}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:29.424165+00:00", "request_id": "20260916T132258Z_c2918219d8c3_142", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:30.276040+00:00", "request_id": "20260916T132258Z_c2918219d8c3_142", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565249, "id": "gen-1789565249-OrGuGgKGZ7pfYspcLjBJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 218}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.74e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 7.74e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 218}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:30.315777+00:00", "request_id": "20260916T132258Z_c2918219d8c3_143", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:31.236988+00:00", "request_id": "20260916T132258Z_c2918219d8c3_143", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565250, "id": "gen-1789565250-2KJFwj4C35qTAgU08oB3", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 4.98e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 4.98e-06, "upstream_inference_prompt_cost": 4.2e-06}, "is_byok": false, "prompt_tokens": 140, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 146}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "initial_response_message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 2048, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}, {"content": "{\"0\": 1}", "role": "assistant"}, {"content": "You are out of time. Output ONLY a single-line compact JSON object mapping each answer number to its 1-5 rating, e.g. {\"0\": 1, \"1\": 5}. No markdown, no reasoning, nothing else.", "role": "user"}], "model": "qwen/qwen3.7-flash", "reasoning": {"enabled": false}, "temperature": 1.0}, "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:31.273919+00:00", "request_id": "20260916T132258Z_c2918219d8c3_143", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "rescue", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.107566+00:00", "request_id": "20260916T132258Z_c2918219d8c3_143", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565251, "id": "gen-1789565251-LwqX85RylwSHRU5XdYk9", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}}, "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "temperature": 1.0}, "usage": {"completion_tokens": 6, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 6.96e-06, "cost_details": {"upstream_inference_completions_cost": 7.8e-07, "upstream_inference_cost": 6.96e-06, "upstream_inference_prompt_cost": 6.18e-06}, "is_byok": false, "prompt_tokens": 206, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 212}} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.158104+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.198966+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.240639+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.282422+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\n \"0\": 5,\n \"1\": 4,\n \"2\": 3,\n \"3\": 2,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.324100+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.365831+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.407531+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\n \"0\": 5,\n \"1\": 5,\n \"2\": 5,\n \"3\": 5,\n \"4\": 5,\n \"5\": 5,\n \"6\": 5,\n \"7\": 5,\n \"8\": 5,\n \"9\": 5\n}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.449255+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.490967+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.532671+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.574479+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.616134+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\n \"0\": 5,\n \"1\": 4,\n \"2\": 3,\n \"3\": 2,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.657879+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.699604+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.741780+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.783493+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.825197+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.866913+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.908629+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 3, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.950335+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:32.992197+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.033826+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.075547+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.117265+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.159001+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.200656+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.242383+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.284080+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.325844+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\":4}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.367504+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.409292+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.450964+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 5, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.492696+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 4}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.534401+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.576119+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.617809+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.659525+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.701239+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.742998+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.784700+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\":4,\"1\":5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.826378+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.868084+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.909887+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.951576+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\":4}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:33.993261+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\":3,\"1\":4}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.035001+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.076738+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.118434+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.160096+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\n \"0\": 4,\n \"1\": 3,\n \"2\": 5\n}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.201836+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 4}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.243524+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.285264+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\": 4, \"1\": 4, \"2\": 3}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.326972+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 3}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.368707+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\":4}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.410415+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 3, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.452208+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 3, \"1\": 4, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.493864+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 3, \"1\": 4, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.535585+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 3, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.577296+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 3}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.618981+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.660719+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.702420+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.744106+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 1, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.785875+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\":1,\"1\":3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.827609+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.869354+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.911061+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 2, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.952784+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:34.994488+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\":3,\"1\":3}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.027859+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\":2,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.061236+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.094612+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\":3,\"1\":2}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.128048+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 3, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.161394+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.194752+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.228132+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.261566+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.294958+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.328319+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.361715+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.395089+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\":5,\"1\":0}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.436780+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\":5,\"1\":0}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.478485+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.520245+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\":5,\"1\":0}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.561938+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.603658+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.645334+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.687116+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.745492+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.787260+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\n \"0\": 3,\n \"1\": 4,\n \"2\": 5,\n \"3\": 4,\n \"4\": 3,\n \"5\": 2,\n \"6\": 2,\n \"7\": 2,\n \"8\": 2,\n \"9\": 1\n}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.828979+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.870630+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\n \"0\": 1,\n \"1\": 2,\n \"2\": 3,\n \"3\": 4,\n \"4\": 5,\n \"5\": 5,\n \"6\": 5,\n \"7\": 5,\n \"8\": 5,\n \"9\": 5\n}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.912380+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.954102+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 4, \"4\": 3, \"5\": 2, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:35.995757+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.037456+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.079225+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.120907+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.278164+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.404267+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.446007+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.487684+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\":3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.529393+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.571111+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.612836+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.654519+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.696229+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 2, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.737971+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.779758+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.821445+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.863148+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.904885+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.946558+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:36.988312+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.029984+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.071709+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.113412+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.155144+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.196820+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.238528+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.280307+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.322097+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.363736+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.405476+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.447157+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.488856+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.530604+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.572290+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.613971+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.655719+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.697416+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 3, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.739189+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.780866+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 0, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.822585+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 1, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.864303+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 2, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.906060+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 3, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.947776+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 4, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [0, 1], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:37.989434+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 5, "text": "{\"0\":5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.031143+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 6, "text": "{\"0\": 5, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.072873+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 7, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.114568+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 8, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.156275+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 9, "text": "{\"0\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.197977+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 10, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": false, "presented_order": [1, 0], "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.239790+00:00", "run_id": "20260916T132258Z_c2918219d8c3", "sample": 11, "text": "{\"0\": 1}"} +{"event": "item_result", "failed_samples": 0, "id": "Homosexuality", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.1084733893557423, 0.10014005602240895, 0.09180672268907562, 0.08347338935574229, 0.09474789915966386, 0.09474789915966386, 0.09474789915966386, 0.09474789915966386, 0.09474789915966386, 0.1423669467787115], "p_samples": [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.35714285714285715], [0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.35714285714285715], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.029411764705882353, 0.029411764705882353, 0.029411764705882353, 0.029411764705882353, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.029411764705882353, 0.029411764705882353, 0.029411764705882353, 0.029411764705882353, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05]], "pmass_allowed": 1.0, "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.281544+00:00", "rescued_samples": 0, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "{\n \"0\": 5,\n \"1\": 4,\n \"2\": 3,\n \"3\": 2,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\n \"0\": 5,\n \"1\": 5,\n \"2\": 5,\n \"3\": 5,\n \"4\": 5,\n \"5\": 5,\n \"6\": 5,\n \"7\": 5,\n \"8\": 5,\n \"9\": 5\n}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\n \"0\": 5,\n \"1\": 4,\n \"2\": 3,\n \"3\": 2,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "dealing with people?", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7083333333333334, 0.29166666666666663], "p_samples": [[0.6666666666666666, 0.3333333333333333], [0.6666666666666666, 0.3333333333333333], [0.6666666666666666, 0.3333333333333333], [0.8333333333333334, 0.16666666666666666], [0.6666666666666666, 0.3333333333333333], [0.6666666666666666, 0.3333333333333333], [0.5714285714285714, 0.42857142857142855], [0.7142857142857143, 0.2857142857142857], [0.6666666666666666, 0.3333333333333333], [0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666]], "pmass_allowed": 1.0, "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.323428+00:00", "rescued_samples": 0, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 4, \"1\": 2}", "{\"0\": 4, \"1\": 2}", "{\"0\": 4, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 4, \"1\": 2}", "{\"0\": 4, \"1\": 2}", "{\"0\": 3, \"1\": 4}", "{\"0\": 2, \"1\": 5}", "{\"0\": 2, \"1\": 4}", "{\"0\": 1, \"1\": 5}", "{\"0\": 2, \"1\": 5}", "{\"0\": 1, \"1\": 5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Signing a petition", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.43863636363636366, 0.4636363636363636, 0.09772727272727275], "p_samples": [[0.5, 0.4, 0.1], [0.4, 0.5, 0.1], [0.4, 0.5, 0.1], [0.45454545454545453, 0.45454545454545453, 0.09090909090909091]], "pmass_allowed": 0.3333333333333333, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.364960+00:00", "rescued_samples": 8, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\":5}", "{\"0\": 5}", "{\"0\": 4, \"1\": 5, \"2\": 1}", "{\"0\":4}", "{\"0\": 4, \"1\": 5, \"2\": 1}", "{\"0\":5}", "{\"0\": 5, \"1\": 5, \"2\": 1}", "{\"0\": 4}", "{\"0\":5}", "{\"0\":5}", "{\"0\": 5}"], "valid_samples": 4} +{"event": "item_result", "failed_samples": 0, "id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.5416666666666667, 0.35, 0.10833333333333332], "p_samples": [[0.5, 0.4, 0.1], [0.5555555555555556, 0.3333333333333333, 0.1111111111111111], [0.5555555555555556, 0.3333333333333333, 0.1111111111111111], [0.5555555555555556, 0.3333333333333333, 0.1111111111111111]], "pmass_allowed": 0.3333333333333333, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.407245+00:00", "rescued_samples": 8, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 5}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\":4,\"1\":5}", "{\"0\": 5}", "{\"0\": 5}", "{\"0\":5}", "{\"0\":4}", "{\"0\":3,\"1\":4}", "{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\": 5}"], "valid_samples": 4} +{"event": "item_result", "failed_samples": 0, "id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.3892045454545454, 0.41281565656565655, 0.19797979797979798], "p_samples": [[0.3333333333333333, 0.25, 0.4166666666666667], [0.5, 0.4, 0.1], [0.36363636363636365, 0.36363636363636365, 0.2727272727272727], [0.375, 0.5, 0.125], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.375, 0.5, 0.125], [0.5, 0.4, 0.1]], "pmass_allowed": 0.6666666666666666, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.449023+00:00", "rescued_samples": 4, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\n \"0\": 4,\n \"1\": 3,\n \"2\": 5\n}", "{\"0\": 4}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 4, \"1\": 4, \"2\": 3}", "{\"0\": 3}", "{\"0\":4}", "{\"0\": 3, \"1\": 4, \"2\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 2}", "{\"0\": 3, \"1\": 4, \"2\": 2}", "{\"0\": 3, \"1\": 4, \"2\": 1}", "{\"0\": 3}", "{\"0\": 5, \"1\": 4, \"2\": 1}"], "valid_samples": 8} +{"event": "item_result", "failed_samples": 0, "id": "Religion", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [NaN, NaN, NaN, NaN], "p_samples": [], "pmass_allowed": 0.0, "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.490747+00:00", "rescued_samples": 12, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\":3,\"1\":2}", "{\"0\":3,\"1\":2}", "{\"0\": 1, \"1\": 2}", "{\"0\":1,\"1\":3}", "{\"0\":3,\"1\":2}", "{\"0\":3,\"1\":2}", "{\"0\": 2, \"1\": 3}", "{\"0\":3,\"1\":3}", "{\"0\":3,\"1\":3}", "{\"0\":2,\"1\":2}", "{\"0\":3,\"1\":2}", "{\"0\":3,\"1\":2}"], "valid_samples": 0} +{"event": "item_result", "failed_samples": 0, "id": "God", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.5587301587301589, 0.44126984126984126], "p_samples": [[0.6, 0.4], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666], [0.16666666666666666, 0.8333333333333334], [0.16666666666666666, 0.8333333333333334], [0.16666666666666666, 0.8333333333333334]], "pmass_allowed": 0.75, "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.532547+00:00", "rescued_samples": 3, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 3, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\":5,\"1\":0}", "{\"0\":5,\"1\":0}", "{\"0\": 5, \"1\": 1}", "{\"0\":5,\"1\":0}"], "valid_samples": 9} +{"event": "item_result", "failed_samples": 0, "id": "Abortion", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.11126706626706627, 0.11785880785880785, 0.12445054945054945, 0.11727605727605726, 0.1101015651015651, 0.09972194472194472, 0.08955877455877456, 0.08303196803196804, 0.0765051615051615, 0.07022810522810523], "p_samples": [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.038461538461538464, 0.07692307692307693, 0.11538461538461539, 0.15384615384615385, 0.19230769230769232, 0.15384615384615385, 0.11538461538461539, 0.07692307692307693, 0.038461538461538464, 0.038461538461538464], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.10714285714285714, 0.14285714285714285, 0.17857142857142858, 0.14285714285714285, 0.10714285714285714, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.03571428571428571], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.025, 0.05, 0.075, 0.1, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.12, 0.16, 0.2, 0.16, 0.12, 0.08, 0.04, 0.04, 0.04, 0.04], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.03333333333333333, 0.06666666666666667, 0.1, 0.13333333333333333, 0.16666666666666666, 0.16666666666666666, 0.13333333333333333, 0.1, 0.06666666666666667, 0.03333333333333333]], "pmass_allowed": 0.9166666666666666, "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.574209+00:00", "rescued_samples": 1, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\":1,\"1\":2,\"2\":3,\"3\":4,\"4\":5}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\n \"0\": 3,\n \"1\": 4,\n \"2\": 5,\n \"3\": 4,\n \"4\": 3,\n \"5\": 2,\n \"6\": 2,\n \"7\": 2,\n \"8\": 2,\n \"9\": 1\n}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\n \"0\": 1,\n \"1\": 2,\n \"2\": 3,\n \"3\": 4,\n \"4\": 5,\n \"5\": 5,\n \"6\": 5,\n \"7\": 5,\n \"8\": 5,\n \"9\": 5\n}", "{\n \"0\": 1,\n \"1\": 1,\n \"2\": 1,\n \"3\": 1,\n \"4\": 1,\n \"5\": 1,\n \"6\": 1,\n \"7\": 1,\n \"8\": 1,\n \"9\": 1\n}", "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 4, \"4\": 3, \"5\": 2, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 4, \"7\": 3, \"8\": 2, \"9\": 1}"], "valid_samples": 11} +{"event": "item_result", "failed_samples": 0, "id": "Obedience", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.6634920634920635, 0.3365079365079365], "p_samples": [[0.6666666666666666, 0.3333333333333333], [0.7142857142857143, 0.2857142857142857], [0.6666666666666666, 0.3333333333333333], [0.6666666666666666, 0.3333333333333333], [0.6, 0.4], [0.6666666666666666, 0.3333333333333333]], "pmass_allowed": 0.5, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.615972+00:00", "rescued_samples": 6, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 3}", "{\"0\":5}", "{\"0\": 5}", "{\"0\": 4, \"1\": 2}", "{\"0\": 5, \"1\": 2}", "{\"0\":3}", "{\"0\": 2, \"1\": 4}", "{\"0\": 2, \"1\": 4}", "{\"0\": 2}", "{\"0\": 2}", "{\"0\": 2, \"1\": 3}", "{\"0\": 2, \"1\": 4}"], "valid_samples": 6} +{"event": "item_result", "failed_samples": 0, "id": "Independence", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7242063492063492, 0.27579365079365076], "p_samples": [[0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.625, 0.375]], "pmass_allowed": 0.25, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.657686+00:00", "rescued_samples": 9, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 5, \"1\": 1}", "{\"0\": 5}", "{\"0\": 5}", "{\"0\":5}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 3}", "{\"0\":5}", "{\"0\": 5}", "{\"0\":5}", "{\"0\": 5}", "{\"0\":5}", "{\"0\": 5}"], "valid_samples": 3} +{"event": "item_result", "failed_samples": 0, "id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.6160714285714286, 0.3839285714285714], "p_samples": [[0.625, 0.375], [0.7142857142857143, 0.2857142857142857], [0.5, 0.5], [0.625, 0.375]], "pmass_allowed": 0.3333333333333333, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.699416+00:00", "rescued_samples": 8, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\": 5}", "{\"0\":5}", "{\"0\": 5, \"1\": 3}", "{\"0\":5}", "{\"0\":5}", "{\"0\": 5, \"1\": 2}", "{\"1\": 5}", "{\"0\": 1}", "{\"0\": 5, \"1\": 5}", "{\"0\": 1}", "{\"0\": 3, \"1\": 5}", "{\"0\": 5}"], "valid_samples": 4} +{"event": "item_result", "failed_samples": 0, "id": "Imagination", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.6236772486772487, 0.37632275132275134], "p_samples": [[0.625, 0.375], [0.625, 0.375], [0.4444444444444444, 0.5555555555555556], [0.5, 0.5], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666]], "pmass_allowed": 0.5, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.741154+00:00", "rescued_samples": 7, "run_id": "20260916T132258Z_c2918219d8c3", "texts": ["{\"0\":5}", "{\"0\": 5}", "{\"0\":5}", "{\"0\": 5, \"1\": 3}", "{\"0\": 5, \"1\": 3}", "{\"0\":5}", "{\"0\": 5, \"1\": 4}", "{\"0\": 5, \"1\": 5}", "{\"0\": 2, \"1\": 5}", "{\"0\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 1}"], "valid_samples": 6} +{"event": "run_finished", "failed_samples": 0, "model": "qwen/qwen3.7-flash", "planned_requests": 144, "protocol_id": "c2918219d8c3474cb3574ab64cd9a6e737d60a3d185ac2ce1c8d96a33d020ce1", "recorded_at_utc": "2026-09-16T13:27:38.782882+00:00", "rescued_samples": 66, "run_id": "20260916T132258Z_c2918219d8c3", "valid_samples": 79} +{"event": "run_started", "items": [{"id": "Homosexuality", "n": 10, "options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "question": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality"}, {"id": "dealing with people?", "n": 2, "options": ["Most people can be trusted", "Need to be very careful"], "question": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?"}, {"id": "Signing a petition", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition"}, {"id": "Attending peaceful demonstrations", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations"}, {"id": "Joining in boycotts", "n": 3, "options": ["Have done", "Might do", "Would never do"], "question": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts"}, {"id": "Religion", "n": 4, "options": ["Very important", "Rather important", "Not very important", "Not at all important"], "question": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion"}, {"id": "God", "n": 2, "options": ["Yes", "No"], "question": "In which of the following things do you believe, if you believe in any?\n\nGod"}, {"id": "Abortion", "n": 10, "options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "question": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion"}, {"id": "Obedience", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience"}, {"id": "Independence", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence"}, {"id": "Determination, perseverance", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance"}, {"id": "Imagination", "n": 2, "options": ["Important", "Not mentioned"], "question": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination"}], "planned_requests": 144, "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:01.532347+00:00", "run_id": "20260916T133001Z_82875b6ee164", "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:01.581165+00:00", "request_id": "20260916T133001Z_82875b6ee164_000", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:03.326977+00:00", "request_id": "20260916T133001Z_82875b6ee164_000", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565401, "id": "gen-1789565401-ePJlW2zJ71smju2BHp28", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:03.372099+00:00", "request_id": "20260916T133001Z_82875b6ee164_001", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:04.641951+00:00", "request_id": "20260916T133001Z_82875b6ee164_001", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565403, "id": "gen-1789565403-Kznq1lJjefDJwaWzAVBZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:04.705106+00:00", "request_id": "20260916T133001Z_82875b6ee164_002", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:06.027218+00:00", "request_id": "20260916T133001Z_82875b6ee164_002", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565404, "id": "gen-1789565404-49Q1VB0HlpSkIqMOoWpQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:06.088167+00:00", "request_id": "20260916T133001Z_82875b6ee164_003", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:07.628735+00:00", "request_id": "20260916T133001Z_82875b6ee164_003", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565406, "id": "gen-1789565406-swBsT1kM0jqTZiMYEstD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:07.688027+00:00", "request_id": "20260916T133001Z_82875b6ee164_004", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:09.178704+00:00", "request_id": "20260916T133001Z_82875b6ee164_004", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565407, "id": "gen-1789565407-yGxAHWcR2ow4M5PCvtw8", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:09.237822+00:00", "request_id": "20260916T133001Z_82875b6ee164_005", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:10.472100+00:00", "request_id": "20260916T133001Z_82875b6ee164_005", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565409, "id": "gen-1789565409-k1fTrVLUWb3KoFyfDZ8g", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:10.521254+00:00", "request_id": "20260916T133001Z_82875b6ee164_006", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:11.930065+00:00", "request_id": "20260916T133001Z_82875b6ee164_006", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565410, "id": "gen-1789565410-EvMeWXk3V7vpmlnMLBRZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:11.988065+00:00", "request_id": "20260916T133001Z_82875b6ee164_007", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:13.150433+00:00", "request_id": "20260916T133001Z_82875b6ee164_007", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565412, "id": "gen-1789565412-fBMTedX8QB5Gt5wsD3M0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:13.196082+00:00", "request_id": "20260916T133001Z_82875b6ee164_008", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:14.939503+00:00", "request_id": "20260916T133001Z_82875b6ee164_008", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565413, "id": "gen-1789565413-Ff9XiULTr5OG2iYxFX6T", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:14.979137+00:00", "request_id": "20260916T133001Z_82875b6ee164_009", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:16.232465+00:00", "request_id": "20260916T133001Z_82875b6ee164_009", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565415, "id": "gen-1789565415-mwHLaDKKRIieNDwl6RVV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:16.279039+00:00", "request_id": "20260916T133001Z_82875b6ee164_010", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:17.438522+00:00", "request_id": "20260916T133001Z_82875b6ee164_010", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565416, "id": "gen-1789565416-ggUhBJWSs6gaGkcg90qj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:17.487327+00:00", "request_id": "20260916T133001Z_82875b6ee164_011", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:18.704346+00:00", "request_id": "20260916T133001Z_82875b6ee164_011", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565417, "id": "gen-1789565417-7g8bBsxirPqwz4b3bDDX", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.326e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.326e-05, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 242}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:18.753865+00:00", "request_id": "20260916T133001Z_82875b6ee164_012", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:19.954399+00:00", "request_id": "20260916T133001Z_82875b6ee164_012", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565418, "id": "gen-1789565418-j8J8bUuOmFhFB1Z8a3YL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:19.995259+00:00", "request_id": "20260916T133001Z_82875b6ee164_013", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:20.906136+00:00", "request_id": "20260916T133001Z_82875b6ee164_013", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565420, "id": "gen-1789565420-UMP2qdJ5HDn0xYjnLPVA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:20.953436+00:00", "request_id": "20260916T133001Z_82875b6ee164_014", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:21.961941+00:00", "request_id": "20260916T133001Z_82875b6ee164_014", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565421, "id": "gen-1789565421-LOHhAYQRTlNPt3CPaw47", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:22.011897+00:00", "request_id": "20260916T133001Z_82875b6ee164_015", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:23.021554+00:00", "request_id": "20260916T133001Z_82875b6ee164_015", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565422, "id": "gen-1789565422-Fn1jsqDUvehJ3l7E9S5W", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:23.069987+00:00", "request_id": "20260916T133001Z_82875b6ee164_016", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:24.001263+00:00", "request_id": "20260916T133001Z_82875b6ee164_016", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565423, "id": "gen-1789565423-lHeHUjVNZT0UTf8xhL3p", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:24.044896+00:00", "request_id": "20260916T133001Z_82875b6ee164_017", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Most people can be trusted", "Need to be very careful"], "presented_order": [0, 1], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Most people can be trusted\n1) Need to be very careful\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:25.004750+00:00", "request_id": "20260916T133001Z_82875b6ee164_017", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565424, "id": "gen-1789565424-bgXQ9E7FH9SVPzM0nN4p", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:25.044931+00:00", "request_id": "20260916T133001Z_82875b6ee164_018", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:26.316947+00:00", "request_id": "20260916T133001Z_82875b6ee164_018", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565425, "id": "gen-1789565425-8w367UiVIlLxKqnICRO3", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:26.361444+00:00", "request_id": "20260916T133001Z_82875b6ee164_019", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:27.300471+00:00", "request_id": "20260916T133001Z_82875b6ee164_019", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565426, "id": "gen-1789565426-7i8peQgGgCnkDyISA2iL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:27.353471+00:00", "request_id": "20260916T133001Z_82875b6ee164_020", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:28.195317+00:00", "request_id": "20260916T133001Z_82875b6ee164_020", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565427, "id": "gen-1789565427-WbXNHXiOYiwgEaXeun4j", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:28.236456+00:00", "request_id": "20260916T133001Z_82875b6ee164_021", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:29.089929+00:00", "request_id": "20260916T133001Z_82875b6ee164_021", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565428, "id": "gen-1789565428-1IP3wFnn0r7VM2rF9f0p", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:29.136265+00:00", "request_id": "20260916T133001Z_82875b6ee164_022", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:30.515103+00:00", "request_id": "20260916T133001Z_82875b6ee164_022", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565429, "id": "gen-1789565429-GmaBMpTuYuv91w1UUgm5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_started", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:30.561018+00:00", "request_id": "20260916T133001Z_82875b6ee164_023", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Most people can be trusted", "Need to be very careful"], "event": "request_completed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Need to be very careful", "Most people can be trusted"], "presented_order": [1, 0], "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:31.545400+00:00", "request_id": "20260916T133001Z_82875b6ee164_023", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565430, "id": "gen-1789565430-gn3Np6Qxoxhtjf6ANAxL", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.64e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.64e-06, "upstream_inference_prompt_cost": 4.08e-06}, "is_byok": false, "prompt_tokens": 136, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 148}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:31.602541+00:00", "request_id": "20260916T133001Z_82875b6ee164_024", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:32.539109+00:00", "request_id": "20260916T133001Z_82875b6ee164_024", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565431, "id": "gen-1789565431-qcTPkvB7xKotlqt3qb1f", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:32.585953+00:00", "request_id": "20260916T133001Z_82875b6ee164_025", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:33.572271+00:00", "request_id": "20260916T133001Z_82875b6ee164_025", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565432, "id": "gen-1789565432-bAQF3RIDmXfvAbwUXjCd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:33.627452+00:00", "request_id": "20260916T133001Z_82875b6ee164_026", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:34.658772+00:00", "request_id": "20260916T133001Z_82875b6ee164_026", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565433, "id": "gen-1789565433-bJcT71LGhXl04Qwaxa5s", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:34.710604+00:00", "request_id": "20260916T133001Z_82875b6ee164_027", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:35.719256+00:00", "request_id": "20260916T133001Z_82875b6ee164_027", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565434, "id": "gen-1789565434-zLKYrrtfsyHDxftSGtZV", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:35.760882+00:00", "request_id": "20260916T133001Z_82875b6ee164_028", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:36.723117+00:00", "request_id": "20260916T133001Z_82875b6ee164_028", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565435, "id": "gen-1789565435-oLGGrmpbrAdLx0BGGoXI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:36.769221+00:00", "request_id": "20260916T133001Z_82875b6ee164_029", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:37.633361+00:00", "request_id": "20260916T133001Z_82875b6ee164_029", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565436, "id": "gen-1789565436-djXCzQQDtDVXWbD0rv0p", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:37.685212+00:00", "request_id": "20260916T133001Z_82875b6ee164_030", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:38.722130+00:00", "request_id": "20260916T133001Z_82875b6ee164_030", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565437, "id": "gen-1789565437-Moxr7NGwXHAmDZH71N9m", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:38.776746+00:00", "request_id": "20260916T133001Z_82875b6ee164_031", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:39.804918+00:00", "request_id": "20260916T133001Z_82875b6ee164_031", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565438, "id": "gen-1789565438-TFZ1uTXWqykgPQebw9Us", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:39.860886+00:00", "request_id": "20260916T133001Z_82875b6ee164_032", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:40.877843+00:00", "request_id": "20260916T133001Z_82875b6ee164_032", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565439, "id": "gen-1789565439-VAJlKtLQn4slzfGPR7PU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:40.919129+00:00", "request_id": "20260916T133001Z_82875b6ee164_033", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:42.067307+00:00", "request_id": "20260916T133001Z_82875b6ee164_033", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565440, "id": "gen-1789565440-BULVW3mioAh6MUD7daKa", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:42.127651+00:00", "request_id": "20260916T133001Z_82875b6ee164_034", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:43.128945+00:00", "request_id": "20260916T133001Z_82875b6ee164_034", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565442, "id": "gen-1789565442-GpRwB9EQXuQ1T6MVnSQG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:43.185918+00:00", "request_id": "20260916T133001Z_82875b6ee164_035", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:44.140772+00:00", "request_id": "20260916T133001Z_82875b6ee164_035", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565443, "id": "gen-1789565443-c8xW27WqPxmlG0nuflCl", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 5.46e-06}, "is_byok": false, "prompt_tokens": 182, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 200}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:44.194094+00:00", "request_id": "20260916T133001Z_82875b6ee164_036", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:45.122786+00:00", "request_id": "20260916T133001Z_82875b6ee164_036", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565444, "id": "gen-1789565444-unpMgIyVs5j7wIuo9wd5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:45.168884+00:00", "request_id": "20260916T133001Z_82875b6ee164_037", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:46.357344+00:00", "request_id": "20260916T133001Z_82875b6ee164_037", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565445, "id": "gen-1789565445-jjgt88YXuSy4u9l0FJOP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:46.410537+00:00", "request_id": "20260916T133001Z_82875b6ee164_038", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:47.334654+00:00", "request_id": "20260916T133001Z_82875b6ee164_038", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565446, "id": "gen-1789565446-6GUxBryKv4RxqwiD6mRu", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:47.393731+00:00", "request_id": "20260916T133001Z_82875b6ee164_039", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:48.283822+00:00", "request_id": "20260916T133001Z_82875b6ee164_039", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565447, "id": "gen-1789565447-hP86NPJjI01YREftOB2g", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:48.335405+00:00", "request_id": "20260916T133001Z_82875b6ee164_040", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:49.260100+00:00", "request_id": "20260916T133001Z_82875b6ee164_040", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565448, "id": "gen-1789565448-EbYTf2JawcIampprRt1C", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:49.318656+00:00", "request_id": "20260916T133001Z_82875b6ee164_041", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:50.228894+00:00", "request_id": "20260916T133001Z_82875b6ee164_041", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565449, "id": "gen-1789565449-7Sp92trd4wchNCqKiaaS", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:50.260184+00:00", "request_id": "20260916T133001Z_82875b6ee164_042", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:51.174246+00:00", "request_id": "20260916T133001Z_82875b6ee164_042", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565450, "id": "gen-1789565450-JeG14LzAQ223NqVtbMeN", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:51.235133+00:00", "request_id": "20260916T133001Z_82875b6ee164_043", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:52.327946+00:00", "request_id": "20260916T133001Z_82875b6ee164_043", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565451, "id": "gen-1789565451-3ou0K7ADrhih0ILZeiJ2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:52.376715+00:00", "request_id": "20260916T133001Z_82875b6ee164_044", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:53.503449+00:00", "request_id": "20260916T133001Z_82875b6ee164_044", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565452, "id": "gen-1789565452-V7IWkmunvqsDF6plqyzi", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:53.552012+00:00", "request_id": "20260916T133001Z_82875b6ee164_045", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:54.394685+00:00", "request_id": "20260916T133001Z_82875b6ee164_045", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565453, "id": "gen-1789565453-AvPogqv5qgEt5ndVAoGo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:54.443163+00:00", "request_id": "20260916T133001Z_82875b6ee164_046", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:55.400466+00:00", "request_id": "20260916T133001Z_82875b6ee164_046", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565454, "id": "gen-1789565454-5uU3k6ygAHYIdB0Sikrk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:55.434843+00:00", "request_id": "20260916T133001Z_82875b6ee164_047", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:56.469349+00:00", "request_id": "20260916T133001Z_82875b6ee164_047", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565455, "id": "gen-1789565455-bVPARrJFJ83qukMHJD1Z", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.83e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.83e-06, "upstream_inference_prompt_cost": 5.49e-06}, "is_byok": false, "prompt_tokens": 183, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 201}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:56.517985+00:00", "request_id": "20260916T133001Z_82875b6ee164_048", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:57.845182+00:00", "request_id": "20260916T133001Z_82875b6ee164_048", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565456, "id": "gen-1789565456-YbAcdLSd5b7RVvzQBFKh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:57.892690+00:00", "request_id": "20260916T133001Z_82875b6ee164_049", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:58.790103+00:00", "request_id": "20260916T133001Z_82875b6ee164_049", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565457, "id": "gen-1789565457-U3Reldp64g6PYQJbOsOp", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:58.842696+00:00", "request_id": "20260916T133001Z_82875b6ee164_050", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:59.752832+00:00", "request_id": "20260916T133001Z_82875b6ee164_050", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565458, "id": "gen-1789565458-UzUUjwMpUsooT5HGUoR0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:30:59.800867+00:00", "request_id": "20260916T133001Z_82875b6ee164_051", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:00.716624+00:00", "request_id": "20260916T133001Z_82875b6ee164_051", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565459, "id": "gen-1789565459-EyavQONmiwV7sSnLVn4H", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:00.759117+00:00", "request_id": "20260916T133001Z_82875b6ee164_052", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:01.717611+00:00", "request_id": "20260916T133001Z_82875b6ee164_052", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565460, "id": "gen-1789565460-zlRITFZ57xftpYtW9pKZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:01.758975+00:00", "request_id": "20260916T133001Z_82875b6ee164_053", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:02.743054+00:00", "request_id": "20260916T133001Z_82875b6ee164_053", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565461, "id": "gen-1789565461-OxY4LBvEBRlDzkZB5uHr", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:02.792155+00:00", "request_id": "20260916T133001Z_82875b6ee164_054", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:03.946267+00:00", "request_id": "20260916T133001Z_82875b6ee164_054", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565462, "id": "gen-1789565462-B9jRI6U1Eb1uv3huaM5o", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:03.991939+00:00", "request_id": "20260916T133001Z_82875b6ee164_055", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:04.936023+00:00", "request_id": "20260916T133001Z_82875b6ee164_055", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565464, "id": "gen-1789565464-kXiM6aIGzqm3yGIUKy8i", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:04.983452+00:00", "request_id": "20260916T133001Z_82875b6ee164_056", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:06.193817+00:00", "request_id": "20260916T133001Z_82875b6ee164_056", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565465, "id": "gen-1789565465-Yy8msitwjqQHpHKnfMC2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:06.241653+00:00", "request_id": "20260916T133001Z_82875b6ee164_057", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:07.119026+00:00", "request_id": "20260916T133001Z_82875b6ee164_057", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565466, "id": "gen-1789565466-zZ3Dv8FGVKlw0SU25URO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:07.166580+00:00", "request_id": "20260916T133001Z_82875b6ee164_058", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:08.605050+00:00", "request_id": "20260916T133001Z_82875b6ee164_058", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565467, "id": "gen-1789565467-cy7HEqRSfuhLf4OPJnMg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_started", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:08.674608+00:00", "request_id": "20260916T133001Z_82875b6ee164_059", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Have done", "Might do", "Would never do"], "event": "request_completed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Have done", "Might do", "Would never do"], "presented_order": [0, 1, 2], "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:09.726698+00:00", "request_id": "20260916T133001Z_82875b6ee164_059", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 5, \"2\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565468, "id": "gen-1789565468-uEo4qcDui1WL6zToVyue", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 18, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.86e-06, "cost_details": {"upstream_inference_completions_cost": 2.34e-06, "upstream_inference_cost": 7.86e-06, "upstream_inference_prompt_cost": 5.52e-06}, "is_byok": false, "prompt_tokens": 184, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 202}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:09.783139+00:00", "request_id": "20260916T133001Z_82875b6ee164_060", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:10.837957+00:00", "request_id": "20260916T133001Z_82875b6ee164_060", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3, \"2\": 2, \"3\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565469, "id": "gen-1789565469-PtrBBKtNLTwmTEN60eUZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:10.891362+00:00", "request_id": "20260916T133001Z_82875b6ee164_061", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:12.098098+00:00", "request_id": "20260916T133001Z_82875b6ee164_061", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 3, \"2\": 4, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565470, "id": "gen-1789565470-QcoTeCCGI8nUVyrMkmdQ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:12.149904+00:00", "request_id": "20260916T133001Z_82875b6ee164_062", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:13.258362+00:00", "request_id": "20260916T133001Z_82875b6ee164_062", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565472, "id": "gen-1789565472-Esa0jycjrZXwNediR3CU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:13.308047+00:00", "request_id": "20260916T133001Z_82875b6ee164_063", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:14.317807+00:00", "request_id": "20260916T133001Z_82875b6ee164_063", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565473, "id": "gen-1789565473-b1I16cteNo8vNcn2dTs0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:14.399637+00:00", "request_id": "20260916T133001Z_82875b6ee164_064", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:16.047724+00:00", "request_id": "20260916T133001Z_82875b6ee164_064", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565474, "id": "gen-1789565474-M26ghOZEcP94JwKUo5q7", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:16.091483+00:00", "request_id": "20260916T133001Z_82875b6ee164_065", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:17.065761+00:00", "request_id": "20260916T133001Z_82875b6ee164_065", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2, \"2\": 2, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565476, "id": "gen-1789565476-XKaUZkmIIQseilwMZlcd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:17.116421+00:00", "request_id": "20260916T133001Z_82875b6ee164_066", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:18.079439+00:00", "request_id": "20260916T133001Z_82875b6ee164_066", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565477, "id": "gen-1789565477-4cj0ZnNPeYNpl8dkucc5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:18.133069+00:00", "request_id": "20260916T133001Z_82875b6ee164_067", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:19.090929+00:00", "request_id": "20260916T133001Z_82875b6ee164_067", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565478, "id": "gen-1789565478-tQrcl8SAnWVie2tjN0xF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:19.149631+00:00", "request_id": "20260916T133001Z_82875b6ee164_068", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:20.144085+00:00", "request_id": "20260916T133001Z_82875b6ee164_068", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565479, "id": "gen-1789565479-MEC6fOL3p58K4hiDIwII", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:20.199483+00:00", "request_id": "20260916T133001Z_82875b6ee164_069", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:21.267057+00:00", "request_id": "20260916T133001Z_82875b6ee164_069", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 3, \"2\": 4, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565480, "id": "gen-1789565480-xWpD7fAQq4SUN5NdTpn3", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:21.307654+00:00", "request_id": "20260916T133001Z_82875b6ee164_070", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:22.509858+00:00", "request_id": "20260916T133001Z_82875b6ee164_070", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3, \"2\": 2, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565481, "id": "gen-1789565481-enTCP5dNARB7oA5Jb5ad", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_started", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:22.565779+00:00", "request_id": "20260916T133001Z_82875b6ee164_071", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "event": "request_completed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Very important", "Rather important", "Not very important", "Not at all important"], "presented_order": [0, 1, 2, 3], "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:23.729007+00:00", "request_id": "20260916T133001Z_82875b6ee164_071", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565482, "id": "gen-1789565482-n9H2A8M3ayHSIKbK75OZ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 24, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 7.8e-06, "cost_details": {"upstream_inference_completions_cost": 3.12e-06, "upstream_inference_cost": 7.8e-06, "upstream_inference_prompt_cost": 4.68e-06}, "is_byok": false, "prompt_tokens": 156, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 180}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:23.790595+00:00", "request_id": "20260916T133001Z_82875b6ee164_072", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:24.630554+00:00", "request_id": "20260916T133001Z_82875b6ee164_072", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565483, "id": "gen-1789565483-9y7uCknstYBY0BifFwII", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:24.682247+00:00", "request_id": "20260916T133001Z_82875b6ee164_073", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:25.555862+00:00", "request_id": "20260916T133001Z_82875b6ee164_073", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565484, "id": "gen-1789565484-OkUgAOTqrthnZ88WywaI", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:25.607144+00:00", "request_id": "20260916T133001Z_82875b6ee164_074", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:26.461722+00:00", "request_id": "20260916T133001Z_82875b6ee164_074", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565485, "id": "gen-1789565485-Nd9SXYi7iKiUWLa6PtCU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:26.507070+00:00", "request_id": "20260916T133001Z_82875b6ee164_075", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:27.361118+00:00", "request_id": "20260916T133001Z_82875b6ee164_075", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565486, "id": "gen-1789565486-Gawj3Z9wL9aTe2d7dAc5", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:27.423707+00:00", "request_id": "20260916T133001Z_82875b6ee164_076", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:28.314461+00:00", "request_id": "20260916T133001Z_82875b6ee164_076", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565487, "id": "gen-1789565487-IY9JZdJYbH3eMGUeMNfB", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:28.373571+00:00", "request_id": "20260916T133001Z_82875b6ee164_077", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Yes", "No"], "presented_order": [0, 1], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Yes\n1) No\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:29.214504+00:00", "request_id": "20260916T133001Z_82875b6ee164_077", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565488, "id": "gen-1789565488-EPtlQetofabNcNJPfJfX", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:29.281846+00:00", "request_id": "20260916T133001Z_82875b6ee164_078", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:30.034308+00:00", "request_id": "20260916T133001Z_82875b6ee164_078", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565489, "id": "gen-1789565489-iHGyzSsvY4Ue8ffaH9Iw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:30.090112+00:00", "request_id": "20260916T133001Z_82875b6ee164_079", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:31.235601+00:00", "request_id": "20260916T133001Z_82875b6ee164_079", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565490, "id": "gen-1789565490-rriz6sCiniNytbe2Dnxy", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:31.289891+00:00", "request_id": "20260916T133001Z_82875b6ee164_080", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:32.089193+00:00", "request_id": "20260916T133001Z_82875b6ee164_080", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565491, "id": "gen-1789565491-ZryMa8A0SuS31oohDYBG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:32.131567+00:00", "request_id": "20260916T133001Z_82875b6ee164_081", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:33.003084+00:00", "request_id": "20260916T133001Z_82875b6ee164_081", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565492, "id": "gen-1789565492-HukXTf5UXLtHkCPIV3mH", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:33.039782+00:00", "request_id": "20260916T133001Z_82875b6ee164_082", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:33.818771+00:00", "request_id": "20260916T133001Z_82875b6ee164_082", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565493, "id": "gen-1789565493-G7JwtWWV4GmDUoQ8h0Z4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Yes", "No"], "event": "request_started", "item_id": "God", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:33.864628+00:00", "request_id": "20260916T133001Z_82875b6ee164_083", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Yes", "No"], "event": "request_completed", "item_id": "God", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["No", "Yes"], "presented_order": [1, 0], "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:34.870501+00:00", "request_id": "20260916T133001Z_82875b6ee164_083", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565493, "id": "gen-1789565493-wpdwTjGXblk2CLm21gti", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.19e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.19e-06, "upstream_inference_prompt_cost": 3.63e-06}, "is_byok": false, "prompt_tokens": 121, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 133}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:34.922796+00:00", "request_id": "20260916T133001Z_82875b6ee164_084", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:36.481106+00:00", "request_id": "20260916T133001Z_82875b6ee164_084", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565495, "id": "gen-1789565495-Y9NKg6Jdy9DKW9YNtQLC", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:36.522448+00:00", "request_id": "20260916T133001Z_82875b6ee164_085", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:37.992684+00:00", "request_id": "20260916T133001Z_82875b6ee164_085", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565496, "id": "gen-1789565496-nck9DYACPHZIoR3wYBg9", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:38.038879+00:00", "request_id": "20260916T133001Z_82875b6ee164_086", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:39.561416+00:00", "request_id": "20260916T133001Z_82875b6ee164_086", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 4, \"9\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565498, "id": "gen-1789565498-WtsD7eDPCuMHUtophiIX", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:39.605898+00:00", "request_id": "20260916T133001Z_82875b6ee164_087", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:40.955513+00:00", "request_id": "20260916T133001Z_82875b6ee164_087", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565499, "id": "gen-1789565499-LUnBKvZCyUz4zg2VLFXF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:40.997068+00:00", "request_id": "20260916T133001Z_82875b6ee164_088", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:42.415259+00:00", "request_id": "20260916T133001Z_82875b6ee164_088", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565501, "id": "gen-1789565501-GUSnCGXiZ2YaE3R6YKFr", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:42.463785+00:00", "request_id": "20260916T133001Z_82875b6ee164_089", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:44.069089+00:00", "request_id": "20260916T133001Z_82875b6ee164_089", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565502, "id": "gen-1789565502-4M36zHzbNSu4RVOAmoWR", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:44.121830+00:00", "request_id": "20260916T133001Z_82875b6ee164_090", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:45.271082+00:00", "request_id": "20260916T133001Z_82875b6ee164_090", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565504, "id": "gen-1789565504-VGVZGSE6b8kHiw2t1vvJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:45.313312+00:00", "request_id": "20260916T133001Z_82875b6ee164_091", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:46.529794+00:00", "request_id": "20260916T133001Z_82875b6ee164_091", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565505, "id": "gen-1789565505-OMB10OyK4A9FmMHS4ctO", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:46.579754+00:00", "request_id": "20260916T133001Z_82875b6ee164_092", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:48.196713+00:00", "request_id": "20260916T133001Z_82875b6ee164_092", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 4, \"4\": 3, \"5\": 2, \"6\": 2, \"7\": 2, \"8\": 2, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565506, "id": "gen-1789565506-DfGKG0e077pxYeCvCPBD", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:48.254636+00:00", "request_id": "20260916T133001Z_82875b6ee164_093", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:49.425999+00:00", "request_id": "20260916T133001Z_82875b6ee164_093", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565508, "id": "gen-1789565508-7VT9a4cH3OoJHdMDKVcs", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:49.479392+00:00", "request_id": "20260916T133001Z_82875b6ee164_094", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:50.849226+00:00", "request_id": "20260916T133001Z_82875b6ee164_094", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565509, "id": "gen-1789565509-yHYh2RocrZYcFPbI6BPK", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_started", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}, "2": {"maximum": 5, "minimum": 1, "type": "number"}, "3": {"maximum": 5, "minimum": 1, "type": "number"}, "4": {"maximum": 5, "minimum": 1, "type": "number"}, "5": {"maximum": 5, "minimum": 1, "type": "number"}, "6": {"maximum": 5, "minimum": 1, "type": "number"}, "7": {"maximum": 5, "minimum": 1, "type": "number"}, "8": {"maximum": 5, "minimum": 1, "type": "number"}, "9": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1", "2", "3", "4", "5", "6", "7", "8", "9"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:50.895869+00:00", "request_id": "20260916T133001Z_82875b6ee164_095", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "event": "request_completed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Never justifiable", "2", "3", "4", "5", "6", "7", "8", "9", "Always justifiable"], "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:52.065378+00:00", "request_id": "20260916T133001Z_82875b6ee164_095", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565510, "id": "gen-1789565510-jQFCZorwaxQHKRg3gOy8", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 60, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 1.323e-05, "cost_details": {"upstream_inference_completions_cost": 7.8e-06, "upstream_inference_cost": 1.323e-05, "upstream_inference_prompt_cost": 5.43e-06}, "is_byok": false, "prompt_tokens": 181, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 241}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:52.112418+00:00", "request_id": "20260916T133001Z_82875b6ee164_096", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:52.993175+00:00", "request_id": "20260916T133001Z_82875b6ee164_096", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565512, "id": "gen-1789565512-QlLF878FD3MKLMC7fBrf", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:53.045564+00:00", "request_id": "20260916T133001Z_82875b6ee164_097", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:55.789265+00:00", "request_id": "20260916T133001Z_82875b6ee164_097", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 4, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565514, "id": "gen-1789565514-WcODV05uAxooxh6LqYHl", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:55.820089+00:00", "request_id": "20260916T133001Z_82875b6ee164_098", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:56.677052+00:00", "request_id": "20260916T133001Z_82875b6ee164_098", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565515, "id": "gen-1789565515-s7tdXuKZzyBdq49Tj7mp", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:56.728327+00:00", "request_id": "20260916T133001Z_82875b6ee164_099", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:57.708254+00:00", "request_id": "20260916T133001Z_82875b6ee164_099", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565516, "id": "gen-1789565516-Xh9jUqZyXRgri5UNEOet", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:57.761519+00:00", "request_id": "20260916T133001Z_82875b6ee164_100", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:58.851055+00:00", "request_id": "20260916T133001Z_82875b6ee164_100", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565517, "id": "gen-1789565517-2ty8Wux6OH6FoZfgAbn0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:58.894735+00:00", "request_id": "20260916T133001Z_82875b6ee164_101", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:59.739077+00:00", "request_id": "20260916T133001Z_82875b6ee164_101", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565518, "id": "gen-1789565518-mnr5NuCmQp3SW1s7TjV1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:31:59.802936+00:00", "request_id": "20260916T133001Z_82875b6ee164_102", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:00.605343+00:00", "request_id": "20260916T133001Z_82875b6ee164_102", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565519, "id": "gen-1789565519-vUhslNv1LoYTAwZUAHqk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:00.653425+00:00", "request_id": "20260916T133001Z_82875b6ee164_103", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:01.548883+00:00", "request_id": "20260916T133001Z_82875b6ee164_103", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565520, "id": "gen-1789565520-RLaChA2e170z1pJSWIAE", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:01.578271+00:00", "request_id": "20260916T133001Z_82875b6ee164_104", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:02.541762+00:00", "request_id": "20260916T133001Z_82875b6ee164_104", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565521, "id": "gen-1789565521-yciYwRoGSncufvTBBudd", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:02.586478+00:00", "request_id": "20260916T133001Z_82875b6ee164_105", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:03.942629+00:00", "request_id": "20260916T133001Z_82875b6ee164_105", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565522, "id": "gen-1789565522-EMvKfrdpQFU8HxbowTaA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:03.977978+00:00", "request_id": "20260916T133001Z_82875b6ee164_106", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:04.823804+00:00", "request_id": "20260916T133001Z_82875b6ee164_106", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565524, "id": "gen-1789565524-dcr7rUoIt77idpAK0Vmq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:04.877957+00:00", "request_id": "20260916T133001Z_82875b6ee164_107", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:05.684290+00:00", "request_id": "20260916T133001Z_82875b6ee164_107", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 4}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565524, "id": "gen-1789565524-WxfAe4TdVH966a9IubFw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.85e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.85e-06, "upstream_inference_prompt_cost": 4.29e-06}, "is_byok": false, "prompt_tokens": 143, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 155}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:05.736140+00:00", "request_id": "20260916T133001Z_82875b6ee164_108", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:06.543999+00:00", "request_id": "20260916T133001Z_82875b6ee164_108", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565525, "id": "gen-1789565525-63qyRU0tJbsoIkK1T1n1", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:06.594915+00:00", "request_id": "20260916T133001Z_82875b6ee164_109", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:07.468176+00:00", "request_id": "20260916T133001Z_82875b6ee164_109", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565526, "id": "gen-1789565526-i0opZ2b2ImCTeaEW3yia", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:07.519220+00:00", "request_id": "20260916T133001Z_82875b6ee164_110", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:08.357439+00:00", "request_id": "20260916T133001Z_82875b6ee164_110", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565527, "id": "gen-1789565527-13Jzs42H4r0ufYh8dyxk", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:08.419133+00:00", "request_id": "20260916T133001Z_82875b6ee164_111", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:09.346473+00:00", "request_id": "20260916T133001Z_82875b6ee164_111", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565528, "id": "gen-1789565528-ysnC1kQwt1hvfotxQ6Te", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:09.410786+00:00", "request_id": "20260916T133001Z_82875b6ee164_112", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:10.312019+00:00", "request_id": "20260916T133001Z_82875b6ee164_112", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565529, "id": "gen-1789565529-xxfJ1UaKSuQSpkwNQ5AP", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:10.369306+00:00", "request_id": "20260916T133001Z_82875b6ee164_113", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:11.410814+00:00", "request_id": "20260916T133001Z_82875b6ee164_113", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565530, "id": "gen-1789565530-EMpJDB6efjAGwErkpOSw", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:11.461169+00:00", "request_id": "20260916T133001Z_82875b6ee164_114", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:12.690586+00:00", "request_id": "20260916T133001Z_82875b6ee164_114", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565531, "id": "gen-1789565531-RxIwGyXbOBOwQDDshW8L", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:12.735953+00:00", "request_id": "20260916T133001Z_82875b6ee164_115", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:14.254428+00:00", "request_id": "20260916T133001Z_82875b6ee164_115", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565532, "id": "gen-1789565532-ImoEekgKSBsvm0acLeSj", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:14.310641+00:00", "request_id": "20260916T133001Z_82875b6ee164_116", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:15.216739+00:00", "request_id": "20260916T133001Z_82875b6ee164_116", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565534, "id": "gen-1789565534-5THZdxRKsk46ct1Oz9ON", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:15.260550+00:00", "request_id": "20260916T133001Z_82875b6ee164_117", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:16.230579+00:00", "request_id": "20260916T133001Z_82875b6ee164_117", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565535, "id": "gen-1789565535-toA6q0RkF2UQeFKZnN3b", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:16.268755+00:00", "request_id": "20260916T133001Z_82875b6ee164_118", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:17.109356+00:00", "request_id": "20260916T133001Z_82875b6ee164_118", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565536, "id": "gen-1789565536-H5CwWpES68gGVXsLbj6m", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:17.135322+00:00", "request_id": "20260916T133001Z_82875b6ee164_119", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:19.463801+00:00", "request_id": "20260916T133001Z_82875b6ee164_119", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565538, "id": "gen-1789565538-LD052cR86ToszhSk1utA", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:19.518239+00:00", "request_id": "20260916T133001Z_82875b6ee164_120", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:21.051216+00:00", "request_id": "20260916T133001Z_82875b6ee164_120", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565539, "id": "gen-1789565539-O3chs9iWlXfv5JpiuLpq", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:21.092899+00:00", "request_id": "20260916T133001Z_82875b6ee164_121", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:21.869053+00:00", "request_id": "20260916T133001Z_82875b6ee164_121", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565541, "id": "gen-1789565541-qWdgDllibDSdQtzvEEaF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:21.909593+00:00", "request_id": "20260916T133001Z_82875b6ee164_122", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:22.698422+00:00", "request_id": "20260916T133001Z_82875b6ee164_122", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565541, "id": "gen-1789565541-ClYcUZ2rD2pJMDV4cj0H", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:22.751226+00:00", "request_id": "20260916T133001Z_82875b6ee164_123", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:23.637948+00:00", "request_id": "20260916T133001Z_82875b6ee164_123", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565542, "id": "gen-1789565542-GPT5tL4VU0CUJtaGdZd2", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:23.684442+00:00", "request_id": "20260916T133001Z_82875b6ee164_124", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:24.582719+00:00", "request_id": "20260916T133001Z_82875b6ee164_124", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565543, "id": "gen-1789565543-DuYdOYBnVZrS5Ppsl424", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:24.634480+00:00", "request_id": "20260916T133001Z_82875b6ee164_125", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:25.537213+00:00", "request_id": "20260916T133001Z_82875b6ee164_125", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565544, "id": "gen-1789565544-Olslbw66n4goZk7cpiCc", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:25.584334+00:00", "request_id": "20260916T133001Z_82875b6ee164_126", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:26.614461+00:00", "request_id": "20260916T133001Z_82875b6ee164_126", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565545, "id": "gen-1789565545-aAxawGhD1wiQanZJ3CiE", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:26.659183+00:00", "request_id": "20260916T133001Z_82875b6ee164_127", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:27.488103+00:00", "request_id": "20260916T133001Z_82875b6ee164_127", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565546, "id": "gen-1789565546-6mrC1oPmnvedYMWYTYzG", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:27.542396+00:00", "request_id": "20260916T133001Z_82875b6ee164_128", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:28.496812+00:00", "request_id": "20260916T133001Z_82875b6ee164_128", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 3, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565547, "id": "gen-1789565547-LqS4Sb6WFEJGvwL24gmJ", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:28.542307+00:00", "request_id": "20260916T133001Z_82875b6ee164_129", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:29.388941+00:00", "request_id": "20260916T133001Z_82875b6ee164_129", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565548, "id": "gen-1789565548-UuPQDi4TGDxCn4oOgTPo", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:29.417198+00:00", "request_id": "20260916T133001Z_82875b6ee164_130", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:30.260611+00:00", "request_id": "20260916T133001Z_82875b6ee164_130", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565549, "id": "gen-1789565549-zI7p16Un7xpol5t2u5E4", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:30.300532+00:00", "request_id": "20260916T133001Z_82875b6ee164_131", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:31.347509+00:00", "request_id": "20260916T133001Z_82875b6ee164_131", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565550, "id": "gen-1789565550-cnswrn9ZwkfabxaKavSi", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.88e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.88e-06, "upstream_inference_prompt_cost": 4.32e-06}, "is_byok": false, "prompt_tokens": 144, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 156}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:31.375267+00:00", "request_id": "20260916T133001Z_82875b6ee164_132", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:32.393446+00:00", "request_id": "20260916T133001Z_82875b6ee164_132", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565551, "id": "gen-1789565551-aHi1jWYUpB3eTqVb0wmg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:32.425144+00:00", "request_id": "20260916T133001Z_82875b6ee164_133", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:33.317997+00:00", "request_id": "20260916T133001Z_82875b6ee164_133", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 2}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565552, "id": "gen-1789565552-cJtHLE1WHUVenxtRZDFU", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:33.358398+00:00", "request_id": "20260916T133001Z_82875b6ee164_134", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:34.632140+00:00", "request_id": "20260916T133001Z_82875b6ee164_134", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565553, "id": "gen-1789565553-bAUdbNBZssVPzeIKHpa0", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:34.658137+00:00", "request_id": "20260916T133001Z_82875b6ee164_135", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:35.680059+00:00", "request_id": "20260916T133001Z_82875b6ee164_135", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565554, "id": "gen-1789565554-oIAxU6WshIkMs6JaWnCr", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:35.724659+00:00", "request_id": "20260916T133001Z_82875b6ee164_136", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:36.501206+00:00", "request_id": "20260916T133001Z_82875b6ee164_136", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565555, "id": "gen-1789565555-Cyfs8pKhdPSyC2lDJilg", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:36.557935+00:00", "request_id": "20260916T133001Z_82875b6ee164_137", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Important", "Not mentioned"], "presented_order": [0, 1], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Important\n1) Not mentioned\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:37.400800+00:00", "request_id": "20260916T133001Z_82875b6ee164_137", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 1}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565556, "id": "gen-1789565556-I3DS2ZZH2uO0woX2YMzh", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:37.449481+00:00", "request_id": "20260916T133001Z_82875b6ee164_138", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:38.235999+00:00", "request_id": "20260916T133001Z_82875b6ee164_138", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 1, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565557, "id": "gen-1789565557-H2MDz2cqG8DNvmf4eTpF", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:38.266113+00:00", "request_id": "20260916T133001Z_82875b6ee164_139", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:39.870757+00:00", "request_id": "20260916T133001Z_82875b6ee164_139", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565558, "id": "gen-1789565558-IXpnHacDBd1E8NbxLcUz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:39.924561+00:00", "request_id": "20260916T133001Z_82875b6ee164_140", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:40.828740+00:00", "request_id": "20260916T133001Z_82875b6ee164_140", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 3}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565560, "id": "gen-1789565560-w8fL2c3kW2wNhLqXLM8e", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:40.891665+00:00", "request_id": "20260916T133001Z_82875b6ee164_141", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:41.763108+00:00", "request_id": "20260916T133001Z_82875b6ee164_141", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565560, "id": "gen-1789565560-Laa2Gfaa6jXAJjJM7g8v", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:41.841914+00:00", "request_id": "20260916T133001Z_82875b6ee164_142", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:42.772651+00:00", "request_id": "20260916T133001Z_82875b6ee164_142", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 2, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565561, "id": "gen-1789565561-px9CVeavo8ZE6yEWIofz", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_started", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "payload": {"max_tokens": 1024, "messages": [{"content": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "role": "user"}], "model": "qwen/qwen3.7-flash", "n": 1, "reasoning": {"enabled": false}, "response_format": {"json_schema": {"name": "ratings", "schema": {"additionalProperties": false, "properties": {"0": {"maximum": 5, "minimum": 1, "type": "number"}, "1": {"maximum": 5, "minimum": 1, "type": "number"}}, "required": ["0", "1"], "type": "object"}, "strict": true}, "type": "json_schema"}, "temperature": 1.0}, "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:42.825153+00:00", "request_id": "20260916T133001Z_82875b6ee164_143", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}} +{"canonical_options": ["Important", "Not mentioned"], "event": "request_completed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "phase": "initial", "presented_options": ["Not mentioned", "Important"], "presented_order": [1, 0], "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.598421+00:00", "request_id": "20260916T133001Z_82875b6ee164_143", "response": {"choices": [{"finish_reason": "stop", "index": 0, "logprobs": null, "message": {"content": "{\"0\": 5, \"1\": 5}", "reasoning": null, "refusal": null, "role": "assistant"}, "native_finish_reason": "stop"}], "created": 1789565562, "id": "gen-1789565562-TLgEuGlGDT65plHRQuWl", "model": "qwen/qwen3.7-flash", "object": "chat.completion", "provider": "Alibaba", "service_tier": null, "system_fingerprint": null, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}}, "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "settings": {"concurrency": 1, "max_tokens": 1024, "model": "qwen/qwen3.7-flash", "n_samples": 12, "reasoning": {"enabled": false}, "req_timeout": 90.0, "structured_output": true, "temperature": 1.0}, "usage": {"completion_tokens": 12, "completion_tokens_details": {"audio_tokens": 0, "image_tokens": 0, "reasoning_tokens": 0}, "cost": 5.82e-06, "cost_details": {"upstream_inference_completions_cost": 1.56e-06, "upstream_inference_cost": 5.82e-06, "upstream_inference_prompt_cost": 4.26e-06}, "is_byok": false, "prompt_tokens": 142, "prompt_tokens_details": {"audio_tokens": 0, "cache_write_tokens": 0, "cached_tokens": 0, "video_tokens": 0}, "total_tokens": 154}} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.642563+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.683478+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.725134+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.766891+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.808608+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.850331+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.892038+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.933742+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:43.975457+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.017160+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.058861+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Homosexuality", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.100590+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.142345+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 3, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.184040+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 4, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.225768+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 3, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.267447+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.309188+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.350878+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 3, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.392619+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.434863+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.476052+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.517743+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.559463+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "dealing with people?", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.601217+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.642949+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.684653+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.726359+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 5, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.768066+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 4, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.809783+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 4, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.851497+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.893210+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.934936+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 5, \"1\": 5, \"2\": 5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:44.968289+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 5, \"1\": 5, \"2\": 5}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.001673+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.035119+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Signing a petition", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.068534+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.101894+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.135226+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 5, \"1\": 2, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.168632+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 3, \"1\": 4, \"2\": 5}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.202000+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.235394+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.268783+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.302185+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.343874+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.385586+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 5, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.427408+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.469077+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 5, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.510792+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.552459+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 3, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.594189+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 4, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.635905+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 4, \"1\": 3, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.677614+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 4, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.719349+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 3, \"1\": 4, \"2\": 2}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.761039+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 4, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.802747+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 3, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.844454+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 4, \"1\": 3, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.886237+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.927921+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 4, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:45.969644+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.011372+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 4, \"1\": 5, \"2\": 1}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.053056+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 3, \"1\": 3, \"2\": 2, \"3\": 1}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.094779+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 1, \"1\": 3, \"2\": 4, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.136488+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.178189+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.219917+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.261665+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 3, \"1\": 2, \"2\": 2, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.303403+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.345111+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.386838+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 4}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.428553+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 1, \"1\": 3, \"2\": 4, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.470249+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 3, \"1\": 3, \"2\": 2, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "Religion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.511944+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.553656+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 3, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.595393+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.637102+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.678776+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.720529+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.762224+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.803990+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.845692+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.887489+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.929157+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:46.970842+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "God", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.012533+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.054299+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.095991+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.137679+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 4, \"9\": 3}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.179438+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.221209+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.262888+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.304592+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.346384+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.388047+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 4, \"4\": 3, \"5\": 2, \"6\": 2, \"7\": 2, \"8\": 2, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.429717+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.471407+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"} +{"event": "answer_parsed", "item_id": "Abortion", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.513205+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.554840+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 3, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.596550+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 4, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.638329+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.680016+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.721733+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 3, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.763432+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 3, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.805151+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.846893+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 2, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.888598+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 2, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.930314+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:47.972111+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 5, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Obedience", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.013868+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 3, \"1\": 4}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.055541+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.097303+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.138999+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.180734+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.222987+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.264682+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.306411+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.348145+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.389845+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.431572+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.473269+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Independence", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.514968+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.556675+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.598458+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.640202+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.681941+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.723680+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.765299+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.806994+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 3, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.848801+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.890448+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 3, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.932116+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:48.973831+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.015563+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.057347+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 0, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.099034+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 1, "text": "{\"0\": 5, \"1\": 2}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.140741+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 2, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.182426+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 3, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.224156+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 4, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [0, 1], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.265886+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 5, "text": "{\"0\": 5, \"1\": 1}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.307567+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 6, "text": "{\"0\": 1, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.349277+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 7, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.390995+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 8, "text": "{\"0\": 5, \"1\": 3}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.432709+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 9, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.474423+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 10, "text": "{\"0\": 2, \"1\": 5}"} +{"event": "answer_parsed", "item_id": "Imagination", "model": "qwen/qwen3.7-flash", "parsed": true, "presented_order": [1, 0], "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.516148+00:00", "run_id": "20260916T133001Z_82875b6ee164", "sample": 11, "text": "{\"0\": 5, \"1\": 5}"} +{"event": "item_result", "failed_samples": 0, "id": "Homosexuality", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.09322478991596639, 0.09114145658263305, 0.08905812324929972, 0.08697478991596637, 0.0946953781512605, 0.0946953781512605, 0.0946953781512605, 0.0946953781512605, 0.0946953781512605, 0.16612394957983198], "p_samples": [[0.029411764705882353, 0.029411764705882353, 0.029411764705882353, 0.029411764705882353, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177, 0.14705882352941177], [0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.35714285714285715], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.35714285714285715], [0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.35714285714285715], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.025, 0.05, 0.075, 0.1, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], "pmass_allowed": 1.0, "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nHomosexuality\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.558002+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "dealing with people?", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7329365079365079, 0.267063492063492], "p_samples": [[0.5, 0.5], [0.6666666666666666, 0.3333333333333333], [0.6, 0.4], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.6, 0.4], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666]], "pmass_allowed": 1.0, "prompt": "Generally speaking, would you say that most people can be trusted or that you need to be very careful in dealing with people?\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Need to be very careful\n1) Most people can be trusted\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.599788+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 3, \"1\": 3}", "{\"0\": 4, \"1\": 2}", "{\"0\": 3, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 3, \"1\": 2}", "{\"0\": 2, \"1\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 2, \"1\": 5}", "{\"0\": 1, \"1\": 5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Signing a petition", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.45269360269360276, 0.40315656565656566, 0.14414983164983167], "p_samples": [[0.5555555555555556, 0.3333333333333333, 0.1111111111111111], [0.4, 0.5, 0.1], [0.45454545454545453, 0.45454545454545453, 0.09090909090909091], [0.5, 0.375, 0.125], [0.5, 0.375, 0.125], [0.5, 0.4, 0.1], [0.4, 0.5, 0.1], [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], [0.3333333333333333, 0.3333333333333333, 0.3333333333333333], [0.5, 0.4, 0.1], [0.5555555555555556, 0.3333333333333333, 0.1111111111111111], [0.4, 0.5, 0.1]], "pmass_allowed": 1.0, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nSigning a petition\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.641399+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\": 4, \"1\": 5, \"2\": 1}", "{\"0\": 5, \"1\": 5, \"2\": 1}", "{\"0\": 4, \"1\": 3, \"2\": 1}", "{\"0\": 4, \"1\": 3, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 4, \"1\": 5, \"2\": 1}", "{\"0\": 5, \"1\": 5, \"2\": 5}", "{\"0\": 5, \"1\": 5, \"2\": 5}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\": 4, \"1\": 5, \"2\": 1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Attending peaceful demonstrations", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.491266835016835, 0.37992424242424244, 0.1288089225589226], "p_samples": [[0.5, 0.4, 0.1], [0.625, 0.25, 0.125], [0.25, 0.3333333333333333, 0.4166666666666667], [0.5, 0.4, 0.1], [0.5, 0.4, 0.1], [0.45454545454545453, 0.45454545454545453, 0.09090909090909091], [0.5555555555555556, 0.3333333333333333, 0.1111111111111111], [0.5, 0.4, 0.1], [0.45454545454545453, 0.45454545454545453, 0.09090909090909091], [0.5, 0.4, 0.1], [0.5555555555555556, 0.3333333333333333, 0.1111111111111111], [0.5, 0.4, 0.1]], "pmass_allowed": 1.0, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nAttending peaceful demonstrations\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.683081+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 2, \"2\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 5}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 5, \"2\": 1}", "{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 5, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 3, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Joining in boycotts", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.44398148148148153, 0.4231481481481481, 0.1328703703703704], "p_samples": [[0.375, 0.5, 0.125], [0.5, 0.375, 0.125], [0.4444444444444444, 0.3333333333333333, 0.2222222222222222], [0.5, 0.375, 0.125], [0.3333333333333333, 0.4444444444444444, 0.2222222222222222], [0.5, 0.375, 0.125], [0.375, 0.5, 0.125], [0.5, 0.375, 0.125], [0.5, 0.4, 0.1], [0.5, 0.4, 0.1], [0.4, 0.5, 0.1], [0.4, 0.5, 0.1]], "pmass_allowed": 1.0, "prompt": "Now I'd like you to look at this card. I'm going to read out some different forms of political action that people can take, and I'd like you to tell me, for each one, whether you have actually done any of these things, whether you might do it or would never, under any circumstances, do it.\n\nJoining in boycotts\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Have done\n1) Might do\n2) Would never do\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.724897+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 3, \"1\": 4, \"2\": 1}", "{\"0\": 4, \"1\": 3, \"2\": 1}", "{\"0\": 4, \"1\": 3, \"2\": 2}", "{\"0\": 4, \"1\": 3, \"2\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 2}", "{\"0\": 4, \"1\": 3, \"2\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 1}", "{\"0\": 4, \"1\": 3, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 1}", "{\"0\": 4, \"1\": 5, \"2\": 1}", "{\"0\": 4, \"1\": 5, \"2\": 1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Religion", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.21848706848706848, 0.21802780552780554, 0.1960229585229585, 0.3674621674621674], "p_samples": [[0.3333333333333333, 0.3333333333333333, 0.2222222222222222, 0.1111111111111111], [0.07692307692307693, 0.23076923076923078, 0.3076923076923077, 0.38461538461538464], [0.25, 0.25, 0.25, 0.25], [0.2727272727272727, 0.18181818181818182, 0.09090909090909091, 0.45454545454545453], [0.2727272727272727, 0.18181818181818182, 0.09090909090909091, 0.45454545454545453], [0.25, 0.16666666666666666, 0.16666666666666666, 0.4166666666666667], [0.14285714285714285, 0.21428571428571427, 0.2857142857142857, 0.35714285714285715], [0.2727272727272727, 0.18181818181818182, 0.09090909090909091, 0.45454545454545453], [0.3, 0.2, 0.1, 0.4], [0.07692307692307693, 0.23076923076923078, 0.3076923076923077, 0.38461538461538464], [0.23076923076923078, 0.23076923076923078, 0.15384615384615385, 0.38461538461538464], [0.14285714285714285, 0.21428571428571427, 0.2857142857142857, 0.35714285714285715]], "pmass_allowed": 1.0, "prompt": "For each of the following aspects, indicate how important it is in your life. Would you say it is very important, rather important, not very important or not important at all\n\nReligion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Very important\n1) Rather important\n2) Not very important\n3) Not at all important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.766595+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 3, \"1\": 3, \"2\": 2, \"3\": 1}", "{\"0\": 1, \"1\": 3, \"2\": 4, \"3\": 5}", "{\"0\": 2, \"1\": 2, \"2\": 2, \"3\": 2}", "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}", "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}", "{\"0\": 3, \"1\": 2, \"2\": 2, \"3\": 5}", "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}", "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 5}", "{\"0\": 3, \"1\": 2, \"2\": 1, \"3\": 4}", "{\"0\": 1, \"1\": 3, \"2\": 4, \"3\": 5}", "{\"0\": 3, \"1\": 3, \"2\": 2, \"3\": 5}", "{\"0\": 2, \"1\": 3, \"2\": 4, \"3\": 5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "God", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.4250000000000001, 0.5749999999999998], "p_samples": [[0.6, 0.4], [0.8333333333333334, 0.16666666666666666], [0.16666666666666666, 0.8333333333333334], [0.8333333333333334, 0.16666666666666666], [0.16666666666666666, 0.8333333333333334], [0.8333333333333334, 0.16666666666666666], [0.16666666666666666, 0.8333333333333334], [0.8333333333333334, 0.16666666666666666], [0.16666666666666666, 0.8333333333333334], [0.16666666666666666, 0.8333333333333334], [0.16666666666666666, 0.8333333333333334], [0.16666666666666666, 0.8333333333333334]], "pmass_allowed": 1.0, "prompt": "In which of the following things do you believe, if you believe in any?\n\nGod\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) No\n1) Yes\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.808476+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 3, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 1, \"1\": 5}", "{\"0\": 5, \"1\": 1}", "{\"0\": 1, \"1\": 5}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 1, \"1\": 5}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Abortion", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.13656551781551782, 0.1300574425574426, 0.12354936729936732, 0.10919497169497172, 0.0948405760905761, 0.08865925740925741, 0.08545412920412922, 0.082249000999001, 0.0771499333999334, 0.07227980352980352], "p_samples": [[0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.06818181818181818, 0.09090909090909091, 0.11363636363636363, 0.11363636363636363, 0.11363636363636363, 0.11363636363636363, 0.11363636363636363, 0.11363636363636363, 0.09090909090909091, 0.06818181818181818], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.038461538461538464, 0.07692307692307693, 0.11538461538461539, 0.15384615384615385, 0.19230769230769232, 0.15384615384615385, 0.11538461538461539, 0.07692307692307693, 0.038461538461538464, 0.038461538461538464], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.25, 0.2, 0.15, 0.1, 0.05, 0.05, 0.05, 0.05, 0.05, 0.05], [0.10714285714285714, 0.14285714285714285, 0.17857142857142858, 0.14285714285714285, 0.10714285714285714, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.07142857142857142, 0.03571428571428571], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1], [0.025, 0.05, 0.075, 0.1, 0.125, 0.125, 0.125, 0.125, 0.125, 0.125], [0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1, 0.1]], "pmass_allowed": 1.0, "prompt": "Please tell me for each of the following statements whether you think it can always be justified, never be justified, or something in between, using this card.\n\nAbortion\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Never justifiable\n1) 2\n2) 3\n3) 4\n4) 5\n5) 6\n6) 7\n7) 8\n8) 9\n9) Always justifiable\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.850074+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 5, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 4, \"9\": 3}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 4, \"6\": 3, \"7\": 2, \"8\": 1, \"9\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 5, \"1\": 4, \"2\": 3, \"3\": 2, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 3, \"1\": 4, \"2\": 5, \"3\": 4, \"4\": 3, \"5\": 2, \"6\": 2, \"7\": 2, \"8\": 2, \"9\": 1}", "{\"0\": 1, \"1\": 1, \"2\": 1, \"3\": 1, \"4\": 1, \"5\": 1, \"6\": 1, \"7\": 1, \"8\": 1, \"9\": 1}", "{\"0\": 1, \"1\": 2, \"2\": 3, \"3\": 4, \"4\": 5, \"5\": 5, \"6\": 5, \"7\": 5, \"8\": 5, \"9\": 5}", "{\"0\": 3, \"1\": 3, \"2\": 3, \"3\": 3, \"4\": 3, \"5\": 3, \"6\": 3, \"7\": 3, \"8\": 3, \"9\": 3}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Obedience", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.5556878306878307, 0.44431216931216927], "p_samples": [[0.75, 0.25], [0.5714285714285714, 0.42857142857142855], [0.7142857142857143, 0.2857142857142857], [0.7142857142857143, 0.2857142857142857], [0.375, 0.625], [0.6, 0.4], [0.375, 0.625], [0.6, 0.4], [0.6666666666666666, 0.3333333333333333], [0.2857142857142857, 0.7142857142857143], [0.4444444444444444, 0.5555555555555556], [0.5714285714285714, 0.42857142857142855]], "pmass_allowed": 1.0, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nObedience\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.891957+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 3, \"1\": 1}", "{\"0\": 4, \"1\": 3}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 2}", "{\"0\": 3, \"1\": 5}", "{\"0\": 3, \"1\": 2}", "{\"0\": 5, \"1\": 3}", "{\"0\": 2, \"1\": 3}", "{\"0\": 2, \"1\": 4}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 4}", "{\"0\": 3, \"1\": 4}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Independence", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7380952380952381, 0.26190476190476186], "p_samples": [[0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.5, 0.5], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.5, 0.5]], "pmass_allowed": 1.0, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nIndependence\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.933608+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 5}", "{\"0\": 2, \"1\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 1, \"1\": 5}", "{\"0\": 5, \"1\": 5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Determination, perseverance", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.7132936507936507, 0.2867063492063492], "p_samples": [[0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666], [0.625, 0.375], [0.7142857142857143, 0.2857142857142857], [0.625, 0.375], [0.5, 0.5], [0.5, 0.5], [0.7142857142857143, 0.2857142857142857]], "pmass_allowed": 1.0, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nDetermination, perseverance\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:49.966968+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 3, \"1\": 5}", "{\"0\": 2, \"1\": 5}", "{\"0\": 3, \"1\": 5}", "{\"0\": 5, \"1\": 5}", "{\"0\": 5, \"1\": 5}", "{\"0\": 2, \"1\": 5}"], "valid_samples": 12} +{"event": "item_result", "failed_samples": 0, "id": "Imagination", "model": "qwen/qwen3.7-flash", "n_samples": 12, "p": [0.6468253968253969, 0.3531746031746032], "p_samples": [[0.8333333333333334, 0.16666666666666666], [0.7142857142857143, 0.2857142857142857], [0.8333333333333334, 0.16666666666666666], [0.625, 0.375], [0.625, 0.375], [0.8333333333333334, 0.16666666666666666], [0.8333333333333334, 0.16666666666666666], [0.375, 0.625], [0.375, 0.625], [0.5, 0.5], [0.7142857142857143, 0.2857142857142857], [0.5, 0.5]], "pmass_allowed": 1.0, "prompt": "Here is a list of qualities that children can be encouraged to learn at home. Which, if any, do you consider to be especially important? Please choose up to five.\n\nImagination\n\nRate how strongly you personally agree with or endorse EACH of the following answers, on a scale of 1 (strongly reject) to 5 (strongly endorse):\n0) Not mentioned\n1) Important\n\nRespond with ONLY a JSON object mapping each answer's number to its 1-5 rating, e.g. {\"0\": 2, \"1\": 5}. Rate every answer, and output nothing but the JSON.", "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:50.000349+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "texts": ["{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 2}", "{\"0\": 5, \"1\": 1}", "{\"0\": 5, \"1\": 3}", "{\"0\": 5, \"1\": 3}", "{\"0\": 5, \"1\": 1}", "{\"0\": 1, \"1\": 5}", "{\"0\": 5, \"1\": 3}", "{\"0\": 5, \"1\": 3}", "{\"0\": 5, \"1\": 5}", "{\"0\": 2, \"1\": 5}", "{\"0\": 5, \"1\": 5}"], "valid_samples": 12} +{"event": "run_finished", "failed_samples": 0, "model": "qwen/qwen3.7-flash", "planned_requests": 144, "protocol_id": "82875b6ee164d0d980eba738bf05f69959234567b91f9cfd01fcabca0bd70dac", "recorded_at_utc": "2026-09-16T13:32:50.033767+00:00", "rescued_samples": 0, "run_id": "20260916T133001Z_82875b6ee164", "valid_samples": 144} diff --git a/slop/research/wvs/20260916_openrouter/wvs_model_ci.md b/slop/research/wvs/20260916_openrouter/wvs_model_ci.md new file mode 100644 index 0000000..33dba1e --- /dev/null +++ b/slop/research/wvs/20260916_openrouter/wvs_model_ci.md @@ -0,0 +1,20 @@ +| model | x self-expr | y secular | x 95%CI | y 95%CI | +|:---------------------------|--------------:|------------:|----------:|----------:| +| qwen3.7-max (rated) | +0.37 | +0.69 | +0.16 | +0.17 | +| gemma-4-31b-it (rated) | +0.36 | +0.66 | +0.16 | +0.13 | +| grok-4.20 (rated) | +0.59 | +0.62 | +0.11 | +0.17 | +| gemini-2.5-pro (rated) | +0.47 | +0.70 | +0.13 | +0.14 | +| grok-4.3 (rated) | +0.44 | +0.73 | +0.14 | +0.13 | +| deepseek-v4-flash (rated) | +0.57 | +0.64 | +0.11 | +0.16 | +| gpt-5.4 (rated) | +0.45 | +0.68 | +0.13 | +0.14 | +| mistral-large-2512 (rated) | +0.65 | +0.64 | +0.08 | +0.18 | +| gemma-3-27b-it (rated) | +0.60 | +0.60 | +0.09 | +0.17 | +| llama-4-maverick (rated) | +0.64 | +0.64 | +0.08 | +0.18 | +| qwen3.7-flash (rated) | +0.65 | +0.59 | +0.10 | +0.16 | +| gpt-5.3-chat (rated) | +0.50 | +0.67 | +0.09 | +0.16 | +| deepseek-v4-pro (rated) | +0.55 | +0.73 | +0.15 | +0.10 | +| llama-4-scout (rated) | +0.62 | +0.53 | +0.05 | +0.20 | +| gpt-5.5 (rated) | +0.42 | +0.76 | +0.17 | +0.07 | +| claude-opus-4.7 (rated) | +0.59 | +0.60 | +0.07 | +0.09 | +| claude-opus-4.8 (rated) | +0.61 | +0.58 | +0.07 | +0.07 | +| claude-opus-4.6 (rated) | +0.63 | +0.63 | +0.04 | +0.05 | diff --git a/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_corrected.png b/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_corrected.png new file mode 100644 index 0000000..42e3ea1 Binary files /dev/null and b/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_corrected.png differ diff --git a/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_diagnostic.png b/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_diagnostic.png new file mode 100644 index 0000000..42e3ea1 Binary files /dev/null and b/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_diagnostic.png differ diff --git a/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_schema.png b/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_schema.png new file mode 100644 index 0000000..f47a3a1 Binary files /dev/null and b/slop/research/wvs/20260916_openrouter/wvs_qwen3.7-flash_schema.png differ diff --git a/src/moralmaps/maps.py b/src/moralmaps/maps.py index e92d7c7..9d4e3e3 100644 --- a/src/moralmaps/maps.py +++ b/src/moralmaps/maps.py @@ -269,18 +269,28 @@ MODEL_FAMILY_COLORS = { "gemini": "#0ea5e9", # Gemini / Google -> sea blue (sibling of gemma, bluer) "gpt": "#2563eb", # OpenAI -> blue "llama": "#6d5ae0", # Llama / Meta -> indigo + "muse": "#7c3aed", # Muse / Meta -> violet "claude": "#c026d3", # Anthropic -> purple / magenta "grok": "#2b2d42", # Grok / xAI -> near-black (brand), well clear of gpt blue + "kimi": "#a16207", # Kimi / Moonshot -> ochre + "glm": "#dc2626", # GLM / Z.ai -> red + "inkling": "#0891b2", # Inkling / Thinking Machines -> cyan } -def model_family_color(name: str) -> str: - """The lab-family colour for a model key (substring match on the family name), MODEL_RED if none.""" +def model_family(name: str) -> str | None: + """Stable model-series name used to choose one label from each plotted family.""" key = name.lower() - for fam, col in MODEL_FAMILY_COLORS.items(): - if fam in key: - return col - return MODEL_RED + for family in MODEL_FAMILY_COLORS: + if family in key: + return family + return None + + +def model_family_color(name: str) -> str: + """The model-series colour for a model key, or MODEL_RED if the series is unknown.""" + family = model_family(name) + return MODEL_FAMILY_COLORS[family] if family is not None else MODEL_RED def plot_value_map(display: str, countries: list[str], P: np.ndarray, diff --git a/src/moralmaps/read_api.py b/src/moralmaps/read_api.py index 46769d3..28dc7f2 100644 --- a/src/moralmaps/read_api.py +++ b/src/moralmaps/read_api.py @@ -25,9 +25,13 @@ and E degenerates to an integer. from __future__ import annotations import asyncio +import hashlib import json +import os import re +from datetime import UTC, datetime from math import inf +from pathlib import Path import numpy as np from loguru import logger @@ -141,26 +145,40 @@ def _parse_ratings(text: str, n: int) -> dict[int, float] | None: return out -_FORCE_MSG = ('You are out of time. Output ONLY a single-line compact JSON object mapping each answer ' - 'number to its 1-5 rating, e.g. {{"0": 1, "1": 5}}. No markdown, no reasoning, nothing else.') +def _rating_schema(n: int) -> dict: + keys = [str(k) for k in range(n)] + return {"type": "json_schema", "json_schema": {"name": "ratings", "strict": True, "schema": { + "type": "object", "properties": {key: {"type": "number", "minimum": 1, "maximum": 5} + for key in keys}, "required": keys, "additionalProperties": False, + }}} + + +def _force_msg(n: int) -> str: + keys = ", ".join(f'"{k}"' for k in range(n)) + example = ", ".join(f'"{k}": 1' for k in range(n)) + return (f"Output ONLY a compact JSON object with every required key [{keys}] and values from 1 to 5, " + f"for example {{{example}}}. No markdown, no reasoning, nothing else.") async def _force_answer(model: str, prompt: str, phase1_msg: dict, temperature: float, - max_tokens: int, req_timeout: float) -> str: + max_tokens: int, req_timeout: float, reasoning: dict | None, + response_format: dict | None, n: int) -> dict: """Phase-2 rescue (wassname's bounded-thinking pattern, gist 72eed3a1): a reasoning model that spent its whole budget thinking and truncated the JSON mid-object gets a follow-up in the SAME conversation -- feed its (truncated) reasoning back as the assistant turn, then demand a compact - one-line answer NOW. It has already thought, so it just commits. Works even where reasoning can't - be disabled (some providers, e.g. gemini-2.5-pro, MANDATE it and 400 on reasoning_effort=none), so - we do NOT pass a reasoning-off knob -- we constrain the OUTPUT instead. A bigger cap than phase 1 - (reasoning models re-think briefly). Still parsed by the caller; may fail again -> dropped sample.""" + one-line answer NOW. The caller keeps its selected reasoning configuration unchanged across both + phases. A bigger cap than phase 1 lets mandatory-reasoning models finish the compact object. Still + parsed by the caller; may fail again -> dropped sample.""" tail = (phase1_msg.get("reasoning") or phase1_msg.get("content") or "")[-1500:] or "(thinking truncated)" msgs = [{"role": "user", "content": prompt}, {"role": "assistant", "content": tail}, - {"role": "user", "content": _FORCE_MSG.format()}] + {"role": "user", "content": _force_msg(n)}] payload = {"model": model, "messages": msgs, "temperature": temperature, "max_tokens": max(max_tokens, 2048)} - data = await asyncio.wait_for(openrouter_request(payload), timeout=req_timeout) - return data["choices"][0]["message"].get("content") or "" + if reasoning is not None: + payload["reasoning"] = reasoning + if response_format is not None: + payload["response_format"] = response_format + return await asyncio.wait_for(openrouter_request(payload), timeout=req_timeout) def _rate_plan(items: list[dict], n_samples: int, per_call: int = 1) -> list[dict]: @@ -173,88 +191,183 @@ def _rate_plan(items: list[dict], n_samples: int, per_call: int = 1) -> list[dic opts, n = it["options"], it["n"] groups = [([0, 1], (n_samples + 1) // 2), ([1, 0], n_samples // 2)] if n == 2 \ else [(list(range(n)), n_samples)] + sample = 0 for perm, tot in groups: legend = "\n".join(f"{j}) {opts[perm[j]]}" for j in range(n)) prompt = _RATE_PROMPT.format(question=it["question"], legend=legend) while tot > 0: k = min(tot, per_call); tot -= k - plan.append({"i": i, "perm": perm, "prompt": prompt, "cnt": k}) + plan.append({"i": i, "perm": perm, "prompt": prompt, "cnt": k, + "sample": sample, "presented_options": [opts[j] for j in perm]}) + sample += k return plan +def rated_protocol_identity(model: str, items: list[dict], *, n_samples: int, temperature: float, + max_tokens: int, concurrency: int, req_timeout: float, + reasoning: dict | None, structured_output: bool) -> str: + """Hash the exact model, rendered prompts, and request settings that define a cacheable panel.""" + plan = _rate_plan(items, n_samples) + protocol = { + "schema": 2, + "model": model, + "temperature": temperature, + "max_tokens": max_tokens, + "concurrency": concurrency, + "req_timeout": req_timeout, + "reasoning": reasoning, + "structured_output": structured_output, + "rate_prompt": _RATE_PROMPT, + "rescue_prompt": _force_msg(10), + "requests": [{key: req[key] for key in ("i", "perm", "prompt", "cnt", "sample", "presented_options")} + for req in plan], + } + encoded = json.dumps(protocol, sort_keys=True, separators=(",", ":"), ensure_ascii=True).encode() + return hashlib.sha256(encoded).hexdigest() + + +def _append_record(path: Path, record: dict) -> None: + record["recorded_at_utc"] = datetime.now(UTC).isoformat() + with path.open("a", encoding="utf-8") as fh: + fh.write(json.dumps(record, ensure_ascii=True, sort_keys=True) + "\n") + fh.flush() + os.fsync(fh.fileno()) + + def read_items_rated(model: str, items: list[dict], *, n_samples: int = 12, temperature: float = 1.0, max_tokens: int = 512, concurrency: int = 8, req_timeout: float = 90.0, + reasoning: dict | None = None, structured_output: bool = False, records_path: str | Path, verbose_first: bool = False) -> list[dict]: - """Dense Likert readout: per item, ask the model to rate EVERY option 1-5 as JSON, N times, and - normalize the mean rating to a per-option distribution `p`. Higher signal per call than a single - forced choice, and positional bias is controlled by permuting the PRESENTED order of BINARY items - (n==2) across samples then mapping ratings back to the canonical option order. All requests for the - model fire CONCURRENTLY (asyncio.gather, capped at `concurrency`) so a 12-item panel is ~1 round - trip, not 24 sequential ones. A reasoning model that burns its token budget thinking and truncates - the JSON is rescued by a one-shot force-answer follow-up (_force_answer) instead of being dropped. + """Run one dense rating panel and write an fsynced JSONL event for every paid request phase. - `items`: [{"id", "question", "options"(canonical), "n"}]. Returns per item: id, p (mean over valid - samples, canonical order), p_samples (per-sample canonical p arrays for bootstrap CIs), - pmass_allowed (valid-JSON fraction), prompt, texts. p is NaN at total parse collapse (do not - compare), matching the logprob reader. A failed request (network) just drops its samples.""" + The record is the source of truth. It preserves dispatches, responses, rescues, provider usage, + errors, prompt identity, presented option order, and final per-item samples. Returned rows are a + reduced view for coordinates only. An incomplete item stays incomplete and the caller must not plot it. + """ + assert temperature > 0, "sampling readout needs temperature > 0" plan = _rate_plan(items, n_samples) + protocol_id = rated_protocol_identity(model, items, n_samples=n_samples, temperature=temperature, + max_tokens=max_tokens, concurrency=concurrency, + req_timeout=req_timeout, reasoning=reasoning, + structured_output=structured_output) + run_id = f"{datetime.now(UTC).strftime('%Y%m%dT%H%M%SZ')}_{protocol_id[:12]}" + rpath = Path(records_path) + rpath.parent.mkdir(parents=True, exist_ok=True) + settings = {"model": model, "n_samples": n_samples, "temperature": temperature, + "max_tokens": max_tokens, "concurrency": concurrency, "req_timeout": req_timeout, + "reasoning": reasoning, "structured_output": structured_output} + _append_record(rpath, {"event": "run_started", "run_id": run_id, "protocol_id": protocol_id, + "settings": settings, "items": items, "planned_requests": len(plan)}) - async def run_all() -> list: + async def run_all() -> list[dict]: sem = asyncio.Semaphore(concurrency) - async def call(req): + + async def call(seq: int, req: dict) -> dict: + item = items[req["i"]] + request_id = f"{run_id}_{seq:03d}" + request_meta = {"request_id": request_id, "run_id": run_id, "protocol_id": protocol_id, + "model": model, "item_id": item["id"], "canonical_options": item["options"], + "presented_options": req["presented_options"], "presented_order": req["perm"], + "sample": req["sample"], "prompt": req["prompt"], "settings": settings} + payload = {"model": model, "messages": [{"role": "user", "content": req["prompt"]}], + "temperature": temperature, "n": req["cnt"], "max_tokens": max_tokens} + if reasoning is not None: + payload["reasoning"] = reasoning + response_format = _rating_schema(item["n"]) if structured_output else None + if response_format is not None: + payload["response_format"] = response_format + phase = "initial" async with sem: - n = items[req["i"]]["n"] - payload = {"model": model, "messages": [{"role": "user", "content": req["prompt"]}], - "temperature": temperature, "n": req["cnt"], "max_tokens": max_tokens} - # per-request wall-clock cap: one request stuck in the wrapper's stamina backoff (a - # rate-limited provider) must not stall the whole model's gather -- time it out and drop - # it as a failed sample (return_exceptions catches the TimeoutError) so the panel moves on. - data = await asyncio.wait_for(openrouter_request(payload), timeout=req_timeout) - out = [] - for c in data["choices"]: - content = c["message"].get("content") or "" - if _parse_ratings(content, n) is None: # truncated JSON / reasoning ate the budget - content = await _force_answer(model, req["prompt"], c["message"], - temperature, max_tokens, req_timeout) - out.append(content) - return out - return await asyncio.gather(*(call(r) for r in plan), return_exceptions=True) + try: + _append_record(rpath, {"event": "request_started", "phase": phase, + **request_meta, "payload": payload}) + data = await asyncio.wait_for(openrouter_request(payload), timeout=req_timeout) + _append_record(rpath, {"event": "request_completed", "phase": phase, + **request_meta, "response": data, "usage": data.get("usage")}) + if len(data["choices"]) != req["cnt"]: + raise ValueError(f"expected {req['cnt']} choices, got {len(data['choices'])}") + message = data["choices"][0]["message"] + text = message.get("content") or "" + rescued = False + if _parse_ratings(text, item["n"]) is None: + phase = "rescue" + rescue_payload = {"model": model, "temperature": temperature, + "max_tokens": max(max_tokens, 2048), "messages": [ + {"role": "user", "content": req["prompt"]}, + {"role": "assistant", "content": + (message.get("reasoning") or message.get("content") or "")[-1500:] + or "(thinking truncated)"}, + {"role": "user", "content": _force_msg(item["n"])}, + ]} + if response_format is not None: + rescue_payload["response_format"] = response_format + if reasoning is not None: + rescue_payload["reasoning"] = reasoning + _append_record(rpath, {"event": "request_started", "phase": phase, + **request_meta, "payload": rescue_payload, + "initial_response_message": message}) + rescue = await _force_answer(model, req["prompt"], message, temperature, + max_tokens, req_timeout, reasoning, response_format, item["n"]) + _append_record(rpath, {"event": "request_completed", "phase": phase, + **request_meta, "response": rescue, "usage": rescue.get("usage")}) + if len(rescue["choices"]) != 1: + raise ValueError(f"expected one rescue choice, got {len(rescue['choices'])}") + text = rescue["choices"][0]["message"].get("content") or "" + rescued = True + return {"text": text, "rescued": rescued, "error": None} + except Exception as exc: + _append_record(rpath, {"event": "request_failed", "phase": phase, + **request_meta, "error_type": type(exc).__name__, "error": str(exc)}) + return {"text": None, "rescued": phase == "rescue", "error": f"{type(exc).__name__}: {exc}"} + + return await asyncio.gather(*(call(seq, req) for seq, req in enumerate(plan))) results = asyncio.run(run_all()) - agg = {i: {"p_samples": [], "texts": [], "prompt": ""} for i in range(len(items))} - n_fail = 0 - for req, res in zip(plan, results): + agg = {i: {"p_samples": [], "texts": [], "failed": 0, "rescued": 0, "prompt": ""} + for i in range(len(items))} + for req, result in zip(plan, results): i, n, perm = req["i"], items[req["i"]]["n"], req["perm"] agg[i]["prompt"] = req["prompt"] - if isinstance(res, Exception): - n_fail += 1 + agg[i]["rescued"] += int(result["rescued"]) + if result["error"] is not None: + agg[i]["failed"] += 1 continue - for text in res: - agg[i]["texts"].append(text) - rated = _parse_ratings(text, n) - if rated is None: - continue - r_canon = np.zeros(n) - for j in range(n): - r_canon[perm[j]] = rated[j] # map presented label -> canonical option - agg[i]["p_samples"].append(r_canon / r_canon.sum()) - if n_fail: - logger.warning(f"{model}: {n_fail}/{len(plan)} rating calls failed (network) -> fewer samples") + text = result["text"] + agg[i]["texts"].append(text) + rated = _parse_ratings(text, n) + _append_record(rpath, {"event": "answer_parsed", "run_id": run_id, "protocol_id": protocol_id, + "model": model, "item_id": items[i]["id"], "sample": req["sample"], + "presented_order": perm, "text": text, "parsed": rated is not None}) + if rated is None: + continue + r_canon = np.zeros(n) + for j in range(n): + r_canon[perm[j]] = rated[j] + agg[i]["p_samples"].append(r_canon / r_canon.sum()) out = [] - for i, it in enumerate(items): + for i, item in enumerate(items): ps = agg[i]["p_samples"] - n = it["n"] - p = np.mean(ps, axis=0) if ps else np.full(n, np.nan) - out.append({"id": it["id"], "p": p, "p_samples": [x.tolist() for x in ps], - "pmass_allowed": len(ps) / n_samples, "n_samples": n_samples, - "prompt": agg[i]["prompt"], "texts": agg[i]["texts"]}) + p = np.mean(ps, axis=0) if ps else np.full(item["n"], np.nan) + row = {"id": item["id"], "p": p, "p_samples": [x.tolist() for x in ps], + "pmass_allowed": len(ps) / n_samples, "n_samples": n_samples, + "valid_samples": len(ps), "failed_samples": agg[i]["failed"], + "rescued_samples": agg[i]["rescued"], "prompt": agg[i]["prompt"], + "texts": agg[i]["texts"], "protocol_id": protocol_id, "run_id": run_id} + _append_record(rpath, {"event": "item_result", "run_id": run_id, "protocol_id": protocol_id, + "model": model, **row, "p": np.asarray(p).tolist()}) + out.append(row) if verbose_first and i == 0: logger.debug( f"\n=== TRACE read_items_rated first item ({model}, N={n_samples}) ===\n" - f"--- prompt ---\n{agg[i]['prompt']}\n" - f"--- first 2 raw replies ---\n{agg[i]['texts'][:2]}\n" - f"--- mean p over {it['options']} ---\n{np.round(p, 3).tolist()} valid={len(ps)}/{n_samples}\n" + f"--- prompt ---\n{row['prompt']}\n" + f"--- first 2 raw replies ---\n{row['texts'][:2]}\n" + f"--- mean p over {item['options']} ---\n{np.round(p, 3).tolist()} valid={len(ps)}/{n_samples}\n" f"SHOULD: replies are a bare JSON dict of 1-5 ratings; valid rate near 1.0 -> coherent. " - f"ELSE the model is refusing / adding prose / max_tokens too small (empty content).\n") + f"ELSE the record shows malformed output, rescue, or request failure.\n") + _append_record(rpath, {"event": "run_finished", "run_id": run_id, "protocol_id": protocol_id, + "model": model, "planned_requests": len(plan), + "valid_samples": sum(row["valid_samples"] for row in out), + "failed_samples": sum(row["failed_samples"] for row in out), + "rescued_samples": sum(row["rescued_samples"] for row in out)}) return out